[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-studies-list:{\"conditionNormalized\":\"machine-learning\",\"overallStatus\":[\"RECRUITING\",\"AVAILABLE\",\"NOT_YET_RECRUITING\"],\"orderBy\":\"LastUpdateSubmitDate:desc\",\"size\":25,\"offset\":0}":3,"health-study-condition:machine-learning":27},{"pageToken":4,"total":5,"offset":6,"count":7,"results":8},null,26,0,25,[9,42,72,113,140,163,191,214,239,264,291,313,342,371,391,410,440,463,494,517,547,572,594,619,642],{"id":10,"slug":11,"hasResults":12,"nctId":13,"briefTitle":14,"officialTitle":15,"acronym":4,"eligibilityCriteria":16,"healthyVolunteers":12,"sex":17,"minAge":18,"maxAge":4,"enrollmentInfo":19,"targetDuration":4,"studyType":22,"phases":4,"briefSummary":23,"conditions":24,"keywords":4,"overallStatus":29,"whyStopped":4,"lastUpdateSubmitDate":30,"lastUpdatePostDateStruct":31,"startDateStruct":34,"completionDateStruct":36,"leadSponsor":38,"locationsCount":41},"100641551","prospective-evaluation-of-an-ai-diagnostic-ultrasound-tool-for-fetal-weight-estimation-100641551",false,"NCT07661433","Prospective Evaluation of an AI Diagnostic Ultrasound Tool for Fetal Weight Estimation","Z 32503 - Prospective Evaluation of an AI Diagnostic Ultrasound Tool for Fetal Weight Estimation","Inclusion Criteria:\n\n* 18 years of age or older\n* Viable intrauterine pregnancy\n* Delivery expected within one week of study procedures between 24 0\u002F7 and 42 6\u002F7 weeks, including participants with a scheduled induction or cesarean delivery on a known date, or those admitted in spontaneous labor\n* Ability and willingness to provide written informed consent\n* Willingness to comply with all study procedures\n\nExclusion Criteria:\n\n* Maternal body mass index ≥ 40 kg\u002Fm\\^2\n* Multiple gestation (i.e., twins or higher order)\n* Known major fetal malformation or anomaly\n* Any maternal condition (medical, psychological, or social) that, in the opinion of the study team, may interfere with study participation or data integrity.","FEMALE","18 Years",{"count":20,"type":21},1000,"ESTIMATED","OBSERVATIONAL","Purpose: The primary objective of this study is to assess the diagnostic accuracy of an AI-enabled ultrasound tool for estimating fetal weight Participants: 1,000 pregnant individuals Procedures (methods): This prospective diagnostic accuracy study will enroll 1,000 pregnant individuals within one week of anticipated delivery. At a single visit, each participant will undergo two ultrasound assessments: (1) standardized sweeps for AI analysis (performed by both specialist and nonspecialist users), (2) specialist-performed fetal biometry.",[25,26,27,28],"Fetal Weight","Pregnancy","Machine Learning","Pregnancy - Prenatal Testing","NOT_YET_RECRUITING","2026-06-22",{"date":32,"type":33},"2026-06-25","ACTUAL",{"date":35,"type":21},"2026-06",{"date":37,"type":21},"2026-12",{"name":39,"class":40},"University of North Carolina, Chapel Hill","OTHER",5,{"id":43,"slug":44,"hasResults":12,"nctId":45,"briefTitle":46,"officialTitle":46,"acronym":4,"eligibilityCriteria":47,"healthyVolunteers":12,"sex":48,"minAge":49,"maxAge":50,"enrollmentInfo":51,"targetDuration":4,"studyType":53,"phases":54,"briefSummary":56,"conditions":57,"keywords":4,"overallStatus":61,"whyStopped":4,"lastUpdateSubmitDate":62,"lastUpdatePostDateStruct":63,"startDateStruct":65,"completionDateStruct":67,"leadSponsor":69,"locationsCount":71},"100441986","a-machine-learning-approach-for-predicting-tdcs-treatment-outcomes-of-adolescents-with-autism-spectrum-disorders-100441986","NCT05035511","A Machine Learning Approach for Predicting tDCS Treatment Outcomes of Adolescents With Autism Spectrum Disorders","Inclusion Criteria:\n\n* Individuals who are confirmed by a clinical psychologist based on the Diagnostic and Statistical Manual of Mental Disorders-5th Ed (DSM-V) criteria of Autism spectrum disorder and structured interview with their parents or primary caregivers on their developmental history using the Autism Diagnostic Interview-Revised (ADI-R).\n* Individuals with intelligence quotient above 60.\n* Individuals who demonstrate the ability to comprehend testing and stimulation instructions.\n\nExclusion Criteria:\n\n* Individuals with severe motor dysfunctions that would hinder their participation, and those with history of other neurological and psychiatric disorders and head trauma, or on psychiatric medication will be excluded from the study","ALL","12 Years","22 Years",{"count":52,"type":21},90,"INTERVENTIONAL",[55],"NA","Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by disturbances in communication, poor social skills, and aberrant behaviors. Particularly detrimental are the presence of restricted and repetitive stereotyped behaviors and uncontrollable temper outbursts over trivial changes in the environment, which often cause emotional stress for the children, their families, schools and neighborhood communities.\n\nFundamental to these cognitive and behavioral problems is the disordered cortical connectivity and resultant executive dysfunction that underpin the use of effective strategies to integrate information across contexts. Brain connectivity problems affect the rate at which information travels across the brain. Slow processing speed relates to a reduced capacity of executive function to recall and formulate thoughts and actions automatically, with the result that autistic children with poor processing speed have great difficulty learning or perceiving relationships across multiple experiences. In consequence, these children compensate for the impaired ability to integrate information from the environment by memorizing visual details or individual rules from each situation. This explains why children with autism tend to follow routines in precise detail and show great distress over seemingly trivial changes in the environment.\n\nTo date, there is no known cure for ASD, and the disorder remains a highly disabling condition. Recently, a non-invasive brain stimulation technique, transcranial direct current Stimulation (tDCS) has shown great promise as a potentially effective and costeffective tool for reducing core symptoms such as anxiety, aggression, impulsivity, and inattention in patients with autism. This technique has been shown to modify behavior by inducing changes in cortical excitability and enhancing connectivity between the targeted brain areas. However, not all ASD patients respond to this intervention the same way and predicting the behavioral impact of tDCS in patients with ASD remains a clinical challenge. This proposed study thus aims to address these challenges by determining whether resting-state EEG and clinical data at baseline can be used to differentiate responders from non-responders to tDCS treatment. Findings from the study will provide new guidance for designing intervention programs for individuals with ASD.",[58,59,60,27],"Transcranial Direct Current Stimulation","Autistic Disorders Spectrum","Electroencephalography","RECRUITING","2026-06-21",{"date":64,"type":33},"2026-06-24",{"date":66,"type":33},"2022-01-05",{"date":68,"type":21},"2026-12-31",{"name":70,"class":40},"The Hong Kong Polytechnic University",1,{"id":73,"slug":74,"hasResults":12,"nctId":75,"briefTitle":76,"officialTitle":77,"acronym":78,"eligibilityCriteria":79,"healthyVolunteers":80,"sex":48,"minAge":18,"maxAge":4,"enrollmentInfo":81,"targetDuration":4,"studyType":53,"phases":83,"briefSummary":84,"conditions":85,"keywords":94,"overallStatus":29,"whyStopped":4,"lastUpdateSubmitDate":105,"lastUpdatePostDateStruct":106,"startDateStruct":107,"completionDateStruct":109,"leadSponsor":111,"locationsCount":71},"100644109","morphology-in-oral-rare-syndromes--artificial-intelligence-for-clinical-diagnosis-100644109","NCT07666269","Morphology in Oral Rare Syndromes & Artificial Intelligence for Clinical Diagnosis","Geometric Morphometric Characterization of Oro-Dental Anomalies in Rare Bone and Cartilage Diseases From 3D Digital Data (MOSAIC)","MOSAIC","Inclusion Criteria:\n\n* For cases: Diagnosis of a rare bone and cartilage disorder confirmed by the Rare Disease Competence Center for Constitutional Bone Disorders (MOC) or Calcium and Phosphate Metabolism Disorders (CaP), genetically and\u002For clinically.\n* Ability to undergo a 3D intra-oral scan;\n* Ability of the participant to understand the information notice provided regarding the use of their medical data and 3D digital models for research purposes, and to express informed non-objection to participation in the research.\n* For controls: healthy adults recruited in the Dental Medicine Department.\n\nExclusion Criteria:\n\n* History of major orthodontic\u002Forthognathic treatment;\n* Craniofacial conditions unrelated to the studied diseases (e.g., cleft palate, non-target craniofacial syndromes);\n* Impossibility to obtain a 3D optical impression;\n* Refusal or inability of the participant to understand the information notice and\u002For to express informed non-objection to participation in the research.",true,{"count":82,"type":21},240,[55],"MOSAIC aims to determine whether oro-dental morphological anomalies, particularly palatal morphology, associated with rare bone and cartilage diseases can be precisely characterized using 3D digital models analysed through geometric morphometrics. The study will also evaluate whether these morphological signatures can train an artificial intelligence (AI) algorithm to classify syndromes. A prospective monocentric case-control cohort will be constituted, including 3D intra-oral scans and associated clinical data. The final goal is to improve diagnostic accuracy and reduce diagnostic delay in rare bone disorders.",[86,87,88,89,90,91,92,93,27],"Osteogenesis Imperfecta","Rare Bone Disorders","Hypophosphatemia","X-Linked","Mucopolysaccharidoses","Tooth Abnormalities","Palate; Deformity","Artificial Intelligence (AI)",[95,96,97,98,99,100,101,102,103,104],"Rare bone diseases","palatal morphology","geometric morphometrics","3D intra-oral scan","machine learning","artificial intelligence","diagnostic classification","osteogenesis imperfecta","X-linked hypophosphatemia","mucopolysaccharidosis","2026-06-18",{"date":64,"type":33},{"date":108,"type":21},"2026-09-01",{"date":110,"type":21},"2028-03-01",{"name":112,"class":40},"University Hospital, Bordeaux",{"id":114,"slug":115,"hasResults":12,"nctId":116,"briefTitle":117,"officialTitle":118,"acronym":4,"eligibilityCriteria":119,"healthyVolunteers":12,"sex":48,"minAge":18,"maxAge":120,"enrollmentInfo":121,"targetDuration":4,"studyType":22,"phases":4,"briefSummary":123,"conditions":124,"keywords":129,"overallStatus":61,"whyStopped":4,"lastUpdateSubmitDate":131,"lastUpdatePostDateStruct":132,"startDateStruct":134,"completionDateStruct":136,"leadSponsor":138,"locationsCount":71},"100638551","multi-omics-inflammatory-phenotype-for-abpa-recurrence-risk-prediction-100638551","NCT07611838","Multi-Omics Inflammatory Phenotype for ABPA Recurrence Risk Prediction","Multi-Omics Data-Derived Inflammatory Phenotype for ABPA Recurrence Risk Prediction: A Multicenter Study","Inclusion Criteria:\n\n* Female and Male patients aged 18-80 years\n* diagnosis of Allergic Bronchopulmonary Aspergillosis ABPA accroding to the 2024 ISHAM Working Group Diagnostic Criteria\n\nExclusion Criteria:\n\n* Patients with malignant tumors or severe organ dysfunction (e.g., cardiac, cerebral, renal, etc.)\n* Patients with severe comorbidities, including active pulmonary tuberculosis, lung cancer, chronic heart failure (NYHA class Ⅳ), chronic kidney disease (CKD stage 5), decompensated cirrhosis, etc.\n* Patients with immunosuppressive status, such as HIV infection, long-term use of oral corticosteroids or immunosuppressive agents.\n* Pregnant or lactating women.\n* Patients with missing key data or incomplete medical records.","80 Years",{"count":122,"type":21},300,"To develop and externally validate a machine learning model for predicting the 1-year risk of relapse in patients with stable ABPA, and to further evaluate its value in risk stratification and clinical decision-making.",[125,27,126,127,128],"Allergic Bronchopulmonary Aspergillosis (ABPA)","Multi-omics","Multicenter Study","Relapse",[130,128,27,127,126],"Allergic Bronchopulmonary Aspergillosis","2026-05-28",{"date":133,"type":33},"2026-06-01",{"date":135,"type":33},"2021-01-01",{"date":137,"type":21},"2028-12-31",{"name":139,"class":40},"Qianfoshan Hospital",{"id":141,"slug":142,"hasResults":12,"nctId":143,"briefTitle":144,"officialTitle":145,"acronym":146,"eligibilityCriteria":147,"healthyVolunteers":12,"sex":48,"minAge":18,"maxAge":4,"enrollmentInfo":148,"targetDuration":4,"studyType":22,"phases":4,"briefSummary":150,"conditions":151,"keywords":4,"overallStatus":61,"whyStopped":4,"lastUpdateSubmitDate":154,"lastUpdatePostDateStruct":155,"startDateStruct":156,"completionDateStruct":158,"leadSponsor":160,"locationsCount":162},"100618587","development-and-pre-validation-of-a-machine-learning-based-prediction-algorithm-for-early-functional-recovery-in-patients-undergoing-hip-and-knee-replacement-surgery-100618587","NCT07333560","Development and Pre-validation of a Machine Learning-based Prediction Algorithm for Early Functional Recovery in Patients Undergoing Hip and Knee Replacement Surgery","Development and Pre-validated Multiple Variable Prediction Model Using Machine Learning for Early Functional Recovery After Joint Replacement Surgery.","FISIO_IA","Inclusion Criteria:\n\n* Adults aged 18 years or older\n* Patients underwent elective hip or knee arthroplasty.\n* Patients for whom postoperative physiotherapy was initiated.\n\nExclusion Criteria:\n\n* Patients who underwent surgery for oncologic disease, femoral fracture, or revision joint arthroplasty.\n* Patients for whom postoperative physiotherapy was not provided due to postoperative complications\n* clinical data are unavailable.",{"count":149,"type":21},943,"The goal of this observational study is to develop and pre-validate a machine learning algorithm to predict early recovery of mobility in patients undergoing hip or knee joint replacement surgery. The primary research question is:\n\nCan a machine learning model accurately classify patients with faster versus slower recovery of autonomous mobility in the first days after joint replacement surgery?\n\nPatients who have undergone elective hip or knee arthroplasty and received post-operative physiotherapy will have their clinical and perioperative data collected retrospectively (2020-2023) and prospectively (March 2026-December 2027). The algorithm will be trained on retrospective data and tested prospectively to evaluate its predictive performance for early mobilization and length of hospital stay.",[93,27,152,153],"Joint Replacement","Predictive Model","2026-05-27",{"date":133,"type":33},{"date":157,"type":33},"2026-03-09",{"date":159,"type":21},"2027-12",{"name":161,"class":40},"Istituto Ortopedico Rizzoli",2,{"id":164,"slug":165,"hasResults":12,"nctId":166,"briefTitle":167,"officialTitle":168,"acronym":4,"eligibilityCriteria":169,"healthyVolunteers":12,"sex":48,"minAge":4,"maxAge":18,"enrollmentInfo":170,"targetDuration":4,"studyType":53,"phases":171,"briefSummary":172,"conditions":173,"keywords":177,"overallStatus":61,"whyStopped":4,"lastUpdateSubmitDate":182,"lastUpdatePostDateStruct":183,"startDateStruct":185,"completionDateStruct":187,"leadSponsor":189,"locationsCount":71},"100584222","pact-involvement-in-cardiology-patients-100584222","NCT06886529","PACT Involvement in Cardiology Patients","Early PACT Involvement in Cardiology Patients Using Machine Learning","Inclusion Criteria:\n\n* Pediatric inpatients admitted to cardiology\n\nExclusion Criteria:\n\n* Expected to be discharged prior to midnight on the day of admission",{"count":20,"type":21},[55],"The goal of this trial is to determine the effectiveness of a machine-learning (ML) model predicting a serious cardiac event within the next three months, when compared pre- versus post-deployment, in pediatric cardiac inpatients. The main questions it aims to answer are whether deployment of the ML model:\n\n1. Increases PACT consultation within the next three months among admissions without PACT involvement in the previous 100 days\n2. Increases PACT consultation or visit within the next three months among those who experience a serious cardiac event during this period\n3. Decreases time to PACT consultation or visit among those seen by PACT during this period\n4. Decreases the incidence of death in the intensive care unit (ICU)\n5. Increases documentation of goals of care\n\nHigh-risk cardiology patients will be identified by an ML model each morning. If the patient has been seen by the PACT team within the past year, the update will go to the PACT team members. If the patient hasn't been seen by the PACT team, the email will be sent to the cardiology physician in charge of the patient. This physician will decide whether a PACT consultation is necessary based on their clinical judgment. If so, a referral will be made using the usual process. Outcomes of the identified patients will be compared pre- and post-deployment.",[27,174,175,176],"Cardiovascular Outcome","Pediatric Palliative Care","Pediatric Cardiology",[178,179,99,180,181],"quality of life","cardiovascular outcomes","prediction models","pediatric","2026-04-22",{"date":184,"type":33},"2026-04-23",{"date":186,"type":33},"2025-10-16",{"date":188,"type":21},"2027-10-16",{"name":190,"class":40},"The Hospital for Sick Children",{"id":192,"slug":193,"hasResults":12,"nctId":194,"briefTitle":195,"officialTitle":195,"acronym":196,"eligibilityCriteria":197,"healthyVolunteers":80,"sex":48,"minAge":198,"maxAge":199,"enrollmentInfo":200,"targetDuration":4,"studyType":22,"phases":4,"briefSummary":201,"conditions":202,"keywords":4,"overallStatus":61,"whyStopped":4,"lastUpdateSubmitDate":205,"lastUpdatePostDateStruct":206,"startDateStruct":208,"completionDateStruct":210,"leadSponsor":212,"locationsCount":41},"100634655","digital-diagnosis-of-cardiac-sound-in-pediatric-patients-di-sound-study-100634655","NCT07542509","Digital diagnoSis of Cardiac sOUNd in peDiatric Patients [DI-SOUND Study]","DI-SOUND","Inclusion criteria:\n\n* Age \\\u003C 30 days\n* Signed informed consent obtained from parent(s) or representative(s)\n\nExclusion criteria:\n\n* Inability to acquire a diagnostic echocardiogram\n* Weight less than 1.5Kg","7 Days","30 Days",{"count":20,"type":21},"Neonatal screening procedures for potentially life-threatening congenital cardiovascular diseases (i.e., duct-dependent systemic or pulmonary circulation), currently implemented at the national level, rely primarily on cardiovascular physical examination performed by a neonatologist. More recently, this approach has been complemented by the assessment of hemoglobin oxygen saturation at both the upper and lower extremities (pre- and post-ductal saturation) in order to improve diagnostic sensitivity, although this practice has not yet been uniformly adopted nationwide. Converging evidence indicates that these screening strategies are affected by significant limitations in both sensitivity (failure to identify affected individuals) and specificity (false-positive findings in healthy subjects). These limitations are associated with substantial overall costs for the healthcare system. Failure to correctly identify affected neonates may result in increased morbidity and mortality, whereas overdiagnosis leads to unnecessary second-level diagnostic investigations and imposes a considerable psychological burden on families, who remain understandably anxious until diagnostic confirmation is achieved.\n\nThe aim of the present research project (proof-of-concept study) is to develop a digital classifier capable to categorize heart sounds with commercially available digital stethoscopes into a binary classification system distinguishing physiological from pathological sounds. The derivation phase will be followed by a prospective validation phase, in which the classifier will be applied to assess its diagnostic performance. This phase will also evaluate the economic impact of the digital screening approach compared with standard practice.\n\nDuring the derivation phase, neonates with known cardiovascular status, as determined by prior echocardiographic assessment (including both healthy subjects and those with congenital heart disease), will be enrolled. Heart sounds will be recorded in a quiet environment under standard clinical conditions, without sedation. Digital recordings will be stored in WAV format and analyzed to develop a binary classification algorithm capable of distinguishing healthy from pathological cases. Following development, the classifier will be prospectively applied to a validation cohort of neonates undergoing conventional cardiovascular screening (clinical examination and pre- and post-ductal pulse oximetry), followed by classification using the digital tool under investigation. All participants will subsequently undergo confirmatory echocardiography. Diagnostic performance metrics, including sensitivity, specificity, positive and negative predictive values, and likelihood ratios, will be calculated for both the digital and conventional screening modalities. Furthermore, the number of missed pathological cases and the number of unnecessary second-level investigations resulting from false-positive findings will be used to define the economic benefit profile of the proposed screening strategy. Monte Carlo simulation techniques will be employed to extrapolate these findings at the national level, using ISTAT data on birth rates and disease prevalence.\n\nIt is anticipated that the development of a digital classifier for the binary classification of neonatal heart sounds will be feasible. Moreover, it is expected that this tool will demonstrate superior diagnostic performance compared with current neonatal screening strategies, with beneficial implications not only for the accurate identification of affected and healthy neonates but also for reducing overall healthcare costs associated with missed diagnoses and inappropriate overdiagnosis.",[203,204,27],"Cardiac Disease","Auscultation of Heart","2026-04-15",{"date":207,"type":33},"2026-04-21",{"date":209,"type":33},"2024-07-18",{"date":211,"type":21},"2027-01-01",{"name":213,"class":40},"IRCCS Azienda Ospedaliero-Universitaria di Bologna",{"id":215,"slug":216,"hasResults":12,"nctId":217,"briefTitle":218,"officialTitle":219,"acronym":4,"eligibilityCriteria":220,"healthyVolunteers":80,"sex":48,"minAge":4,"maxAge":4,"enrollmentInfo":221,"targetDuration":223,"studyType":22,"phases":4,"briefSummary":224,"conditions":225,"keywords":4,"overallStatus":29,"whyStopped":4,"lastUpdateSubmitDate":232,"lastUpdatePostDateStruct":233,"startDateStruct":235,"completionDateStruct":236,"leadSponsor":237,"locationsCount":71},"100634172","deep-learning-framework-for-continuous-depth-of-anesthesia-forecasting-100634172","NCT07536230","Deep Learning Framework for Continuous Depth of Anesthesia Forecasting","Validation of a Deep Learning Framework for Continuous Forecasting of Pharmacodynamic Responses and Physiological Trajectories During General Anesthesia","Inclusion Criteria:\n\n* Patients scheduled for elective surgery requiring general anesthesia.\n* Procedures requiring continuous depth of anesthesia monitoring (BIS).\n\nExclusion Criteria:\n\n\\- Procedures where the primary anesthetic plan does not involve continuous electronic data capture.",{"count":222,"type":21},115,"1 Day","The integration of Artificial Intelligence (AI) in anesthesiology offers the potential to shift patient monitoring from reactive to predictive. Deep learning architectures, specifically Long Short-Term Memory (LSTM) networks, excel at processing complex, time-series data to forecast future clinical states.\n\nWhile standard PK\u002FPD models (such as the state of the art Eleveld model for Propofol and Remifentanil) estimate target-site drug concentrations (Ce), they do not account for real-time, patient-specific dynamic responses. This study aims to deploy an AI framework designed to predict future physiological states.",[226,227,228,229,27,230,231,153],"BIS","BIS-EEG","Artifical Intelligence","Intraoperative","Anesthesia","Anesthesia Awareness","2026-04-10",{"date":234,"type":33},"2026-04-17",{"date":133,"type":21},{"date":108,"type":21},{"name":238,"class":40},"Universitair Ziekenhuis Brussel",{"id":240,"slug":241,"hasResults":12,"nctId":242,"briefTitle":243,"officialTitle":244,"acronym":4,"eligibilityCriteria":245,"healthyVolunteers":12,"sex":48,"minAge":18,"maxAge":4,"enrollmentInfo":246,"targetDuration":248,"studyType":22,"phases":4,"briefSummary":249,"conditions":250,"keywords":4,"overallStatus":61,"whyStopped":4,"lastUpdateSubmitDate":255,"lastUpdatePostDateStruct":256,"startDateStruct":258,"completionDateStruct":260,"leadSponsor":262,"locationsCount":71},"100633009","predictive-value-of-gastrointestinal-blood-flow-for-enteral-nutrition-intolerance-in-critically-ill-patients-100633009","NCT07521111","Predictive Value of Gastrointestinal Blood Flow for Enteral Nutrition Intolerance in Critically Ill Patients","Study on the Predictive Value of Gastrointestinal Blood Flow for Enteral Nutrition Intolerance in Critically Ill Patients","Inclusion Criteria:\n\n* Age \\> 18 years old.\n* Expected duration of enteral nutrition support \\> 7 days.\n* Patients or their legal representatives sign the informed consent form.\n\nExclusion Criteria:\n\n* History of major gastrointestinal surgery such as subtotal gastrectomy and gastrointestinal anastomosis.\n* Contraindications to abdominal point-of-care ultrasound (POCUS) examination (e.g., recent large-area abdominal burns, dressings blocking movement, open abdomen).\n* Presence of severe gastrointestinal diseases such as gastroparesis, intestinal obstruction, digestive tract perforation, and gastrointestinal bleeding upon admission.\n* Presence of severe peripheral vascular disease or valvular heart disease.\n* Pregnant or lactating women.",{"count":247,"type":21},500,"28 Days","This study aims to explore the correlation between gastrointestinal blood flow and the incidence of enteral nutrition intolerance (ENI) and its symptoms in critically ill patients, construct and compare predictive models including blood flow parameters, and evaluate their incremental predictive value.",[251,252,253,254,27],"Critical Illness","Enteral Nutrition Intolerance","Enteral Nutrition Feeding","Prediction Models","2026-04-02",{"date":257,"type":33},"2026-04-09",{"date":259,"type":33},"2026-01-25",{"date":261,"type":21},"2027-06-30",{"name":263,"class":40},"Ruijin Hospital",{"id":265,"slug":266,"hasResults":12,"nctId":267,"briefTitle":268,"officialTitle":269,"acronym":4,"eligibilityCriteria":270,"healthyVolunteers":12,"sex":48,"minAge":271,"maxAge":18,"enrollmentInfo":272,"targetDuration":4,"studyType":53,"phases":274,"briefSummary":275,"conditions":276,"keywords":279,"overallStatus":61,"whyStopped":4,"lastUpdateSubmitDate":283,"lastUpdatePostDateStruct":284,"startDateStruct":286,"completionDateStruct":288,"leadSponsor":290,"locationsCount":71},"100585463","timely-ordering-of-pharmacogenetic-testing-100585463","NCT06902688","Timely Ordering of Pharmacogenetic Testing","Timely Ordering of Pharmacogenetic Testing in Pediatric Oncology","Inclusion Criteria:\n\n* Inpatient at The Hospital for Sick Children\n* Between 6 months to 18 years old\n\nExclusion Criteria:\n\n* Prior pharmacogenetic testing and\u002For prior receipt of a targeted medication\n* Current Intensive Care Unit (ICU) admission\n* Expected hospital discharge is prior to midnight on the day of admission","6 Months",{"count":273,"type":21},275,[55],"The goal of this trial is to learn if a machine learning (ML) model can help optimize drug therapy in the pediatric population. The main question\\[s\\] it aims to answer are whether a machine learning model predicting receipt of a targeted medication within the next three months:\n\n* Increases the offering of pharmacogenetic testing prior to receipt of a targeted medication\n* Increases the number of patients with pharmacogenetic results prior to receipt of a targeted medication\n* Increases the number of patients who have alteration in medication choice or dose based on pharmacogenetic results\n\nThis trial only focuses on the prediction and provision of participants with a high-risk of receiving a medication with a pharmacogenetic indication in the next three months.",[27,254,277,278],"Pediatrics","Precision Medicine",[280,99,281,180,282],"precision medicine","pharmacogenetics","pediatrics","2026-03-03",{"date":285,"type":33},"2026-03-05",{"date":287,"type":33},"2025-06-10",{"date":289,"type":21},"2027-06-10",{"name":190,"class":40},{"id":292,"slug":293,"hasResults":12,"nctId":294,"briefTitle":295,"officialTitle":296,"acronym":4,"eligibilityCriteria":297,"healthyVolunteers":12,"sex":48,"minAge":18,"maxAge":4,"enrollmentInfo":298,"targetDuration":4,"studyType":22,"phases":4,"briefSummary":300,"conditions":301,"keywords":4,"overallStatus":29,"whyStopped":4,"lastUpdateSubmitDate":306,"lastUpdatePostDateStruct":307,"startDateStruct":308,"completionDateStruct":309,"leadSponsor":311,"locationsCount":4},"100627163","machine-learning-prediction-of-mortality-after-prone-positioning-in-ards-100627163","NCT07445061","Machine Learning Prediction of Mortality After Prone Positioning in ARDS","A Machine Learning Model to Predict Mortality in Patients With Acute Respiratory Distress Syndrome After Prone Positioning","Inclusion Criteria:\n\n* Diagnosis of ARDS according to the Berlin definition \\[15\\];\n* Receipt of at least one session of prone position ventilation (PPV) during hospitalization;\n* Requirement for mechanical ventilation.\n\nExclusion Criteria:\n\n* Age \\\u003C18 years;\n* PPV duration \\\u003C6 hours;\n* ICU length of stay \\\u003C24 hours;\n* Pregnancy;\n* Missing key clinical data.",{"count":299,"type":21},377,"Acute respiratory distress syndrome (ARDS) is a life-threatening condition with high mortality. Prone position ventilation (PPV) is an evidence-based therapy that improves oxygenation and survival in patients with moderate to severe ARDS; however, outcomes remain heterogeneous. Early identification of patients at high risk of mortality after PPV may improve clinical decision-making and individualized management.\n\nThis retrospective observational study aims to develop and validate a machine learning model to predict intensive care unit (ICU) mortality in ARDS patients receiving prone position ventilation. Clinical, laboratory, and treatment variables collected from ICU electronic medical records will be used to construct prediction models using multiple machine learning algorithms. The performance of these models will be evaluated and compared to identify the optimal model for mortality prediction.",[302,303,27,304,305],"Acute Respiratory Distress Syndrome (ARDS)","Prone Position Ventilation","ICU","ARDS","2026-03-01",{"date":283,"type":33},{"date":306,"type":21},{"date":310,"type":21},"2026-05-01",{"name":312,"class":40},"Shanghai Zhongshan Hospital",{"id":314,"slug":315,"hasResults":12,"nctId":316,"briefTitle":317,"officialTitle":318,"acronym":4,"eligibilityCriteria":319,"healthyVolunteers":12,"sex":48,"minAge":320,"maxAge":321,"enrollmentInfo":322,"targetDuration":4,"studyType":22,"phases":4,"briefSummary":324,"conditions":325,"keywords":330,"overallStatus":61,"whyStopped":4,"lastUpdateSubmitDate":333,"lastUpdatePostDateStruct":334,"startDateStruct":336,"completionDateStruct":338,"leadSponsor":340,"locationsCount":71},"100602907","remote-monitoring-of-asthma-in-children-and-young-people-100602907","NCT07129616","Remote Monitoring of Asthma in Children and Young People","Remote Monitoring of Asthma in Children and Young People - Reducing Risk of Asthma Attack Using a Connected Patient Approach","Inclusion Criteria:\n\n* Children and Young People with a diagnosis of asthma (coded as asthma or suspected asthma) or a prescription of inhaled corticosteroid in the prior 2 years.\n\nExclusion Criteria:\n\n* Alternative non-asthma diagnosis that would require inhaled steroid\n* cystic fibrosis\n* bronchiectasis\n* primary ciliary dyskinaesia","5 Years","17 Years",{"count":323,"type":21},900,"The objective of this study is to determine whether healthcare data and remotely collected patient data can accurately predict asthma attacks in children and young people aged 5-17 years. The main outcome is:\n\nwhen using this new system, is there a reduction in asthma attacks compared with a historic average.\n\nThe whole population of children and young people with asthma will have routine healthcare data monitored, with a subset of people with high risk asthma asked to participate in a more detail study involving remotely monitored data.",[326,327,328,329,27],"Asthma Childhood","Asthma Attack","Remote Monitoring","Risk Assessment",[331,332],"asthma attack risk reduction","remote monitoring","2026-01-20",{"date":335,"type":33},"2026-01-22",{"date":337,"type":33},"2025-11-20",{"date":339,"type":21},"2027-03-01",{"name":341,"class":40},"University of Edinburgh",{"id":343,"slug":344,"hasResults":12,"nctId":345,"briefTitle":346,"officialTitle":347,"acronym":348,"eligibilityCriteria":349,"healthyVolunteers":12,"sex":48,"minAge":350,"maxAge":4,"enrollmentInfo":351,"targetDuration":4,"studyType":53,"phases":353,"briefSummary":354,"conditions":355,"keywords":360,"overallStatus":29,"whyStopped":4,"lastUpdateSubmitDate":363,"lastUpdatePostDateStruct":364,"startDateStruct":365,"completionDateStruct":367,"leadSponsor":369,"locationsCount":71},"100620111","multimodal-exercise-therapy-for-non-surgical-intervention-of-nonspecific-low-back-pain-100620111","NCT07353372","Multimodal Exercise Therapy for Non-Surgical Intervention of Nonspecific Low Back Pain.","Interventional Study on Paraspinal Muscle Degeneration Leading to Lumbar Degenerative Diseases","METNLBP","Inclusion Criteria:\n\n1. Age ≥ 60 years\n2. Chronic low-back pain for \\> 3 months (no surgical indication)\n3. Planned to receive conservative treatment\n4. Willing to participate and able to provide written informed consent\n\nExclusion Criteria:\n\n1. Specific low-back pain due to infection, tumour, fracture, ankylosing spondylitis, scoliosis, or other structural spinal disorders\n2. Previous lumbar surgery or current surgical indication for lumbar disease 3.Severe cardiopulmonary, hepatic or renal insufficiency that precludes exercise or drug therapy\n\n4.Severe cognitive impairment or psychiatric disorder preventing cooperation 5.Marked exercise limitations or physical disability precluding rehabilitation training 6.Participation in another clinical trial that could interfere with outcomes 7.Known hypersensitivity to any study medication","60 Years",{"count":352,"type":21},314,[55],"This multicenter, assessor-blinded, two-arm parallel randomized controlled trial (N = 314) will compare the efficacy and safety of a 6-week multidimensional exercise program plus usual pharmacological care (experimental arm) versus usual pharmacological care alone (control arm) in adults ≥ 60 years with chronic non-specific low-back pain (LBP) and imaging evidence of paraspinal muscle degeneration. The primary endpoint is change in Oswestry Disability Index (ODI) at 12 months. Secondary endpoints include pain VAS, JOA score, recurrence rate, and patient satisfaction measured repeatedly to 12 months. Advanced MRI radiomics and machine-learning algorithms will be used to build a \"paraspinal muscle imaging-function-prognosis\" prediction model and an open-access web tool for risk stratification. The study will generate a standardized, evidence-based non-operative care pathway for chronic LBP driven by paraspinal muscle degeneration",[356,357,27,358,359],"Paraspinal Muscles","Low Back Pain","Radiomics","Non-surgical Treatment",[361,362],"Nonsteroidal anti-inflammatory drugs","Multidimensional exercise intervention","2026-01-19",{"date":333,"type":33},{"date":366,"type":21},"2026-01-01",{"date":368,"type":21},"2028-09-01",{"name":370,"class":40},"Xuanwu Hospital, Beijing",{"id":372,"slug":373,"hasResults":12,"nctId":374,"briefTitle":375,"officialTitle":376,"acronym":4,"eligibilityCriteria":377,"healthyVolunteers":12,"sex":48,"minAge":18,"maxAge":4,"enrollmentInfo":378,"targetDuration":198,"studyType":22,"phases":4,"briefSummary":380,"conditions":381,"keywords":4,"overallStatus":29,"whyStopped":4,"lastUpdateSubmitDate":383,"lastUpdatePostDateStruct":384,"startDateStruct":386,"completionDateStruct":388,"leadSponsor":390,"locationsCount":4},"100618879","research-on-the-development-and-validation-of-an-early-prediction-model-for-delirium-100618879","NCT07337356","Research on the Development and Validation of an Early Prediction Model for Delirium","Research on the Development and Validation of an Early Prediction Model for Delirium Based on Machine Vision Analysis","Inclusion Criteria:\n\n* Age ≥ 18 years, expected ICU stay ≥ 24 hours, and informed consent to participate in this study;\n\nExclusion Criteria:\n\n* Patients with severe facial trauma\u002Fdeformities that prevent complete expression acquisition, and patients with a history of emotional problems (such as anxiety, depression, etc.).",{"count":379,"type":21},795,"Delirium has a high incidence rate and significantly affects patient prognosis. Diagnosis often relies on manual assessment, which is subject to strong subjectivity, high rates of missed diagnosis, and poor stability. This study employs non-contact identification technology based on machine vision analysis to quantitatively analyze characteristic biological feature data such as micro-expressions. It then investigates the correlation between these features and delirium subtypes. By integrating clinical phenotypic data and using machine learning algorithms, a multi-modal early prediction model for delirium is constructed to meet the clinical need for early warning of delirium subtypes and enhance the efficacy of delirium identification.",[382,254,27],"Delirium","2026-01-04",{"date":385,"type":33},"2026-01-13",{"date":387,"type":21},"2026-02-01",{"date":389,"type":21},"2027-02-01",{"name":263,"class":40},{"id":392,"slug":393,"hasResults":12,"nctId":394,"briefTitle":395,"officialTitle":395,"acronym":4,"eligibilityCriteria":396,"healthyVolunteers":80,"sex":48,"minAge":4,"maxAge":4,"enrollmentInfo":397,"targetDuration":4,"studyType":22,"phases":4,"briefSummary":399,"conditions":400,"keywords":4,"overallStatus":29,"whyStopped":4,"lastUpdateSubmitDate":401,"lastUpdatePostDateStruct":402,"startDateStruct":404,"completionDateStruct":406,"leadSponsor":408,"locationsCount":4},"100614836","muscle-ml-multimodal-integration-of-muscle-strength-structure-by-machine-learning-for-precision-rehabilitation-after-acl-injury-100614836","NCT07284771","MUSCLE-ML: Multimodal Integration of Muscle Strength, Structure by Machine Learning for Precision Rehabilitation After ACL Injury","Inclusion Criteria:\n\n* Unilateral ACL injury and plan for ACLR\n* Commit the post-operation physiotherapy in Prince of Wales Hospital\n\nExclusion Criteria:\n\n* Preoperative radiographic signs of arthritis\n* Patient non-compliance to the rehabilitation program",{"count":398,"type":21},182,"The goal of this clinical trial is to use machine learning (ML) to predict functional recovery by integrating muscle-related factors and other relevant parameters for identification of non-responders to conventional rehabilitation. The main questions it aims to answer are:\n\nDo deficit clusters lead to poorer functional recovery compared to non-deficit clusters? Does an ML-derived composite score that integrates quadriceps\u002Fhamstring strength and size outperform isolated metrics in predicting RTP success?\n\nResearchers will compare deficit clusters against non-deficit clusters to determine if deficit clusters lead to poorer functional recovery.\n\nParticipants will:\n\nReturn for 5 follow-up timepoints in total for PRO and functional assessments including pre-operation, 1-, 3-, 6- and 12-months post-operation.",[27],"2025-12-03",{"date":403,"type":33},"2025-12-16",{"date":405,"type":21},"2026-04-01",{"date":407,"type":21},"2028-08-31",{"name":409,"class":40},"Chinese University of Hong Kong",{"id":411,"slug":412,"hasResults":12,"nctId":413,"briefTitle":414,"officialTitle":415,"acronym":4,"eligibilityCriteria":416,"healthyVolunteers":12,"sex":48,"minAge":18,"maxAge":4,"enrollmentInfo":417,"targetDuration":4,"studyType":22,"phases":4,"briefSummary":419,"conditions":420,"keywords":426,"overallStatus":61,"whyStopped":4,"lastUpdateSubmitDate":431,"lastUpdatePostDateStruct":432,"startDateStruct":434,"completionDateStruct":436,"leadSponsor":437,"locationsCount":71},"100612666","machine-learning-for-predicting-spinal-anesthesia-duration-100612666","NCT07256548","Machine Learning for Predicting Spinal Anesthesia Duration","Comparative Evaluation of Machine Learning Algorithms for Predicting Spinal Anesthesia Termination Time","Inclusion Criteria:\n\n1. Patients scheduled to undergo total knee arthroplasty between November 2025 and March 2026 at the Kocaeli City Hospital Operating Theaters.\n2. Patients who have provided written informed consent to participate in the study.\n3. Patients whose surgery is planned under spinal anesthesia.\n4. Patients for whom complete clinical data can be obtained during the study period.\n5. Adults aged 18 years or older, classified as American Society of Anesthesiologist's (ASA) Physical Status I or II.\n\nExclusion Criteria:\n\n1. Patients who were converted to general anesthesia during surgery or initially operated under general anesthesia.\n2. Patients who required postoperative intensive care unit (ICU) admission following anesthesia.\n3. Patients who developed surgical complications and for whom postoperative mobilization could not be planned.\n4. Patients with cognitive impairment preventing them from completing pain assessment scales in the postoperative period.\n5. Patients with neuropathic pain, multiple sclerosis, or other neuromotor disorders will be excluded from the study.",{"count":418,"type":21},140,"Spinal anesthesia provides significant advantages over general anesthesia in knee arthroplasty, including reduced blood loss, faster recovery, and fewer complications. However, predicting its duration is critical for patient safety and effective postoperative management. This study evaluates the usability of machine learning (ML) algorithms to predict the termination time of spinal anesthesia and the patient's readiness for mobilization. Using demographic, surgical, and anesthetic variables, ML models were trained to estimate anesthesia duration. Accurate predictions may improve intraoperative planning, optimize postoperative care, and enhance patient outcomes. Integrating ML-based predictive systems into anesthesia practice can contribute to safer, more efficient, and personalized perioperative management.",[421,27,422,423,424,425],"Spinal Anesthesia","Knee Arthroplasty, Total","Spinal Anesthesia Duration","Postoperative Care","Postoperative Acute Pain",[427,99,428,429,430],"spinal anesthesia","Knee arthroplasty","spinal anesthesia duration","Acute postoperative pain","2025-12-01",{"date":433,"type":33},"2025-12-08",{"date":435,"type":33},"2025-10-31",{"date":306,"type":21},{"name":438,"class":439},"Kocaeli City Hospital","OTHER_GOV",{"id":441,"slug":442,"hasResults":12,"nctId":443,"briefTitle":444,"officialTitle":445,"acronym":4,"eligibilityCriteria":446,"healthyVolunteers":12,"sex":48,"minAge":18,"maxAge":447,"enrollmentInfo":448,"targetDuration":4,"studyType":22,"phases":4,"briefSummary":450,"conditions":451,"keywords":4,"overallStatus":61,"whyStopped":4,"lastUpdateSubmitDate":454,"lastUpdatePostDateStruct":455,"startDateStruct":457,"completionDateStruct":459,"leadSponsor":461,"locationsCount":162},"100589682","a-deep-learning-model-for-blood-volume-estimation-from-multi-modal-ultrasound-100589682","NCT06957587","A Deep Learning Model for Blood Volume Estimation From Multi-modal Ultrasound","Quantitative Estimation of Preoperative Blood Volume Using Multi-modal Ultrasound and Deep Learning","Inclusion Criteria:\n\n* Agree to join this study and sign the informed consent form;\n* Age between 18 and 75 years old (inclusive);\n* BMI (body mass index) is between 18 and 30 kg\u002Fm2;\n* American Society of Anesthesiologists (ASA) grades I-II\n\nExclusion Criteria:\n\n* Preoperative hemoglobin (Hb) \\\u003C10g\u002Fdl\n* Cardiac dysfunction (NYHA class III-IV), respiratory dysfunction (ATS class 2-4), history of liver and kidney dysfunction (such as transaminase \u002F albumin \u002F bilirubin abnormalities, hepatitis history, serum creatinine \u002F urea nitrogen rise, etc.), nervous system abnormalities (those who cannot cooperate due to stroke or its sequelae, Alzheimer, etc.);\n* The ultrasonic display of inferior vena cava, internal jugular vein, subclavian vein or common carotid artery is extremely poor, venous thrombosis or anatomical abnormalities;\n* Multiple injury with chest, abdomen or brain;\n* Pregnant woman","75 Years",{"count":449,"type":21},800,"1. Background \\& Rationale:\n\n   Accurate assessment of a patient's blood volume (BV) status before surgery is critical for preventing perioperative complications. However, there is currently no clinically feasible, accurate, and non-invasive method for direct BV quantification. We hypothesize that dynamic ultrasound videos of major blood vessels contain rich, sub-visual spatiotemporal information about vascular compliance and filling that can be leveraged to estimate BV.\n2. Objective:\n\n   To develop and validate a deep learning model that integrates multi-modal ultrasound video data to achieve non-invasive, quantitative estimation of preoperative blood volume.\n3. Study Design:\n\n   A prospective, single-center, observational study.\n4. Methods:\n\n   Participants: Adult patients scheduled for surgery.\n\n   Data Acquisition:\n\n   Input (Features): Preoperative ultrasound video clips will be recorded in standardized views of four key vessels: the Internal Jugular Vein (IJV), Subclavian Vein (SCV), Inferior Vena Cava (IVC), and Common Carotid Artery (CA).\n\n   Target (Label): The true Blood Volume (BV) will be calculated for each patient using the acute normovolemic hemodilution (ANH) method. The change in hemoglobin concentration before and after this process is used to calculate the total blood volume with high clinical reliability.\n\n   Model Development: A hybrid deep learning architecture (e.g., CNN + LSTM\u002FTransformer) will be trained to extract features from the ultrasound videos and learn the complex, non-linear mapping to the BV value derived from ANH. The model will be trained and internally validated using a k-fold cross-validation approach.\n5. Expected Outcome \\& Significance:\n\nWe anticipate the development of a novel, end-to-end deep learning model capable of providing a quantitative BV estimate from routine ultrasound scans. This technology has the potential to revolutionize perioperative fluid management by offering a rapid, non-invasive, and accurate tool for objective volume status assessment, ultimately guiding personalized therapy and improving patient outcomes.",[452,453,27],"Blood Volume Analysis","Ultrasound","2025-11-13",{"date":456,"type":33},"2025-11-17",{"date":458,"type":33},"2025-10-01",{"date":460,"type":21},"2027-08-31",{"name":462,"class":40},"Shanghai 6th People's Hospital",{"id":464,"slug":465,"hasResults":12,"nctId":466,"briefTitle":467,"officialTitle":467,"acronym":468,"eligibilityCriteria":469,"healthyVolunteers":12,"sex":48,"minAge":470,"maxAge":4,"enrollmentInfo":471,"targetDuration":4,"studyType":22,"phases":4,"briefSummary":473,"conditions":474,"keywords":483,"overallStatus":29,"whyStopped":4,"lastUpdateSubmitDate":485,"lastUpdatePostDateStruct":486,"startDateStruct":488,"completionDateStruct":490,"leadSponsor":492,"locationsCount":4},"100605065","cardiovascular-complications-in-patients-undergoing-allogeneic-hematopoietic-stem-cell-transplantation-100605065","NCT07157670","Cardiovascular Complications in Patients Undergoing Allogeneic Hematopoietic Stem Cell Transplantation.","ALLOCARDIOTOX","Inclusion Criteria:\n\n* Age ≥ 15 years\n* Informed about the study and without objection to participation (or with consent from legal guardians)\n* Undergoing allogeneic HSCT\n\nExclusion Criteria:\n\n* Patient not followed up at the participating center\n* Pregnant or breastfeeding women\n* Patient not affiliated with social security\n* Patient under guardianship, curatorship, or legal protection","15 Years",{"count":472,"type":21},400,"Allogeneic hematopoietic stem cell transplantation (HSCT) represents a major therapeutic strategy for malignant hematologic diseases, with the number of procedures steadily increasing in France each year. Conditioning and maintenance regimens carry a risk of both short- and long-term cardiotoxicity, leading to serious cardiovascular events including acute coronary syndrome (ACS), cardiac dysfunction, arrhythmias, pulmonary hypertension, and pericardial effusion. The pathophysiology of cardiotoxicity in HSCT patients remains poorly understood.\n\nIt is therefore crucial to investigate underlying mechanisms and identify predictive factors of cardiotoxicity in order to provide appropriate cardiological follow-up and management. Current European Society of Cardiology guidelines recommend routine monitoring of HSCT patients with echocardiography and cardiac biomarkers (NT-proBNP, troponin), although these recommendations are based on small-scale studies. The cardiodepressor factor DPP3 has shown promising results in cardio-oncology, with a causal role in anthracycline-induced cardiac dysfunction. Its role in HSCT-related cardiotoxicity requires further evaluation.\n\nThis multicenter study of HSCT recipients will be a valuable resource, enabling a better understanding of the pathophysiology of cardiotoxicity and prognosis. It will highlight imaging (echocardiography, calcium score, supra-aortic Doppler), electrocardiographic, and biological markers (including DPP3) associated with prognosis.",[475,476,477,478,479,480,481,482,27],"Cardiotoxicity","DPP3","HSCT","Allogeneic Hematopoietic Stem Cell Transplantation","ACS - Acute Coronary Syndrome","Cardiac Dysfunction","Arrythmia, Cardiac","Pulmonary Hypertension",[476,477,475,484,27],"Allogeneic hematopoietic stem cell transplantation","2025-09-02",{"date":487,"type":33},"2025-09-05",{"date":489,"type":21},"2025-09-15",{"date":491,"type":21},"2028-09-15",{"name":493,"class":40},"Assistance Publique - Hôpitaux de Paris",{"id":495,"slug":496,"hasResults":12,"nctId":497,"briefTitle":498,"officialTitle":499,"acronym":500,"eligibilityCriteria":501,"healthyVolunteers":12,"sex":48,"minAge":18,"maxAge":4,"enrollmentInfo":502,"targetDuration":4,"studyType":22,"phases":4,"briefSummary":504,"conditions":505,"keywords":4,"overallStatus":61,"whyStopped":4,"lastUpdateSubmitDate":508,"lastUpdatePostDateStruct":509,"startDateStruct":511,"completionDateStruct":513,"leadSponsor":515,"locationsCount":71},"100537397","prognotic-role-of-cmr-in-takotsubo-syndrome-100537397","NCT06277297","Prognotic Role of CMR in Takotsubo Syndrome","Exploring the eVolution in prognOstic capabiLity of mUlti-sequence Cardiac magneTIc resOnance in patieNts Affected by Takotsubo Cardiomyopathy","EVOLUTION","Inclusion Criteria:\n\n* Takotsubo syndrome diagnosis (according to Position Statement of the European Society of Cardiology Heart Failure Association)\n* Adult patients ( \\> 18y old)\n* Availability at baseline of clinical variables, standard transthoracic echocardiography, and cardiovascular magnetic resonance acquisition\n\nExclusion Criteria:\n\n* \\\u003C18 y old\n* Lack of transthoracic echocardiography and cardiovascular magnetic resonance examinations\n* Preexisting cardiomyopathies\n* Previous myocardial infarction\n* Suspected or known prior irreversible myocardial damage\n* Valvular heart disease",{"count":503,"type":21},350,"The primary objective of this observational registry is to develop a comprehensive clinical and imaging score (incorporating echocardiography and cardiac magnetic resonance data) that enhances risk stratification for patients with Takotsubo syndrome.\n\nThe secondary objectives of this registry are as follows:\n\nInvestigate the diagnostic value of cardiac magnetic resonance parameters in predicting in-hospital and long-term outcomes in patients with Takotsubo syndrome.\n\nCompare the proposed risk stratification score for patients with Takotsubo syndrome with previously existing scores.\n\nInvestigate the contribution of machine learning models in predicting in-hospital and long-term outcomes compared to standard clinical scores.\n\nThe design and rationale of this registry are available at 10.1097\u002FRTI.0000000000000709",[506,27,507],"Takotsubo Cardiomyopathy","Magnetic Resonance Imaging","2025-06-04",{"date":510,"type":33},"2025-06-08",{"date":512,"type":33},"2022-11-09",{"date":514,"type":21},"2032-11",{"name":516,"class":40},"University of Cagliari",{"id":518,"slug":519,"hasResults":12,"nctId":520,"briefTitle":521,"officialTitle":522,"acronym":4,"eligibilityCriteria":523,"healthyVolunteers":80,"sex":48,"minAge":524,"maxAge":525,"enrollmentInfo":526,"targetDuration":4,"studyType":53,"phases":528,"briefSummary":529,"conditions":530,"keywords":535,"overallStatus":61,"whyStopped":4,"lastUpdateSubmitDate":538,"lastUpdatePostDateStruct":539,"startDateStruct":541,"completionDateStruct":543,"leadSponsor":544,"locationsCount":162},"100498560","home-sleep-therapy-for-older-adults-with-mci-100498560","NCT05771844","Home Sleep Therapy for Older Adults With MCI","Home Sleep Therapy System for Mild Cognitive Impairment","Inclusion Criteria:\n\n* For participant with Amnestic MCI, the inclusion age range is 55-85 years old.\n* For healthy volunteers without MCI, the inclusion age range is 40-80 years old.\n\nExclusion Criteria:\n\n* History of seizures\n* History of epilepsy\n* History of mod\u002Fsevere brain injury or trauma (including neurosurgery)\n* History or presence of significant neurological disease such as Parkinson\n* History of Electroconvulsive Therapy (ECT)\n* Presence of severe insomnia\n* Presence of untreated sleep apnea\n* Presence of severe anxiety or depression\n* Medications that may affect the EEG\n* History of stroke\n* Sensitivity or allergy to lidocaine or silver\n* Presence of active suicidal ideation\n* Presence of metal in head or implants or medication infusion device\n* Pregnancy\n* Adverse reaction to TMS","40 Years","85 Years",{"count":527,"type":21},60,[55],"The goal of this clinical trial is to learn about the ability of non-invasive brain stimulation during sleep to enhance people's deep sleep and its potential benefit on memory in people with mild cognitive impairment via home use sleep therapy device (SleepWISP) as well as learn about biomarkers associated with Alzheimer disease (AD). The clinical trial aims to answer the following main questions:\n\n1. Whether the non-invasive transcranial electrical stimulation (TES) delivered by SleepWISP could provide short-term enhancement of deep sleep in a single night in the target population.\n2. Whether TES delivered by SleepWISP could enhance deep sleep over multiple nights in the target population.\n3. Whether enhance on deep sleep could improve memory performance in the target population.\n\nParticipants will be asked to wear non-invasive and painless devices that record their brain activity during sleep along with an actigraphy watch that measures their movement throughout the day. In addition, blood samples or nasal swab assays will be collected from participants multiple times during the study.",[531,532,533,27,534],"Mild Cognitive Impairment","Sleep","Transcranial Electrical Stimulation","Memory",[536,537],"Slow Wave Sleep","NREM N3 Sleep","2025-02-14",{"date":540,"type":33},"2025-02-17",{"date":542,"type":33},"2023-02-08",{"date":133,"type":21},{"name":545,"class":546},"Brain Electrophysiology Laboratory Company","INDUSTRY",{"id":548,"slug":549,"hasResults":12,"nctId":550,"briefTitle":551,"officialTitle":552,"acronym":4,"eligibilityCriteria":553,"healthyVolunteers":12,"sex":48,"minAge":18,"maxAge":4,"enrollmentInfo":554,"targetDuration":4,"studyType":22,"phases":4,"briefSummary":556,"conditions":557,"keywords":560,"overallStatus":61,"whyStopped":4,"lastUpdateSubmitDate":563,"lastUpdatePostDateStruct":564,"startDateStruct":566,"completionDateStruct":568,"leadSponsor":569,"locationsCount":571},"100452842","artificial-intelligence-for-automated-clinical-data-exploration-from-electronic-medical-records-cardiomining-ai-100452842","NCT05176769","Artificial Intelligence for Automated Clinical Data Exploration From Electronic Medical Records (CardioMining-AI)","The Usefulness of Artificial Intelligence for Automated Extraction and Processing of Clinical Data From Electronic Medical Records (CardioMining-AI)","Inclusion Criteria:\n\n* Hospitalised patients in Cardiology Departments in Greece\n* Patients whose medical records are electronically stored in each hospital's computer\u002Finformation systems\n\nExclusion Criteria:\n\n* Patients that died during hospitalization, and thus no discharge letter was issued",{"count":555,"type":21},60000,"The purpose of this study is to highlight the usefulness of artificial intelligence and machine learning to develop computer algorithms that will achieve with great reliability, speed and accuracy the automatic extraction and processing of large volumes of raw and unstructured clinical data from electronic medical files.",[558,27,559],"Artificial Intelligence","Electronic Medical Records",[100,99,561,562],"medical records","digital health","2025-01-27",{"date":565,"type":33},"2025-01-29",{"date":567,"type":33},"2022-01-14",{"date":306,"type":21},{"name":570,"class":40},"AHEPA University Hospital",9,{"id":573,"slug":574,"hasResults":12,"nctId":575,"briefTitle":576,"officialTitle":576,"acronym":577,"eligibilityCriteria":578,"healthyVolunteers":12,"sex":48,"minAge":18,"maxAge":4,"enrollmentInfo":579,"targetDuration":4,"studyType":22,"phases":4,"briefSummary":580,"conditions":581,"keywords":4,"overallStatus":29,"whyStopped":4,"lastUpdateSubmitDate":586,"lastUpdatePostDateStruct":587,"startDateStruct":589,"completionDateStruct":591,"leadSponsor":593,"locationsCount":162},"100575629","use-of-machine-learning-algorithms-biomarkers-and-measures-of-quality-of-life-to-personalize-medical-management-of-liver-and-heart-transplant-recipients-100575629","NCT06774768","Use of Machine-learning Algorithms, Biomarkers and Measures of Quality of Life to Personalize Medical Management of Liver and Heart Transplant Recipients","DARE","Retrospective cohort Inclusion criteria Receiving a heart or liver transplantation at IRCCS AOUBO between January 2008 and December 2020.\n\nSurviving at least 6 months after surgery Receiving at least one outpatient clinical assessment, comprising clinical evaluation, standard laboratory tests, and graft ultrasound Older than 18 years old\n\nExclusion criteria Unavailability of medical records in the standard data repositories of IRCCS AOUBO\n\nProspective cohort\n\nInclusion criteria Older thant 18 years old Receiving a heart or liver transplantation at least 6 months before study entry and being in active follow up at IRCCS AOUBO Obtaining informed consent\n\nExclusion criteria None",{"count":20,"type":21},"This is an observational, low risk tissue based, non-pharmacological, retrospective-prospective study for adults heart and liver transplant patients, related to IRCCS Azienda Ospedaliero-Universitaria di Bologna (IRCCS AOUBO).\n\nThis clinical study is part of the national multicentric project DARE. The project has the wide overarching aim to develop digital solutions for personalized healthcare.",[93,582,583,584,27,585],"Hepatocellular Carcinoma (HCC)","Heart Transplantation","Liver Transplantation","Major Cardiovascular Event","2025-01-09",{"date":588,"type":33},"2025-01-14",{"date":590,"type":21},"2025-01-30",{"date":592,"type":21},"2029-12-31",{"name":213,"class":40},{"id":595,"slug":596,"hasResults":12,"nctId":597,"briefTitle":598,"officialTitle":598,"acronym":4,"eligibilityCriteria":599,"healthyVolunteers":12,"sex":48,"minAge":18,"maxAge":600,"enrollmentInfo":601,"targetDuration":4,"studyType":22,"phases":4,"briefSummary":603,"conditions":604,"keywords":608,"overallStatus":29,"whyStopped":4,"lastUpdateSubmitDate":610,"lastUpdatePostDateStruct":611,"startDateStruct":613,"completionDateStruct":615,"leadSponsor":617,"locationsCount":4},"100549003","accuracy-of-an-artificial-intelligence-assisted-diagnostic-system-for-caries-diagnosis-a-prospective-multicenter-clinical-study-100549003","NCT06428344","Accuracy of an Artificial Intelligence-assisted Diagnostic System for Caries Diagnosis: a Prospective Multicenter Clinical Study","Inclusion Criteria:\n\n\\-\n\nInclusion Criteria:\n\n1. Patients presenting with clinical manifestations of caries as their chief complaint;\n2. Age ≥18 and ≤70 years, irrespective of gender;\n3. Oral panoramic radiographs showing a complete dentition, specifically with the second molars and all premolars intact in each quadrant;\n4. On the oral panoramic radiographs, the number of teeth with restorations or fillings does not exceed one in any quadrant;\n5. Oral panoramic radiographs that are clear, easy to interpret, and free from significant artifacts;\n6. Participants who voluntarily agree to partake, can comprehend the purpose of the study, and are capable of signing an informed consent form.\n\nExclusion Criteria:\n\n1. Oral panoramic radiographs that are unclear, with overlapping, blurring, or artifacts present;\n2. Insufficient number of teeth available for study;\n3. Severe tooth wear or erosion leading to significant alteration in tooth morphology;\n4. Presence of supernumerary teeth, microdontia, or missing teeth;\n5. Conditions not suitable for oral radiography, such as pregnancy or undergoing radiation therapy for tumors;\n6. Limited mouth opening that precludes clinical examination;\n7. Neurological disorders, psychiatric illnesses, or psychological impairments;\n8. Participation in another clinical trial within the last three months;\n9. Any other condition deemed by the researchers as unsuitable for inclusion in the study.","70 Years",{"count":602,"type":21},220,"This clinical trial was designed as a prospective, multicenter, multi-reader multi-case (MRMC), superiority, parallel-controlled study. Participants who met the trial criteria and signed the informed consent form were enrolled. The trial group involved diagnoses of caries on panoramic radiographs using an artificial intelligence-assisted diagnostic system, while the control group involved diagnoses made by dental practitioners specializing in operative dentistry and endodontics with five years of experience, who interpreted oral panoramic radiographs to determine the presence and severity of caries.",[605,606,607,27],"Dental Caries","Artificial Intellegence","Diagnosis",[605,609],"AI Diagnosis","2024-07-08",{"date":612,"type":33},"2024-07-10",{"date":614,"type":21},"2024-08-01",{"date":616,"type":21},"2027-06-01",{"name":618,"class":40},"Zhejiang Provincial People's Hospital",{"id":620,"slug":621,"hasResults":12,"nctId":622,"briefTitle":623,"officialTitle":624,"acronym":4,"eligibilityCriteria":625,"healthyVolunteers":12,"sex":48,"minAge":4,"maxAge":4,"enrollmentInfo":626,"targetDuration":4,"studyType":22,"phases":4,"briefSummary":627,"conditions":628,"keywords":630,"overallStatus":29,"whyStopped":4,"lastUpdateSubmitDate":633,"lastUpdatePostDateStruct":634,"startDateStruct":636,"completionDateStruct":638,"leadSponsor":640,"locationsCount":71},"100548475","using-machine-learning-to-detect-risky-behavior-in-psychiatric-clinics-100548475","NCT06421480","Using Machine Learning to Detect Risky Behavior in Psychiatric Clinics","Detecting Risky Behaviors and Providing a Safe Environment in Patients Receiving Inpatient Treatment in a Psychiatric Clinic Using Machine Learning Model","Inclusion Criteria:\n\n* It is suitable for all adult patients receiving inpatient treatment in psychiatric clinics. It is designed for the room where patients sleep.\n\nExclusion Criteria:\n\n* People under the age of 18 will be excluded from the study",{"count":71,"type":21},"The aim of this study is to ensure the safety of patients in a psychiatric clinic and to detect risky behaviors by using machine learning method. Risky behaviors are defined as behaviors that are personally, socially and developmentally undesirable and endanger life and health.Patient safety and maintaining a safe environment are among the primary duties of healthcare professionals. Suicide is the most important evidence-based risk factor, especially among individuals with psychiatric illnesses, and is one of the most important factors that threaten patient safety. At the end of this study, it is aimed to detect risky behaviors of patients before they harm themselves and to enable healthcare professionals to make early intervention for these behaviors, thus supporting a safe treatment environment, with the computer system that has been trained with the machine learning model installed in the clinics.",[27,629],"Dangerous Behavior",[631,99,632],"psychiatric clinic","risky behavior","2024-06-09",{"date":635,"type":33},"2024-06-11",{"date":637,"type":21},"2024-06-20",{"date":639,"type":21},"2024-09-20",{"name":641,"class":40},"Istanbul Medeniyet University",{"id":643,"slug":644,"hasResults":12,"nctId":645,"briefTitle":646,"officialTitle":647,"acronym":648,"eligibilityCriteria":649,"healthyVolunteers":80,"sex":48,"minAge":18,"maxAge":4,"enrollmentInfo":650,"targetDuration":4,"studyType":53,"phases":652,"briefSummary":653,"conditions":654,"keywords":4,"overallStatus":61,"whyStopped":4,"lastUpdateSubmitDate":658,"lastUpdatePostDateStruct":659,"startDateStruct":661,"completionDateStruct":663,"leadSponsor":665,"locationsCount":71},"100528669","appropriate-use-of-blood-cultures-in-the-emergency-department-through-machine-learning-100528669","NCT06163781","Appropriate Use of Blood Cultures in the Emergency Department Through Machine Learning","Appropriate Use of Blood Cultures in the Emergency Department Through Machine Learning: a Randomized Controlled Trial","ABC","Inclusion Criteria:\n\n* Age \\>= 18 years\n* Have a clinical indication for a blood culture analysis (according to the treating physician)\n* Have sufficient data recorded (laboratory results and vital sign measurements) for a prediction to be made (at least 20% of the needed parameters)\n\nExclusion Criteria:\n\n* Central Venous Line (CVL) or Peripherally Inserted Central Catheter (PICC) in situ\n* Neutrophil count \\\u003C 0.5 \\* 109\u002FL\n* Candidemia or S. aureus bacteraemia in the past 3 months.\n* Most likely diagnosis of endocarditis\u002Fspondylodiscitis\u002Finfected prosthetic material\n* Pregnant or breastfeeding patients\n* Not capable of giving informed consent",{"count":651,"type":21},7584,[55],"The goal of this clinical trial is to study whether the use of our blood culture prediction tool is non-inferior to current practice and if it can improve certain outcomes in all adult patients presenting to the emergency department with a clinical indication for a blood culture analysis (according to the treating physician). The primary endpoint is 30-day mortality. Key secondary outcomes are:\n\n* hospital admission rates\n* in-hospital mortality\n* hospital length-of-stay. In the intervention group, the physician will follow the advice of our blood culture prediction tool.\n\nIn the comparison group all patients will undergo a blood culture analysis.",[558,27,655,656,657],"Microbiology","Emergency Service, Hospital","Randomized Controlled Trial","2024-05-03",{"date":660,"type":33},"2024-05-07",{"date":662,"type":33},"2024-02-19",{"date":664,"type":21},"2027-07",{"name":666,"class":40},"Amsterdam UMC, location VUmc"]