[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-studies-list:{\"conditionNormalized\":\"artifical-intelligence\",\"overallStatus\":[\"RECRUITING\",\"AVAILABLE\",\"NOT_YET_RECRUITING\"],\"orderBy\":\"LastUpdateSubmitDate:desc\",\"size\":25,\"offset\":0}":3,"health-study-condition:artifical-intelligence":28},{"pageToken":4,"total":5,"offset":6,"count":5,"results":7},null,19,0,[8,46,73,114,135,161,186,230,255,281,304,331,356,381,425,454,475,503,530],{"id":9,"slug":10,"hasResults":11,"nctId":12,"briefTitle":13,"officialTitle":14,"acronym":15,"eligibilityCriteria":16,"healthyVolunteers":17,"sex":18,"minAge":19,"maxAge":4,"enrollmentInfo":20,"targetDuration":23,"studyType":24,"phases":4,"briefSummary":25,"conditions":26,"keywords":30,"overallStatus":33,"whyStopped":4,"lastUpdateSubmitDate":34,"lastUpdatePostDateStruct":35,"startDateStruct":38,"completionDateStruct":40,"leadSponsor":42,"locationsCount":45},"100645000","large-language-models-versus-human-examiners-for-grading-physiotherapy-clinical-cases-100645000",false,"NCT07677202","Large Language Models Versus Human Examiners for Grading Physiotherapy Clinical Cases","Agreement Between Large Language Models and Faculty Assessment in the Evaluation of Clinical Reasoning Case Examinations in Undergraduate Physiotherapy Education: A Comparative Reliability Study","PACE-AI","Inclusion Criteria:\n\n* Students officially enrolled in the course \"Specific Methods in Physiotherapy\" (third year of the Physiotherapy Degree) during the study period.\n* Submission of a completed written clinical-reasoning case examination as part of the course.\n* Provision of informed consent for the anonymized examination to be used for educational-research purposes.\n\nExclusion Criteria:\n\n* Refusal to provide, or withdrawal of, informed consent.\n* Blank, incomplete, or non-evaluable examinations (e.g., no developed written response).\n* Examinations that cannot be reliably de-identified prior to assessment.",true,"ALL","18 Years",{"count":21,"type":22},65,"ESTIMATED","1 Day","OBSERVATIONAL","This study evaluates whether large language models (LLMs) can reliably assess written clinical-reasoning case examinations completed by undergraduate physiotherapy students, compared with faculty assessment. In the course \"Specific Methods in Physiotherapy\" (third year of the Physiotherapy Degree), students solve complex clinical cases that require clinical reasoning, technical knowledge, and therapeutic decision-making. These cases are traditionally graded by faculty, a time-consuming process that may show inter-rater variability.\n\nA set of de-identified student case examinations will be assessed using the rubric currently applied in the course, which covers clarity and structure of clinical reasoning, integration of the biopsychosocial model (ICF and APTA frameworks), accuracy in identifying pain mechanisms, coherence between diagnosis, hypotheses, and treatment, originality and depth of analysis, and professional writing. Each examination will be scored independently by three LLMs (for example, Claude, ChatGPT, and Gemini), each receiving an identical standardized prompt that embeds the same rubric, and by faculty serving as the reference standard.\n\nTo avoid overloading faculty, full double human grading may not be feasible; the human reference will therefore consist of expert faculty grading by one independent rater or, when resources allow, two independent raters. In contrast, paired assessment is fully implemented across the AI models: each examination is scored by several LLMs, and each model is queried in duplicate, allowing the study to estimate agreement between models and the test-retest stability of each model.\n\nThe primary aim is to quantify agreement between LLM-generated scores and the faculty reference score. Secondary aims include agreement among the LLMs, test-retest reliability of each model, criterion-level agreement, the quality and usefulness of the qualitative feedback generated, the time and cost associated with each approach, and students' perceptions of the usefulness of human versus AI feedback.\n\nThe findings will clarify the strengths and limitations of LLMs as supportive tools for formative assessment in health-professions education and will inform criteria for their responsible and effective use. No LLM output will affect students' official grades, which remain the sole responsibility of faculty.",[27,28,29],"Educational Assessment","Artifical Intelligence","Physical Therapy Education",[31,32],"Clinical Competence","Educational, Medical","NOT_YET_RECRUITING","2026-06-24",{"date":36,"type":37},"2026-06-30","ACTUAL",{"date":39,"type":22},"2026-08-01",{"date":41,"type":22},"2026-08-10",{"name":43,"class":44},"Neuron, Spain","OTHER",1,{"id":47,"slug":48,"hasResults":11,"nctId":49,"briefTitle":50,"officialTitle":51,"acronym":52,"eligibilityCriteria":53,"healthyVolunteers":11,"sex":18,"minAge":19,"maxAge":54,"enrollmentInfo":55,"targetDuration":4,"studyType":24,"phases":4,"briefSummary":57,"conditions":58,"keywords":61,"overallStatus":33,"whyStopped":4,"lastUpdateSubmitDate":65,"lastUpdatePostDateStruct":66,"startDateStruct":67,"completionDateStruct":69,"leadSponsor":71,"locationsCount":45},"100641629","bispectral-index-in-patients-undergoing-vertebral-surgery-using-artificial-intelligence-programs-a-methodological-study-100641629","NCT07650604","Bispectral Index in Patients Undergoing Vertebral Surgery Using Artificial Intelligence Programs: A Methodological Study","INTERPRETATION AND EVULATION OF THE PROXIMITY TO CLINICAL EXPERIENCE OF BISPECTRAL INDEX (BIS) IN PATIENTS UNDERGOİNG VERTEBRAL SURGERY USING ARTIFICIAL INTELLIGENCE PROGRAMS: A METHODOLOGICAL STUDY","BIS","Inclusion Criteria:\n\n* elective vertebra surgery\n* aged 18-65\n* ASA score I-III\n* BMI \\\u003C30 kg\u002Fm2\n\nExclusion Criteria:\n\n* patient's refusal\n* BMI \\>30 kg\u002Fm2\n* serious liver or kidney disease\n* ASA 4 ve more\n* anatomical abnormality at probe site\n* history of mental or neurological disease,\n* history of previous intracranial aneurysm or intracranial tumor surgery\n* history of moderate or severe pulmonary disease,\n* emergency surgery","65 Years",{"count":56,"type":22},63,"This study aims to interpret the Bispectral Index (BIS) monitoring method, which we routinely use for monitoring in scoliosis surgery, with artificial intelligence (AI) tools and to determine the accuracy and reliability of AI tools in clinical practice by comparing this interpretation with the interpretations of two clinicians experienced in BIS.",[28,59,60],"Bispectral Index","Spine",[62,63,64],"artificial intelligence","bispectral index","vertebra surgery","2026-06-22",{"date":34,"type":37},{"date":68,"type":22},"2026-06-10",{"date":70,"type":22},"2026-12-10",{"name":72,"class":44},"Antalya Health Sciences University",{"id":74,"slug":75,"hasResults":11,"nctId":76,"briefTitle":77,"officialTitle":77,"acronym":78,"eligibilityCriteria":79,"healthyVolunteers":17,"sex":18,"minAge":19,"maxAge":4,"enrollmentInfo":80,"targetDuration":4,"studyType":24,"phases":4,"briefSummary":82,"conditions":83,"keywords":99,"overallStatus":104,"whyStopped":4,"lastUpdateSubmitDate":105,"lastUpdatePostDateStruct":106,"startDateStruct":108,"completionDateStruct":110,"leadSponsor":112,"locationsCount":45},"100643686","development-of-a-mobile-terminal-based-intelligent-detection-system-for-multiple-anterior-segment-diseases-of-the-eye-100643686","NCT07634913","Development of a Mobile Terminal-Based Intelligent Detection System for Multiple Anterior Segment Diseases of the Eye","LENS","Inclusion Criteria:\n\n* Adults aged 18 years or older;\n* Willing to participate and able to provide written informed consent prior to enrollment.\n\nExclusion Criteria:\n\n* Unable to cooperate with anterior segment image capture (including smartphone-based photography or slit-lamp biomicroscopy).",{"count":81,"type":22},3000,"This is a multi-center, cross-sectional study evaluating a smartphone-based artificial intelligence (AI) system for anterior segment eye disease screening. The system is designed to identify 16 clinically important anterior segment conditions from images captured using a standard Android smartphone. A core design feature of the system is that all image analysis is performed entirely on the smartphone itself, without requiring internet connectivity or cloud-based server infrastructure.\n\nThe study is motivated by a structural challenge in the deployment of medical AI: systems that depend on cloud infrastructure for inference are non-functional in settings without reliable internet access, which disproportionately excludes populations in low-resource regions where the burden of preventable eye disease is highest. This study evaluates whether an on-device AI system, designed with operational constraints as a primary engineering objective, can deliver clinically acceptable diagnostic performance while remaining operable under real-world connectivity limitations.\n\nThe study comprises five evaluation components. First, the diagnostic performance of the AI system is benchmarked against board-certified ophthalmologists of varying seniority on a standardized set of smartphone-captured anterior segment images. Second, the usability of the system is evaluated among non-medical users who perform self-administered screening with minimal instruction, with per-screening time recorded across consecutive attempts to characterize the learning curve. Third, a head-to-head field trial directly compares the on-device AI system against a functionally equivalent cloud-based deployment of the same model architecture across key operational dimensions including screening duration, diagnostic performance, and user acceptability. Fourth, population-level screening is conducted among consecutively enrolled community residents at two low-resource sites, with per-disease sensitivity and specificity calculated against reference-standard slit-lamp examinations. Fifth, pre-specified health-economic and environmental analyses compare the two deployment modalities in terms of per-person screening cost, cost-effectiveness, per-inference electricity consumption, and projected carbon emissions at scale.\n\nThe reference standard for all diagnostic comparisons is slit-lamp biomicroscopic examination performed by board-certified ophthalmologists. The study is designed and reported in accordance with the DECIDE-AI reporting guideline for early-stage clinical evaluation of AI-driven decision-support systems.",[28,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98],"Cataract","Pterygium","Keratopathy","Subconjunctival Hemorrhage","Conjunctivitis","Stye","Blepharitis","Entropion","Ectropion","Exophthalmos","Irregular Pupils","Conjunctival Concretions","Hyphema","Hypopyon","Corneal Transplant Status",[100,101,102,103],"Artificial Intelligence","Standalone Deployment","Smartphone","Eye Disease Screening","RECRUITING","2026-06-08",{"date":107,"type":37},"2026-06-09",{"date":109,"type":37},"2023-12-12",{"date":111,"type":22},"2028-12",{"name":113,"class":44},"Zhongshan Ophthalmic Center, Sun Yat-sen University",{"id":115,"slug":116,"hasResults":11,"nctId":117,"briefTitle":118,"officialTitle":118,"acronym":119,"eligibilityCriteria":120,"healthyVolunteers":17,"sex":18,"minAge":19,"maxAge":4,"enrollmentInfo":121,"targetDuration":4,"studyType":24,"phases":4,"briefSummary":123,"conditions":124,"keywords":4,"overallStatus":104,"whyStopped":4,"lastUpdateSubmitDate":126,"lastUpdatePostDateStruct":127,"startDateStruct":129,"completionDateStruct":131,"leadSponsor":133,"locationsCount":45},"100614819","smartphone-based-digital-screening-for-aortic-valve-stenosis-100614819","NCT07284550","Smartphone Based Digital Screening for Aortic Valve Stenosis","SMART-VALVE","The following inclusion and exclusion criteria will be used for training, validation and test sets:\n\nInclusion criteria for group I (moderate to severe AS):\n\n* Moderate to severe AS defined as AVA ≤ 1.5cm² in echocardiographic assessment\n* No other significant VHD, valvular prosthesis, pacemaker or congenital heart defect\n* Documented echocardiography as part of routine clinical practice no older than 90 days\n* Patient age ≥ 18 years\n* Provided written informed consent\n\nInclusion criteria for group II:\n\n* No significant VHD, valvular prosthesis, pacemaker or congenital heart defect\n* Documented echocardiography as part of routine clinical practice no older than 90 days\n* Patient age ≥ 18 years\n* Provided written informed consent\n\nExclusion criteria (applicable for all groups):\n\n• Informed consent form not signed.",{"count":122,"type":22},500,"Heart valve diseases are among the most serious cardiovascular conditions in older age. One of the most common forms is aortic valve stenosis, a narrowing of the valve opening between the left ventricle and the main artery. As the valve becomes tighter, the heart must work harder and harder to pump blood through the body. This process often develops slowly over many years and initially causes no clear symptoms. As a result, the condition is frequently detected only in advanced stages, when warning signs such as shortness of breath, chest pain, or dizziness appear. Without treatment, aortic valve stenosis can become life-threatening. If detected early, however, very effective treatment options are available today.\n\nUp to now, the disease has been reliably diagnosed mainly through echocardiography. Yet this method is complex, costly, and requires specialized medical staff. A simple, affordable, and broadly accessible screening option does not yet exist.\n\nThe interdisciplinary clinical research project explores whether conventional smartphones could fill this gap. Almost all modern devices are equipped with sensors such as microphones, accelerometers, and gyroscopes. These can capture both heart sounds and subtle vibrations of the chest. The research team is investigating whether reliable diagnostic information for the diagnosis of aortic valve stenosis can be extracted from such recordings. To achieve this, the signals are processed with newly developed methods and analyzed using artificial intelligence.\n\nFor the study, several hundred patients with and without valve disease will be examined. The smartphone results will be compared with established diagnostic standards, particularly echocardiography, to test accuracy and reliability.\n\nIf successful, the approach could enable a straightforward, digital heart check at home using nothing more than a conventional smartphone. Such a tool would provide an accessible, low-cost, and widely available method for early detection, helping more people receive timely and potentially life-saving treatment.",[125,28],"Aortic Valve Stenosis","2026-05-27",{"date":128,"type":37},"2026-05-28",{"date":130,"type":37},"2026-01-12",{"date":132,"type":22},"2029-11",{"name":134,"class":44},"Medical University Innsbruck",{"id":136,"slug":137,"hasResults":11,"nctId":138,"briefTitle":139,"officialTitle":140,"acronym":141,"eligibilityCriteria":142,"healthyVolunteers":17,"sex":18,"minAge":19,"maxAge":4,"enrollmentInfo":143,"targetDuration":4,"studyType":24,"phases":4,"briefSummary":145,"conditions":146,"keywords":4,"overallStatus":104,"whyStopped":4,"lastUpdateSubmitDate":151,"lastUpdatePostDateStruct":152,"startDateStruct":154,"completionDateStruct":156,"leadSponsor":158,"locationsCount":160},"100637518","aid-fog-artificial-intelligence-driven-freezing-of-gait-detection-in-the-home-100637518","NCT07580612","AID-FOG: Artificial Intelligence-Driven Freezing of Gait Detection in the Home","Artificial Intelligence-Driven Freezing Of Gait Detection in the Home: Investigating How Free-living Activities Affect the Algorithm","AID-FOG","Inclusion Criteria:\n\nFor all participants\n\n* Voluntary written informed consent of the participant has been obtained prior to any study-related procedures, except the non-recorded pre-screening questions;\n* At least 18 years of age at the time of signing the Informed Consent Form (ICF);\n* Person is cognitively able to follow and understand instructions and provide voluntary written informed consent;\n* Person is able to walk for short distances (± 10 meters) independently, with- or without use of a walking aid;\n* Person does not live in a temporary or permanent care facility.\n\nFor participants with PD:\n\n* Clinical diagnosis of Parkinson's disease (PD) made by a neurologist according to the Movement Disorders Society guidelines;\n* Person self-reports to experience daily FOG (for recruitment of freezers only);\n* Person is willing to temporarily delay the morning anti-Parkinsonian medication during the standardized assessment visit.\n\nExclusion criteria:\n\n* Occurrence of any of the following within 3 months prior to informed consent: myocardial infarction, hospitalization for unstable angina, stroke, coronary artery bypass graft (CABG), percutaneous coronary intervention (PCI), implantation of a cardiac resynchronization therapy device (CRTD), active treatment for cancer or other malignant disease, uncontrolled congestive heart disease (NYHA class \\>3), acute psychosis or major psychiatric disorders or continued substance abuse, other neurological (than PD) or orthopaedic impairment that significantly impacts on gait;\n* Participant self-reports daily falls;\n* Participation in another interventional study, with or without an investigational medicinal product (IMP) or device (IMD)",{"count":144,"type":22},126,"Freezing of gait (FOG) is a debilitating symptom of Parkinson's disease increases the risk of falling. Despite being a common symptom, it is still difficult to evaluate freezing of gait quickly and accurately. Currently, the gold-standard method to determine the severity of FOG is a manual analysis of video footage by an experienced assessor, collected during standardized FOG-provoking walking tests. Because this is a very time-intensive process, where different assessors sometimes obtain different results, our team at KU Leuven have developed an artificial-intelligent (AI) algorithm trained to identify FOG episodes based on wearable inertial measurement unit (IMU) sensor data. The AI algorithm has already undergone initial validation during laboratory testing, yielding promising results. The aim of this study is to investigate whether the AI algorithm can accurately detect FOG episodes in a less controlled environment, namely the home environment. In a second phase, the investigators will also use the collected data to improve the AI algorithm for automated FOG detection in the home. Finally, the investigators want to explore whether the AI algorithm can detect FOG in real-time.",[147,148,149,150,28],"Parkinson Disease, Idiopathic","Freezing of Gait","Validation","Wearable Sensors","2026-05-05",{"date":153,"type":37},"2026-05-12",{"date":155,"type":37},"2025-09-22",{"date":157,"type":22},"2027-06",{"name":159,"class":44},"KU Leuven",3,{"id":162,"slug":163,"hasResults":11,"nctId":164,"briefTitle":165,"officialTitle":166,"acronym":4,"eligibilityCriteria":167,"healthyVolunteers":17,"sex":18,"minAge":4,"maxAge":4,"enrollmentInfo":168,"targetDuration":4,"studyType":170,"phases":171,"briefSummary":173,"conditions":174,"keywords":4,"overallStatus":104,"whyStopped":4,"lastUpdateSubmitDate":177,"lastUpdatePostDateStruct":178,"startDateStruct":180,"completionDateStruct":182,"leadSponsor":184,"locationsCount":45},"100636179","ai-supported-flipped-learning-in-breast-self-examination-training-100636179","NCT07562321","AI-SUPPORTED FLIPPED LEARNING IN BREAST SELF-EXAMINATION TRAINING","IMPACT OF AN AI-SUPPORTED FLIPPED LEARNING MODEL ON NURSING STUDENTS' BREAST SELF-EXAMINATION KNOWLEDGE AND PERFORMANCE: A RANDOMIZED CONTROLLED TRIAL","Inclusion Criteria:\n\n* Being a second-year student in a nursing undergraduate program\n* Not having previously received breast examination training\n* Having signed the voluntary consent form\n\nExclusion Criteria:\n\n* Having any health problem that would prevent continuing to work\n* Requesting to withdraw from work voluntarily",{"count":169,"type":22},80,"INTERVENTIONAL",[172],"NA","The global increase in cancer cases has made breast cancer the second most common cancer after lung cancer and a primary health problem among women. Early diagnosis is the most critical factor in improving survival rates and quality of life in breast cancer. Breast self-examination (BSE), which enables individuals to notice changes in their own breast tissue during the early diagnosis process, is a low-cost and effective awareness method. It is essential that nurses, who play a key role in raising public awareness on this issue, and nursing students, who are the future healthcare professionals, have sufficient knowledge and practical skills in BSE. However, the literature shows that even if students have theoretical knowledge, their application rates are low. In this context, the \"AI-Supported Flipped Learning\" model, which goes beyond traditional methods and supports active learning, personalized feedback, and digital literacy, has the potential to be an innovative solution in nursing education. Objective: This study aims to evaluate the effect of AI-supported flipped learning model and traditional education on the knowledge levels and performance skills of nursing students regarding BSE knowledge and skills.",[28,175,176],"Nursing Education","Breast Self-Examination","2026-04-30",{"date":179,"type":37},"2026-05-06",{"date":181,"type":37},"2026-03-20",{"date":183,"type":22},"2026-05-10",{"name":185,"class":44},"Baskent University",{"id":187,"slug":188,"hasResults":11,"nctId":189,"briefTitle":190,"officialTitle":190,"acronym":4,"eligibilityCriteria":191,"healthyVolunteers":17,"sex":18,"minAge":192,"maxAge":193,"enrollmentInfo":194,"targetDuration":4,"studyType":170,"phases":196,"briefSummary":197,"conditions":198,"keywords":212,"overallStatus":104,"whyStopped":4,"lastUpdateSubmitDate":219,"lastUpdatePostDateStruct":220,"startDateStruct":222,"completionDateStruct":224,"leadSponsor":226,"locationsCount":229},"100636162","develop-and-evaluate-an-artificial-intelligence-assisted-prehabilitation-program-for-returning-to-work-and-cost-effectiveness-analysis-in-patients-with-oral-cancer-100636162","NCT07562100","Develop and Evaluate An Artificial Intelligence Assisted Prehabilitation Program for Returning to Work and Cost-effectiveness Analysis in Patients With Oral Cancer","Inclusion Criteria:\n\n* Adult (\\> 20 years old and younger than 70 years old)\n* Newly diagnosed as OC and scheduled to receive cancer-related treatment\n* Able to use Mobile phone\n* Willing to sign an informed consent form after receiving a detailed explanation of the study's aims and procedures\n* Healthcare professionals involved in patients' care, including doctors, nurses, case managers, dietitians, rehabilitation therapists, and psychologists\n* Family members who are primary caregivers of the participating patients, engaged in different stages of medical care\n\nExclusion Criteria:\n\n* Risk populations for walking or performing exercise\n* Patients with cognitive impairment or psychiatric diseases","20 Years","70 Years",{"count":195,"type":22},650,[172],"The goal of this clinical trial is to develop and evaluate an Artificial Intelligence Assisted Prehabilitation Program (AI APP) for returning to work and cost-effectiveness analysis in patients with oral cancer (OC). The main questions it aims to answer are:\n\n* What kinds of needs are related to returning to work (RTW) in patients with OC from diagnosis to survival that we can incorporate into the development of AI APP to assist this population ?\n* How is the effect of the AI APP that based on findings from the first question for patients with OC on physical and psychological distress, fear of recurrence, self-efficacy in coping with cancer, communication, motor function, quality of life, and RTW?\n* How is the effect of the RTW AI prediction model to identify high-risk groups ? And how is the comprehensive cost effectiveness of benefits and quality of life of the AI APP for OC population?\n\nResearchers will compare patients without using AI APP to see if the AI APP works to assist with coping physical and psychological distress, communication, motor function, quality of life, and RTW issues for individuals with OC?\n\nParticipants will:\n\n* Be asked to fulfill a structural questionnaire, or engage in a semi-structured one-by-one interview or a focus group to assess their physical, psychological, and social support needs in the first stage.\n* Be invited to participant the pilot testing of AI APP in the second stage.\n* Be provided and trained by 3-month AI APP for 3 months or cared as usual in the third stage.\n* Complete a structural questionnaire and follow up one year, including the baseline (before using the AI app) and at 1-2 weeks, 3 months, 6 months, 9 months, and 12 months after the baseline.\n* Engage in one-by-one interview or a focus group to assess user experiences of the AI APP.",[199,200,201,202,203,204,205,206,207,208,209,28,210,211],"Oral Cancer","Psychological Distress","Communication Aids for Disabled","Physiotherapy","Return to Work","Prehabilitation","Physical Symptom Distress","Motor Function","Rehabilitation","Quality of Life","Cost Effectiveness","Prediction Model","Case Management, APP(Application)",[100,203,199,200,201,202,204,213,214,207,215,216,217,218],"Physical symptom distress","motor function","Quality of life","Cost effectiveness","prediction model","case management, APP(Application)","2026-04-24",{"date":221,"type":37},"2026-05-01",{"date":223,"type":37},"2024-09-27",{"date":225,"type":22},"2029-12-01",{"name":227,"class":228},"Taipei Veterans General Hospital, Taiwan","OTHER_GOV",5,{"id":231,"slug":232,"hasResults":11,"nctId":233,"briefTitle":234,"officialTitle":234,"acronym":235,"eligibilityCriteria":236,"healthyVolunteers":11,"sex":18,"minAge":19,"maxAge":4,"enrollmentInfo":237,"targetDuration":239,"studyType":24,"phases":4,"briefSummary":240,"conditions":241,"keywords":4,"overallStatus":33,"whyStopped":4,"lastUpdateSubmitDate":246,"lastUpdatePostDateStruct":247,"startDateStruct":249,"completionDateStruct":251,"leadSponsor":253,"locationsCount":45},"100632601","qatar-cardiometabolic-retrospective-cohort-analysis-using-artificial-intelligence-100632601","NCT07515807","Qatar Cardiometabolic Retrospective Cohort-Analysis Using Artificial Intelligence","QCRC-AI","Inclusion Criteria:\n\n* Age ≥ 18\n* Qatari and Arab participants\n* Participants admitted for Acute Coronary Syndrome (ACS) or Acute Heart Failure (AHF)\n* Metabolic disease: Diabetes (HbA1C ≥ 6.5% or any HbA1C if a patient is on an antidiabetic agent) or pre-diabetes: 5.7% ≤ HbA1c ≤ 6.4%\n\nExclusion Criteria:\n\n* Non-Qatari or non-Arab participants\n* Non-diabetic: HbA1C \\\u003C 5.7%\n* This chart review involves no direct interaction with individuals. Prisoners are not a focus of this study, and incarceration status is not identifiable in the records reviewed.",{"count":238,"type":22},10000,"2 Years","Cardiovascular disease is the leading cause of death worldwide, and individuals with diabetes or other cardiometabolic conditions are at increased risk of adverse cardiovascular outcomes. Although advances in prevention and treatment have reduced cardiovascular events globally, cardiometabolic disease continues to represent a significant health burden, particularly in regions with high diabetes prevalence. In Qatar and other Gulf Cooperation Council countries, the prevalence of diabetes and obesity is increasing, contributing to a high proportion of participants presenting with acute coronary syndrome who have type 2 diabetes or prediabetes. This observational study will use electronic medical record data from patients hospitalized at the Heart Hospital with acute coronary syndrome and a concomitant diagnosis of diabetes or prediabetes. The study will assess trends in cardiovascular risk factors and cardiovascular events, including readmission and mortality. An artificial intelligence component will be used to develop and validate machine learning based risk prediction models to forecast adverse cardiovascular outcomes in participants with cardiometabolic disease. These models will integrate clinical, biochemical, imaging, and other non-invasive data routinely collected during participants care to identify predictors of cardiovascular events.",[242,243,244,245,28],"Cardio Vascular Disease","Acute Coronary Syndromes (ACS)","Type 2 Diabetes","Pre Diabetes","2026-04-15",{"date":248,"type":37},"2026-04-21",{"date":250,"type":22},"2026-07-16",{"date":252,"type":22},"2030-07-16",{"name":254,"class":44},"Weill Cornell Medical College in Qatar",{"id":256,"slug":257,"hasResults":11,"nctId":258,"briefTitle":259,"officialTitle":260,"acronym":4,"eligibilityCriteria":261,"healthyVolunteers":17,"sex":18,"minAge":4,"maxAge":4,"enrollmentInfo":262,"targetDuration":23,"studyType":24,"phases":4,"briefSummary":264,"conditions":265,"keywords":4,"overallStatus":33,"whyStopped":4,"lastUpdateSubmitDate":272,"lastUpdatePostDateStruct":273,"startDateStruct":275,"completionDateStruct":277,"leadSponsor":279,"locationsCount":45},"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":263,"type":22},115,"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.",[52,266,28,267,268,269,270,271],"BIS-EEG","Intraoperative","Machine Learning","Anesthesia","Anesthesia Awareness","Predictive Model","2026-04-10",{"date":274,"type":37},"2026-04-17",{"date":276,"type":22},"2026-06-01",{"date":278,"type":22},"2026-09-01",{"name":280,"class":44},"Universitair Ziekenhuis Brussel",{"id":282,"slug":283,"hasResults":11,"nctId":284,"briefTitle":285,"officialTitle":286,"acronym":287,"eligibilityCriteria":288,"healthyVolunteers":11,"sex":289,"minAge":19,"maxAge":4,"enrollmentInfo":290,"targetDuration":4,"studyType":170,"phases":292,"briefSummary":293,"conditions":294,"keywords":4,"overallStatus":104,"whyStopped":4,"lastUpdateSubmitDate":296,"lastUpdatePostDateStruct":297,"startDateStruct":299,"completionDateStruct":301,"leadSponsor":302,"locationsCount":45},"100614976","study-comparing-two-image-acquisition-modalities-for-second-trimester-pregnancy-screening-ultrasound-echo-ia-100614976","NCT07286591","Study Comparing Two Image Acquisition Modalities for Second-trimester Pregnancy Screening Ultrasound (Echo-IA)","A Single-center Cross-sectional Study Comparing Two Image Acquisition Modalities for Second-trimester Pregnancy Screening Ultrasound.","Echo-IA","Inclusion Criteria:\n\n* Women aged 18 or over,\n* With a single, viable pregnancy, definitively dated by first-trimester ultrasound, with no known malformations,\n* Scheduled for a routine second-trimester screening ultrasound, i.e., between 20 weeks + 0 days and 24 weeks + 6 days,\n* Having given their informed consent,\n* Affiliated with the social security system or a beneficiary of such a plan.\n\nExclusion Criteria:\n\n* Multiple pregnancy,\n* Known fetal malformation,\n* Pathological pregnancy,\n* Cognitive impairment, or a disorder causing difficulty understanding instructions or answering questionnaires,\n* Patient under legal guardianship,\n* Patient not covered by health insurance,\n* Protected patient: adult under guardianship, curatorship, or other legal protection, deprived of liberty by judicial or administrative decision","FEMALE",{"count":291,"type":22},50,[172],"The second-trimester morphology ultrasound is a key examination in obstetric monitoring that aims to assess fetal growth, identify any structural abnormalities, and inspect anexes such as placenta, umbilical cord, cervix,...\n\nSeveral studies suggest that a significant proportion of fetal malformations can be detected during this time frame if a complete morphological analysis is performed. However, the reliability of the screening depends on the quality of the equipment, the operator's level of expertise, and adherence to protocols that define the necessary scans.\n\nIn France, since the first reports of the National Technical Committee on Prenatal Screening Ultrasound (2005), particular attention has been paid to standardizing practices. More recently, the French National Conference on Obstetric and Fetal Ultrasound (CNEOF) published new recommendations (2022, revised in 2023) including the development of reference silhouettes for the second-trimester examination, proposing 26 views (22 required and 4 additional). However, the CNEOF does not formalize quality criteria for evaluating the conformity of these images; this task has been taken over by the French College of Fetal Ultrasound (CFEF), which has established a scoring and validation grid for each fetal slice (see CFEF 2022 document).\n\nIn parallel, artificial intelligence (AI) is gradually becoming established as a decision support and automation tool in medical imaging, particularly in ultrasound. Deep learning algorithms are capable of identifying anatomical structures, positioning measurement markers, and selecting the most optimal slice, reducing inter-operator variability and streamlining workflow. In the field of obstetric ultrasound, some companies have launched systems capable of detecting or annotating fetal structures in real time, potentially improving diagnostic reliability and reproducibility. Samsung has developed a system called Live View Assist, available on its latest generation ultrasound scanners, which uses AI to automatically recognize and freeze the required fetal slices in real time.\n\nThe tool also offers automated validation: if the detected slice conforms to the expected standards, it is directly checked off on a checklist. This innovation promises time savings, a reduced risk of missing certain complex slices, and improved standardization.\n\nHowever, there is little data, particularly in France, regarding to the actual performance of this tool in a routine screening context. Before considering the integration of Live View Assist and AI into daily practice, it is therefore essential to evaluate the quality of the images it acquires, the feasibility of a complete examination assisted by AI, as well as the potential impact on examination time and improvement of the workload for sonographers.\n\nThe aim of this study is to evaluate whether the quality of the 20 mandatory images automatically validated by Live View Assist is not inferior to that of the 20 mandatory images acquired and validated manually by an ultrasound technician, according to the CFEF quality criteria based on the silhouettes recommended by the CNEOF.",[28,295],"Echography Ultrasound","2026-03-16",{"date":298,"type":37},"2026-03-17",{"date":300,"type":37},"2025-12-15",{"date":36,"type":22},{"name":303,"class":44},"Clinique Rive Gauche",{"id":305,"slug":306,"hasResults":11,"nctId":307,"briefTitle":308,"officialTitle":308,"acronym":4,"eligibilityCriteria":309,"healthyVolunteers":17,"sex":18,"minAge":19,"maxAge":4,"enrollmentInfo":310,"targetDuration":4,"studyType":170,"phases":312,"briefSummary":313,"conditions":314,"keywords":317,"overallStatus":104,"whyStopped":4,"lastUpdateSubmitDate":322,"lastUpdatePostDateStruct":323,"startDateStruct":325,"completionDateStruct":327,"leadSponsor":329,"locationsCount":45},"100628921","the-long-term-effect-of-artificial-intelligence-assisted-colonoscopy-on-risk-of-metachronous-advanced-colonic-lesion-100628921","NCT07467928","The Long-term Effect of Artificial Intelligence-assisted Colonoscopy on Risk of Metachronous Advanced Colonic Lesion","Inclusion Criteria:\n\n* Eligible participants are those who were previously enrolled and completed our randomized controlled trial comparing AI-assisted and conventional colonoscopy\n\nExclusion Criteria:\n\n* In addition to the baseline exclusion criteria of the index trial, patients who were found to have inflammatory bowel disease, colorectal cancer and underwent bowel resection would be excluded. Similar to index trial, patients who have developed severe comorbid illnesses that make surveillance colonoscopy and polypectomy unsafe, or become unable to provide informed consent for trial participation would also be excluded\n\nIndex trial: Lui TK, Lam CP, To EW, Ko MK, Tsui VWM, Liu KS, et al. Endocuff With or Without Artificial Intelligence-Assisted Colonoscopy in Detection of Colorectal Adenoma: A Randomized Colonoscopy Trial. Am J Gastroenterol. 2024;119(7):1318-25",{"count":311,"type":22},404,[172],"The goal of this prospective study is to to evaluate the prevalence of metachronous advanced colonic lesions in subsequent surveillance colonoscopies in patients who had previously undergone AI-assisted colonoscopy to conventional colonoscopy examinations.\n\nThe main question it aims to answer is whether employing AI-assisted colonoscopy can decrease the likelihood of metachronous advanced colonic lesions during subsequent surveillance colonoscopies.\n\nResearchers will compare patient who undergo conventional colonoscopy in previous colonoscopy to see if AI-assisted colonoscopy can decrease the likelihood of metachronous advanced colonic lesions during subsequent surveillance colonoscopies. Participants will undergo surveillance colonoscopy to assess the presence of metachronous advanced colonic lesion",[315,28,316],"Colonic Polyps","Advanced Metachonous Colonic Lesion",[318,319,320,321],"Colonic polyps","Artificial intelligence","Surveillance colonoscopy","advanced metachonous colonic lesion","2026-03-08",{"date":324,"type":37},"2026-03-12",{"date":326,"type":37},"2025-07-01",{"date":328,"type":22},"2028-02-28",{"name":330,"class":44},"The University of Hong Kong",{"id":332,"slug":333,"hasResults":11,"nctId":334,"briefTitle":335,"officialTitle":335,"acronym":4,"eligibilityCriteria":336,"healthyVolunteers":17,"sex":18,"minAge":19,"maxAge":4,"enrollmentInfo":337,"targetDuration":4,"studyType":170,"phases":339,"briefSummary":340,"conditions":341,"keywords":344,"overallStatus":104,"whyStopped":4,"lastUpdateSubmitDate":348,"lastUpdatePostDateStruct":349,"startDateStruct":351,"completionDateStruct":353,"leadSponsor":354,"locationsCount":45},"100582123","evaluating-ai-generated-plain-language-summaries-on-patient-comprehension-of-ophthalmology-notes-among-english-speaking-patients-100582123","NCT06859216","Evaluating AI-Generated Plain Language Summaries on Patient Comprehension of Ophthalmology Notes Among English-Speaking Patients","Inclusion Criteria:\n\n* Age ≥ 18 years English-speaking Receiving ophthalmology care at the Jules Stein Eye Institute Able to provide informed consent\n\nExclusion Criteria:\n\n* Known cognitive impairments (e.g., dementia, intellectual disability) that would affect comprehension Prisoners or wards of the state Unable to provide informed consent",{"count":338,"type":22},460,[172],"This clinical trial is testing whether plain language summaries made by artificial intelligence help people understand their eye doctor's notes better. Adults receiving eye care at the Jules Stein Eye Institute will get either the usual medical notes or a note with the addition of an AI-generated summary that explains the information in simple, everyday words. Participants will then answer a short survey and receive a follow-up call to share how clear the information was, how well they understood their diagnosis and treatment, and whether they feel more confident about their care. The goal is to find out if these plain language summaries can make it easier for people to understand their eye care and improve communication between patients and health care providers.",[342,28,343],"Ophthalmic Disease","Large Language Model",[345,346,28,347],"Plain Language Summary","LLM","Patient education","2026-03-03",{"date":350,"type":37},"2026-03-05",{"date":352,"type":37},"2025-03-01",{"date":276,"type":22},{"name":355,"class":44},"University of California, Los Angeles",{"id":357,"slug":358,"hasResults":11,"nctId":359,"briefTitle":360,"officialTitle":361,"acronym":362,"eligibilityCriteria":363,"healthyVolunteers":11,"sex":18,"minAge":364,"maxAge":365,"enrollmentInfo":366,"targetDuration":4,"studyType":170,"phases":368,"briefSummary":369,"conditions":370,"keywords":4,"overallStatus":104,"whyStopped":4,"lastUpdateSubmitDate":373,"lastUpdatePostDateStruct":374,"startDateStruct":375,"completionDateStruct":377,"leadSponsor":379,"locationsCount":45},"100627718","artificial-intelligence-based-cognitive-training-in-patients-with-stroke-100627718","NCT07452276","Artificial Intelligence-Based Cognitive Training in Patients With Stroke","Effect of Artificial Intelligence-Based Training on Cognitive Functions in Patients With Stroke","AICoRe-Stroke","Inclusion Criteria:\n\n* Patient's age will range from 45- 60 years.\n\n  * Diagnosis of stroke in the chronic stage (≥3 months post-onset).\n  * Presence of cognitive impairment confirmed by a Montreal Cognitive Assessment (MoCA) score ≤ 26.\n  * Medically stable and able to follow instructions.\n  * patients with memory deficits following stroke\n\nExclusion Criteria:\n\n\\- Patients will be excluded if they have:\n\n* Severe visual, hearing, or speech impairments prevent participation.\n* History of other neurological or psychiatric disorders.\n* Unstable cardiovascular or systemic disease.\n* Severe depression (Beck Depression Inventory \\> 29).","45 Years","60 Years",{"count":367,"type":22},40,[172],"This study would answer the following question:Does AI application-based training improve cognitive function in Patients with Stroke?\n\nThe aims of this study:\n\nTo investigate the efficacy of AI application-based training on cognitive function in stroke patients.",[371,372,28],"Post-Stroke Cognitive Impairment (PSCI)","Stroke","2026-03-01",{"date":350,"type":37},{"date":376,"type":37},"2026-01-01",{"date":378,"type":22},"2026-06-06",{"name":380,"class":44},"Omima Alaa Eldin Hussein",{"id":382,"slug":383,"hasResults":11,"nctId":384,"briefTitle":385,"officialTitle":386,"acronym":387,"eligibilityCriteria":388,"healthyVolunteers":17,"sex":289,"minAge":389,"maxAge":364,"enrollmentInfo":390,"targetDuration":4,"studyType":24,"phases":4,"briefSummary":392,"conditions":393,"keywords":402,"overallStatus":104,"whyStopped":4,"lastUpdateSubmitDate":416,"lastUpdatePostDateStruct":417,"startDateStruct":419,"completionDateStruct":421,"leadSponsor":423,"locationsCount":45},"100612012","smartphone-vs-manual-interpretation-of-biomarkers-for-ovulation-and-luteal-phase-detection-smom-study-100612012","NCT07248046","Smartphone vs Manual Interpretation of Biomarkers for Ovulation and Luteal Phase Detection (SMOM Study)","Comparing Cervical Mucus, PDG, LH, and Basal Body Temperature Combinations for Ovulation and Luteal Phase Identification Using the Premom Smartphone App Versus User-Read Test Results: A Prospective Observational Study","SMOM","Inclusion Criteria:\n\n* Female, aged 16 to 45\n* Natural menstrual cycles equal or less than 35 days\n* Off hormonal contraception for more than 3 months\n* Current user of the Premom App\n* Willing to track cervical mucus, LH, PDG, and BBT for 3 full cycles\n* Lives within 50 km of study site in the Ottawa region\n* Able to provide informed consent\n\nExclusion Criteria:\n\n* Pregnant or breastfeeding\n* Current hormonal therapy or contraception\n* Known anovulatory disorders, e.g., Polycystic Ovary Syndrome, hypothalamic amenorrhea.\n* Very irregular or absent cycles\n* Not using the Premom App\n* Unable or unwilling to complete tracking or provide consent","16 Years",{"count":391,"type":22},30,"This study will compare different combinations of fertility signs (cervical mucus (CM), luteinizing hormone \\[LH\\], pregnanediol glucuronide \\[PDG\\], and basal body temperature \\[BBT\\]) to determine which are most reliable for identifying ovulation and luteal phase length. Thirty existing Premom App users will track daily observations for three menstrual cycles. Participants will record mucus, perform urine tests, upload test strip photos to the Premom App, and measure BBT. Both participant readings and AI-assisted app readings will be analyzed. The main goal is to find which marker pairings give the most accurate picture of ovulation timing and luteal phase length. Secondary goals include understanding ease of use, the number of tests required, and whether the app improves accuracy.",[394,395,28,396,397,398,399,400,401],"Fertility","Mobile Applications","Cervical Mucus","Body Temperature","Luteinizing Hormone (LH)","Ovulation","Menstrual Cycle","Progesterone",[399,403,400,394,396,404,405,401,406,407,408,395,409,100,410,411,412,413,414,415],"Luteal Phase","Luteinizing Hormone","Pregnanediol Glucuronide","Basal Body Temperature","Female Reproductive Physiology","Fertility Awareness-Based Methods","Smartphone App","Self-Testing","Home Diagnostic Tests","Observational Study","Prospective Studies","Digital Health","Women's Health","2026-01-10",{"date":418,"type":37},"2026-01-13",{"date":420,"type":22},"2026-01-15",{"date":422,"type":22},"2026-11-15",{"name":424,"class":44},"Bruyère Health Research Institute.",{"id":426,"slug":427,"hasResults":11,"nctId":428,"briefTitle":429,"officialTitle":429,"acronym":430,"eligibilityCriteria":431,"healthyVolunteers":11,"sex":18,"minAge":432,"maxAge":433,"enrollmentInfo":434,"targetDuration":4,"studyType":24,"phases":4,"briefSummary":436,"conditions":437,"keywords":440,"overallStatus":33,"whyStopped":4,"lastUpdateSubmitDate":445,"lastUpdatePostDateStruct":446,"startDateStruct":448,"completionDateStruct":450,"leadSponsor":452,"locationsCount":45},"100599384","ai-ecg-accessory-pathway-localisation-study-100599384","NCT07083791","AI-ECG Accessory Pathway Localisation Study","AAPLS","Inclusion Criteria:\n\n* Referred for EPS procedure as part of their clinical care, with a finding of pre-excitation on their ECG\n* Manifest pre-excitation on their ECG any time prior to their procedure\n* Able to give consent\n* Minimum age 13 years old\n* Maximum age 100 years old\n\nExclusion Criteria:\n\n* Unable to give consent\n* Adults \\> 100 years old\n* Children \\\u003C 13 years old\n* Patients with known location of their accessory pathway from a previous EP study","13 Years","100 Years",{"count":435,"type":22},100,"This study seeks to validate the real-world accuracy of an AI-based algorithm for identifying the location of an accessory pathway from the 12-lead electrocardiogram",[438,28,439],"Accessory Pathway","ECG",[441,442,439,443,444],"AI-ECG","AI","Accessory pathway","Accessory pathway localisation","2025-07-21",{"date":447,"type":37},"2025-07-24",{"date":449,"type":22},"2025-08-01",{"date":451,"type":22},"2027-03-01",{"name":453,"class":44},"Imperial College London",{"id":455,"slug":456,"hasResults":11,"nctId":457,"briefTitle":458,"officialTitle":458,"acronym":4,"eligibilityCriteria":459,"healthyVolunteers":11,"sex":18,"minAge":460,"maxAge":461,"enrollmentInfo":462,"targetDuration":4,"studyType":24,"phases":4,"briefSummary":464,"conditions":465,"keywords":4,"overallStatus":33,"whyStopped":4,"lastUpdateSubmitDate":467,"lastUpdatePostDateStruct":468,"startDateStruct":470,"completionDateStruct":471,"leadSponsor":473,"locationsCount":45},"100597836","artificial-intelligence-model-assisted-accurate-diagnosis-of-early-stage-breast-cancer-100597836","NCT07063667","Artificial Intelligence Model-Assisted Accurate Diagnosis of Early-Stage Breast Cancer","Inclusion Criteria:\n\n* Patients pathologically diagnosed with breast cancer or excluded from breast cancer\n* Available pathological results of breast masses\n* Involving diagnostic population onl\n\nExclusion Criteria:\n\n* Suffering from mental disorders\n* Presence of non-breast diseases during examination\n* Presence of breast implants\n* Undergoing non-breast surgery or having received radiotherapy\u002Fchemotherapy\n* Lactating or pregnant women\n* Missing data","19 Years","85 Years",{"count":463,"type":22},900,"Retrospectively collect the clinical data, breast MRI images, breast ultrasound images and reports, laboratory indicators (such as CA199, CA153, CA125, CEA\u002FAFP), pathological diagnosis results, HE staining images, and existing immunohistochemical results (including CD8A, KPT5, GFRA1, PFKP, ER\u002FPR percentage, Her-2 expression, Ki-67 index, etc.) of patients pathologically confirmed with or excluded from breast cancer in our center between January 2019 and December 2024. For biopsy specimens from patients diagnosed with breast cancer and immunohistochemically confirmed as HR+\u002FHer-2+ during the same period, additional immunohistochemical staining for CD8A, KPT5, GFRA1, and PFKP should be performed, with images and results collected.\n\nThe collected basic clinical information, imaging data, pathological findings, and laboratory metrics of patients will serve as candidate inputs. Units of measurement will be standardized, and missing data will be imputed using the multiple imputation by chained equations algorithm. Data harmonization will employ the Box-Cox algorithm, while min-max scaling will be used for standardization. The adaptive synthetic sampling method with a balance ratio of 0.5 will address data imbalance. For the collected patient data, deep learning will be applied to screen features from the images, combined with clinical significance to identify malignant risk factors. A neural network classifier will be trained on the training set data, with independent variables including breast MRI\u002Fultrasound images, CA199, CA153, CA125, AFP\u002FCEA, etc., and dependent variables including breast cancer status and subtype. Pathological biopsy results will be set as the validation standard.\n\nModel tuning will be conducted on the validation set to construct a breast cancer prediction model. It should be noted that as a single-center study, the results have limited generalizability. The further optimization and evaluation plan for the model involves using breast disease screening data from external centers for validation and refinement, evaluating the model's practical impact on clinical decision-making, and continuously tracking and optimizing its performance.",[466,28],"Breast Cancer, Metastatic","2025-07-02",{"date":469,"type":37},"2025-07-14",{"date":449,"type":22},{"date":472,"type":22},"2026-10-31",{"name":474,"class":44},"Daping Hospital and the Research Institute of Surgery of the Third Military Medical University",{"id":476,"slug":477,"hasResults":11,"nctId":478,"briefTitle":479,"officialTitle":480,"acronym":4,"eligibilityCriteria":481,"healthyVolunteers":11,"sex":18,"minAge":482,"maxAge":483,"enrollmentInfo":484,"targetDuration":4,"studyType":24,"phases":4,"briefSummary":486,"conditions":487,"keywords":490,"overallStatus":33,"whyStopped":4,"lastUpdateSubmitDate":494,"lastUpdatePostDateStruct":495,"startDateStruct":497,"completionDateStruct":499,"leadSponsor":501,"locationsCount":45},"100573704","accuracy-of-detection-of-dental-caries-from-intraoral-images-using-different-artificiai-intelligence-models-100573704","NCT06749743","Accuracy Of Detection Of Dental Caries From Intraoral Images Using Different ArtificiaI Intelligence Models","Accuracy Of Dental Caries Detection From Intraoral Images Using Different Artificial Intelligence Models Versus Conventional Visual Examination Among A Group Of Children: A Diagnostic Accuracy Study","Inclusion Criteria:\n\n* Child dentition having at least one decayed tooth.\n\nExclusion Criteria:\n\n* Child dentition with developmental enamel defects.\n* Children with any systemic medical condition.\n* Parent \u002F child refuse to participate in the study.\n* Uncooperative child.","4 Years","12 Years",{"count":485,"type":22},398,"The goal of this observational study is to evaluate the diagnostic accuracy of different deep learning models in detecting dental caries from intra oral images taken by a professional intra oral camera in children. The main question it aims to answer is:\n\nWhat is the diagnostic accuracy of different deep learning models in detecting dental caries from intra oral images taken by a professional intra oral camera in children compared to the conventional clinical visual examination?",[488,28,489],"Dental Caries (Diagnosis)","Intraoral Images",[62,491,492,493],"dental caries","diagnosis","intraoral images","2025-02-28",{"date":496,"type":37},"2025-03-04",{"date":498,"type":22},"2025-04-30",{"date":500,"type":22},"2025-12-30",{"name":502,"class":44},"Cairo University",{"id":504,"slug":505,"hasResults":11,"nctId":506,"briefTitle":507,"officialTitle":508,"acronym":509,"eligibilityCriteria":510,"healthyVolunteers":17,"sex":18,"minAge":19,"maxAge":4,"enrollmentInfo":511,"targetDuration":4,"studyType":24,"phases":4,"briefSummary":512,"conditions":513,"keywords":516,"overallStatus":104,"whyStopped":4,"lastUpdateSubmitDate":522,"lastUpdatePostDateStruct":523,"startDateStruct":525,"completionDateStruct":527,"leadSponsor":528,"locationsCount":45},"100574920","ai-based-muscular-ultrasound-to-assess-intensive-care-unit-acquired-weakness-100574920","NCT06765551","AI Based Muscular Ultrasound to Assess Intensive Care Unit-acquired Weakness","Artificial IntelligenCe Based UlTrasonographic Assessment of IntensiVe CAre UniT-acquired WEakness (ACTIVATE)","ACTIVATE","Inclusion Criteria:\n\n* Patients aged 18 years or above\n* Major elective surgery, e.g. cardiothoracic or abdominal surgery\n* Expected ICU stay \\>1 day postoperatively\n* Healthy, age-machted subjects without ICUAW (recruited from staff of the department of anesthesiology and intensive care medicine)\n\nExclusion Criteria:\n\n* No informed consent\n* Emergency surgery\n* Previous participation in the same study\n* Preexisting neuromuscular disease\n* Preexisting central nervous system disease with residual neuromuscular impairment (e.g. cerebral haemorrhage, stroke, brain tumor)\n* High-dose glucocorticoid therapy (\\>300 mg hydrocortisone or equivalent per day) before or during particiation in the study",{"count":291,"type":22},"The aim of this observational case-control study is to investigate, whether artificial intelligence can detect ultrasound-derived imaging characteristics typical for intensive care unit-acquired weakness. The main questions it aims to answer are:\n\n1. Is the evaluation of specific parameters of neuromuscular ultrasound using AI-based image analysis suitable for detecting and monitoring critically ill ICU patients with ICUAW?\n2. Do the results of AI-based ultrasound image analysis correlate with:\n\n(A) the severity of ICUAW (B) the visual grading of muscle echogenicity (C) the 30- and 90-day-outcome?",[514,28,515],"Intensive Care Unit-acquired Weakness","Ultrasound",[517,518,519,520,521],"weakness","neuromuscular","critical illness polyneuropathy","critical illness myopathy","neuromuscular impairment","2025-01-03",{"date":524,"type":37},"2025-01-09",{"date":526,"type":37},"2024-10-01",{"date":36,"type":22},{"name":529,"class":44},"Jena University Hospital",{"id":531,"slug":532,"hasResults":11,"nctId":533,"briefTitle":534,"officialTitle":535,"acronym":4,"eligibilityCriteria":536,"healthyVolunteers":17,"sex":18,"minAge":4,"maxAge":4,"enrollmentInfo":537,"targetDuration":539,"studyType":24,"phases":4,"briefSummary":540,"conditions":541,"keywords":544,"overallStatus":33,"whyStopped":4,"lastUpdateSubmitDate":548,"lastUpdatePostDateStruct":549,"startDateStruct":551,"completionDateStruct":553,"leadSponsor":555,"locationsCount":4},"100557797","realistic-in-generation-of-hep-2-cell-images-using-latent-diffusion-models-a-multi-center-visual-turing-test-100557797","NCT06542783","Realistic in Generation of HEp-2 Cell Images Using Latent Diffusion Models: a Multi-center Visual Turing Test","Evaluating the Realism of ANA HEp-2 Cell Images Synthesized Using Latent Diffusion Models: A Multi-center Visual Turing Test","Inclusion Criteria:\n\n* Originating from reputable medical institutions\n* Possessing relevant certification and qualifications\n* Having over one year of experience in interpreting anti-nuclear antibody (ANA) patterns within a laboratory setting\n\nExclusion Criteria:\n\n* Lacking relevant professional certification and qualifications\n* Without experience in interpreting ANA patterns\n* Unwilling to accept the rules and informed consent of the visual Turing test",{"count":538,"type":22},300,"6 Months","The objective of this prospective observational study is to rigorously examine the feasibility and efficacy of utilizing latent diffusion models for data augmentation in anti-nuclear antibody (ANA) Hep-2 cell immunofluorescence images. The main question it aims to answer is:\n\nCan the application of such models potentially enhance the data quality, increase sample diversity, or improve the accuracy and efficiency of subsequent analytical processes (like disease diagnosis and classification) when utilized with ANA-related images?",[542,543,28],"Anti-nuclear Antibody","Visual Turing Tests",[545,546,547],"anti-nuclear antibody","latent diffusion models","Visual Turing tests","2024-08-02",{"date":550,"type":37},"2024-08-07",{"date":552,"type":22},"2024-09",{"date":554,"type":22},"2026-06",{"name":556,"class":44},"Xinhua Hospital, Shanghai Jiao Tong University School of Medicine"]