[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-studies-list:{\"conditionNormalized\":\"ai-artificial-intelligence\",\"overallStatus\":[\"RECRUITING\",\"AVAILABLE\",\"NOT_YET_RECRUITING\"],\"orderBy\":\"LastUpdateSubmitDate:desc\",\"size\":25,\"offset\":0}":3,"health-study-condition:ai-artificial-intelligence":28},{"pageToken":4,"total":5,"offset":6,"count":5,"results":7},null,21,0,[8,48,82,108,138,175,212,233,276,296,313,339,359,395,423,449,479,505,533,554,580],{"id":9,"slug":10,"hasResults":11,"nctId":12,"briefTitle":13,"officialTitle":14,"acronym":15,"eligibilityCriteria":16,"healthyVolunteers":11,"sex":17,"minAge":18,"maxAge":4,"enrollmentInfo":19,"targetDuration":22,"studyType":23,"phases":4,"briefSummary":24,"conditions":25,"keywords":33,"overallStatus":36,"whyStopped":4,"lastUpdateSubmitDate":37,"lastUpdatePostDateStruct":38,"startDateStruct":41,"completionDateStruct":43,"leadSponsor":45,"locationsCount":4},"100644397","feasibility-and-preliminary-performance-of-an-ai-prototype-for-digital-rose-during-ebus-tbna-and-peripheral-tbna-a-prospective-pilot-study-ai-rose-feas-100644397",false,"NCT07662967","Feasibility and Preliminary Performance of an AI Prototype for Digital ROSE During EBUS-TBNA and Peripheral TBNA: a Prospective Pilot Study (AI-ROSE-FEAS)","Preliminary Feasibility and Diagnostic Performance of an Investigational Artificial Intelligence Prototype for Digital Rapid On-Site Evaluation (ROSE) of Cytological Slides During Diagnostic Bronchoscopy With Endobronchial Ultrasound-Guided Transbronchial Needle Aspiration (EBUS-TBNA) and Peripheral Transbronchial Needle Aspiration: a Prospective Monocentric Pilot Study (AI-ROSE-FEAS)","AI-ROSE-FEAS","Inclusion Criteria\n\n* Age ≥ 18 years.\n* Clinical indication for diagnostic bronchoscopy with EBUS-TBNA for mediastinal lymphadenopathy and\u002For TBNA on peripheral lung lesions, according to ACCP\u002FERS-ESTS guidelines and the Unit's diagnostic pathways.\n* The procedure will be performed in two-person mode with IP-ROSE performed as per the center's SOP.\n* Ability to provide written informed consent.\n* Willingness to undergo the required follow-up.\n\nExclusion Criteria\n\n* Absolute contraindications to bronchoscopy.\n* Known histological diagnosis of the target lesion (except re-staging).\n* Concomitant interventional procedures that alter the standard sequence.\n* Inability to provide written informed consent.\n* Any condition that, in the investigator's judgment, compromises patient safety or the reliability of the data.","ALL","18 Years",{"count":20,"type":21},65,"ESTIMATED","6 Months","OBSERVATIONAL","Rapid On-Site Evaluation (ROSE) of cytological slides obtained during EBUS-TBNA improves diagnostic yield by providing real-time adequacy assessment and preliminary diagnostic orientation after each needle pass. In centers without a dedicated cytopathologist, ROSE is performed by a second interventional pulmonologist acting as a dedicated ROSE operator (interventional pulmonologist-performed ROSE, IP-ROSE), a model associated with good but variable diagnostic performance compared to cytopathologist-performed ROSE.\n\nThis study evaluates the feasibility and preliminary diagnostic performance of an investigational artificial intelligence prototype for digital ROSE. The prototype, developed in-house by the Principal Investigator, analyzes microscopic images of Diff-Quik stained cytological slides acquired through a dedicated digital microscope, together with basic clinical data, via API calls to a multimodal AI model. It produces two outputs: sample adequacy (appropriate\u002Fnot appropriate) and malignancy suspicion (benign\u002Fmalignant), each with a confidence score. The AI output is recorded in the study database for research purposes only and is not shown to the operator in real time; it does not influence clinical decisions during the procedure.\n\nThe study is a prospective, monocentric, observational pilot study enrolling 65 adult patients undergoing EBUS-TBNA or peripheral TBNA with IP-ROSE at a single interventional pulmonology unit. The primary statistical unit is the individual ROSE slide, with an expected 130 to 160 evaluable slides. Co-primary endpoints are: (1) technical feasibility of the AI prototype, defined as the proportion of slides with valid AI output within 90 seconds; and (2) AI accuracy for sample adequacy assessment compared to the definitive cytopathological diagnosis, with an expected 95% confidence interval precision of ±5.5%. Secondary endpoints include AI accuracy for malignancy suspicion, agreement between the AI prototype and the IP-ROSE operator, and AI output latency.\n\nThe AI prototype is not a commercially approved or CE-marked medical device. It was developed internally by the Principal Investigator for research purposes and is evaluated exclusively within this study. Data from this pilot study will inform the design of a subsequent confirmatory non-inferiority trial, which will be the subject of separate registration and ethical approval.",[26,27,28,29,30,31,32],"Lung Neoplasms","Bronchoscopy","AI (Artificial Intelligence)","Mediastinal ( Chest) Masses","Mediastinal Lymphadenopathy","EBUS Guided Transbronchial Needle Aspiration","Lung Cancer (Diagnosis)",[34,35],"bronchoscopy","artificial intelligence","NOT_YET_RECRUITING","2026-06-17",{"date":39,"type":40},"2026-06-23","ACTUAL",{"date":42,"type":21},"2026-07-01",{"date":44,"type":21},"2027-03",{"name":46,"class":47},"Azienda Ospedaliera di Rilievo Nazionale A.Cardarelli","OTHER",{"id":49,"slug":50,"hasResults":11,"nctId":51,"briefTitle":52,"officialTitle":53,"acronym":4,"eligibilityCriteria":54,"healthyVolunteers":11,"sex":17,"minAge":55,"maxAge":56,"enrollmentInfo":57,"targetDuration":4,"studyType":59,"phases":60,"briefSummary":62,"conditions":63,"keywords":65,"overallStatus":36,"whyStopped":4,"lastUpdateSubmitDate":72,"lastUpdatePostDateStruct":73,"startDateStruct":75,"completionDateStruct":77,"leadSponsor":79,"locationsCount":81},"100643729","a-feasibility-study-of-ai-assisted-physiotherapy-for-oral-cancer-patients-100643729","NCT07635381","A Feasibility Study of AI-Assisted Physiotherapy for Oral Cancer Patients","A Feasibility Study of AI-Assisted Physiotherapy for Oromandibular and Neck-Shoulder Mobility in Oral Cancer Patients","Inclusion Criteria:\n\n* Oral cancer patients with trismus, clinical signs of neck or shoulder joint impairment after oral cancer surgery or radiotherapy in recent 12 months\n* Age between 20 and 70 years\n\nExclusion Criteria:\n\n* Could not communicate\n* Had any disorder that could influence movement performance (e.g., stroke, Parkinsonism, head injury)","20 Years","70 Years",{"count":58,"type":21},15,"INTERVENTIONAL",[61],"NA","This study aims to evaluate the feasibility, safety, and acceptability of a newly developed artificial intelligence (AI)-assisted physiotherapy system for oromandibular and neck-shoulder range of motion (ROM) in patients who have undergone treatment for oral cancer.\n\nIn this single-group, prospective, non-randomized pilot study, recruited participants will receive 4 to 6 weeks of AI-assisted physiotherapy. Participants will undergo a comprehensive clinical evaluation at baseline and post-intervention. During the intervention period, the AI system will perform a daily automated assessment to dynamically generate and adjust personalized exercise programs. Participants will perform these prescribed programs 4 to 6 times daily. Pre- and post-intervention changes, along with key feasibility parameters, acceptability, and safety metrics, will be statistically analyzed to inform future definitive trials.",[28,64],"Oral Cancer",[66,67,68,69,70,71],"Oral cancer","Artificial intelligence","Maximum interincisal opening","Physiotherapy","Range of motion","Neck and shoulder","2026-06-08",{"date":74,"type":40},"2026-06-09",{"date":76,"type":21},"2026-06-15",{"date":78,"type":21},"2027-12-31",{"name":80,"class":47},"National Taiwan University Hospital",1,{"id":83,"slug":84,"hasResults":11,"nctId":85,"briefTitle":86,"officialTitle":87,"acronym":4,"eligibilityCriteria":88,"healthyVolunteers":89,"sex":17,"minAge":18,"maxAge":4,"enrollmentInfo":90,"targetDuration":4,"studyType":59,"phases":92,"briefSummary":93,"conditions":94,"keywords":98,"overallStatus":99,"whyStopped":4,"lastUpdateSubmitDate":100,"lastUpdatePostDateStruct":101,"startDateStruct":102,"completionDateStruct":104,"leadSponsor":106,"locationsCount":81},"100643565","ai-based-communication-simulation-and-human-library-narratives-100643565","NCT07637461","AI-Based Communication Simulation and Human Library Narratives.","Pediatric Nursing Education Integrating AI-Based Communication Simulation and Human Library Narratives: Evaluating Effects on Students' Communication, Empathy, and Emotion Regulation","Inclusion Criteria:\n\n1. Must be 18 years of age or older\n2. Must have completed required nursing courses including Anatomy, Physical Examination and Assessment (with Laboratory), and Basic Nursing (with Internship)\n3. Must be willing to participate in the research and agree to complete the relevant questionnaire.\n\nExclusion Criteria:\n\n1. Individuals with mental illness or cognitive impairment that may affect their participation in the research or the reliability of the data",true,{"count":91,"type":21},40,[61],"This research plan aims to establish an effectiveness assessment system for promoting clinical communication, empathy, and emotion regulation in pediatric nursing students through a combination of AI communication simulation and human library narrative.\n\nThis study sets five specific objectives:\n\n1. To develop and implement a pediatric nursing teaching model that integrates AI communication simulation and human library narrative.\n2. To evaluate the effectiveness of this teaching model in improving nursing students' clinical communication skills.\n3. To examine the effectiveness of this teaching intervention in enhancing nursing students' empathy.\n4. To explore the impact of this teaching intervention on nursing students' emotion regulation abilities.\n5. To understand students' learning experiences, feelings, changes in emotion regulation, and suggestions for improvement regarding this integrated teaching intervention.",[95,96,97,28],"Nursing Students","Emotion Regulation","Empathy",[95,96,97,28],"RECRUITING","2026-06-04",{"date":74,"type":40},{"date":103,"type":21},"2026-08-01",{"date":105,"type":21},"2027-07-31",{"name":107,"class":47},"National Defense Medical University, Taiwan",{"id":109,"slug":110,"hasResults":11,"nctId":111,"briefTitle":112,"officialTitle":112,"acronym":4,"eligibilityCriteria":113,"healthyVolunteers":11,"sex":17,"minAge":18,"maxAge":4,"enrollmentInfo":114,"targetDuration":4,"studyType":23,"phases":4,"briefSummary":116,"conditions":117,"keywords":121,"overallStatus":36,"whyStopped":4,"lastUpdateSubmitDate":132,"lastUpdatePostDateStruct":133,"startDateStruct":135,"completionDateStruct":136,"leadSponsor":137,"locationsCount":81},"100638268","ai-based-wound-monitoring-automated-wound-progression-assessment-via-marker-free-image-sequence-100638268","NCT07619430","AI-Based Wound Monitoring: Automated Wound Progression Assessment Via Marker-Free Image Sequence","Inclusion Criteria:\n\n* (1) Presence of a hard-to-heal wound that has remained unhealed for more than one month. (2)The wound can be photographed according to the standardized imaging protocol. (3)The participant was aged 18 years or older and provided informed consent personally or via a legally authorized representative, with a signed informed consent form.\n\nExclusion Criteria:\n\n* Wounds whose margins could not be fully included within the imaging field.",{"count":115,"type":21},1000,"This study aims to develop a low-cost, marker-free intelligent wound assessment system that can analyze wound photos taken with a standard smartphone. By comparing wound images over time, the system will generate a quantifiable Wound Progression Index (WPI) to provide objective feedback on whether a wound is improving, stable, or worsening. The long-term goal is to support early detection of wound deterioration and improve wound care in both clinical and home settings.",[118,28,119,120],"Hard-to-heal Wounds","Wound Care","Pressure Injuries",[122,123,124,125,126,127,128,129,130,131],"chronic wounds","wound assessment","medical AI","semantic segmentation","longitudinal image registration","marker-free","wound progression index","home-based care","telemedicine","clinical validation","2026-05-27",{"date":134,"type":40},"2026-06-02",{"date":72,"type":21},{"date":78,"type":21},{"name":80,"class":47},{"id":139,"slug":140,"hasResults":11,"nctId":141,"briefTitle":142,"officialTitle":142,"acronym":143,"eligibilityCriteria":144,"healthyVolunteers":11,"sex":17,"minAge":4,"maxAge":4,"enrollmentInfo":145,"targetDuration":4,"studyType":59,"phases":147,"briefSummary":148,"conditions":149,"keywords":160,"overallStatus":36,"whyStopped":4,"lastUpdateSubmitDate":166,"lastUpdatePostDateStruct":167,"startDateStruct":169,"completionDateStruct":171,"leadSponsor":173,"locationsCount":4},"100640138","development-and-validation-of-a-deep-learning-model-to-predict-endodontic-retreatment-difficulty-from-periapical-radiographs-100640138","NCT07611279","Development and Validation of a Deep Learning Model to Predict Endodontic Retreatment Difficulty From Periapical Radiographs","Ai Retreatment","Inclusion Criteria:\n\nPeriapical radiographs of maxillary and mandibular molars requiring non-surgical endodontic retreatment will be included. Radiographs should exhibit satisfactory image quality, characterized by adequate sharpness, contrast, and minimal distortion or noise to allow accurate assessment of relevant anatomical and treatment-related features. Images should clearly display the tooth of interest, surrounding periapical structures, and any existing root canal filling materials or restorations.\n\nExclusion Criteria:\n\nDeciduous teeth, non-restorable, non-treated teeth",{"count":146,"type":21},123,[61],"The aim of this study is to develop and evaluate an artificial intelligence-based model capable of analyzing periapical radiographs of maxillary and mandibular molars to predict the difficulty level of non-surgical root canal retreatment. By integrating deep learning techniques with routinely acquired periapical radiographs, this study aims to enhance diagnostic support, improve clinical decision-making, and facilitate appropriate case selection or referral in endodontic practice.",[150,151,152,28,153,154,155,156,157,158,159],"Endodontic Retreatment","Non-surgical Retreatment","Endodontics","Deep Learning Model","DIFFICULTY ASSESSMENT","SEPARATED INSTRUMENT","Perforation","Missed Canals","Poor Obturation","Obturation Quality",[161,162,163,164,35,165],"endodontic retreatment","difficulty assessment","endodontics","ai","deep learning model","2026-05-20",{"date":168,"type":40},"2026-05-28",{"date":170,"type":21},"2026-07",{"date":172,"type":21},"2027-01",{"name":174,"class":47},"Cairo University",{"id":176,"slug":177,"hasResults":11,"nctId":178,"briefTitle":179,"officialTitle":180,"acronym":181,"eligibilityCriteria":182,"healthyVolunteers":89,"sex":17,"minAge":18,"maxAge":4,"enrollmentInfo":183,"targetDuration":185,"studyType":23,"phases":4,"briefSummary":186,"conditions":187,"keywords":194,"overallStatus":99,"whyStopped":4,"lastUpdateSubmitDate":203,"lastUpdatePostDateStruct":204,"startDateStruct":206,"completionDateStruct":208,"leadSponsor":210,"locationsCount":81},"100637404","ai-assisted-ct-for-risk-stratification-in-coronary-artery-disease-action-100637404","NCT07577609","AI-assisted CT for Risk Stratification in Coronary Artery Disease (ACTION)","Artificial Intelligence-assisted CT for Risk Stratification in COronary Artery Disease to PreveNt Future Coronary Events and Improve Outcomes","ACTION","Inclusion Criteria:\n\n* Adults aged ≥18 years\n* Undergoing clinically indicated cardiac CT\n* Able and willing to provide informed consent\n\nExclusion Criteria:\n\n* History of malignancy\n* Abnormal renal function (e.g., eGFR or serum creatinine outside reference range)\n* Contraindications to iodinated contrast agents or CT imaging",{"count":184,"type":21},10000,"5 Years","The goal of this observational study is to learn if AI-assisted cardiac CT imaging can improve cardiovascular risk stratification and prediction of future coronary events in an adult population undergoing clinically indicated cardiac CT.\n\nThe main questions it aims to answer are:\n\n* Can AI-enhanced cardiac CT accurately assess cardiovascular risk in a real-world adult population?\n* How do CT-derived plaque characteristics correlate with clinical, biochemical, and lifestyle risk factors? Researchers will compare subgroups (e.g., patients with different risk profiles, biomarkers, or imaging findings, and a subset undergoing OCT imaging) to see if differences in imaging and clinical parameters are associated with cardiovascular risk and plaque vulnerability.\n\nParticipants will:\n\n* Provide informed consent and medical history\u002Fdemographic information\n* Undergo blood sampling for cardiovascular and metabolic biomarkers\n* Have a resting ECG performed\n* Complete a detailed lifestyle and health questionnaire\n* Receive a non-invasive cardiac CT scan interpreted by an expert\n* Potentially receive heart rate-lowering medication (e.g., metoprolol) if required for imaging quality\n* Be referred for further clinical evaluation if clinically indicated ",[188,189,190,28,191,192,193],"Coronary Artery Disease (CAD)","Computed Tomography Angiography","Biomarkers","Lipoprotein(a)","Atheroscleroses, Coronary","Myocardial Ischemia",[195,196,197,198,199,200,201,202],"AI-assisted cardiac CT","Coronary CT angiography (CCTA)","Cardiovascular risk stratification","Coronary plaque characterization","High-risk plaque","Machine learning","CT-FFR (fractional flow reserve)","Coronary calcium scoring","2026-05-05",{"date":205,"type":40},"2026-05-11",{"date":207,"type":40},"2022-12-21",{"date":209,"type":21},"2032-12-21",{"name":211,"class":47},"University of Galway",{"id":213,"slug":214,"hasResults":11,"nctId":215,"briefTitle":216,"officialTitle":217,"acronym":4,"eligibilityCriteria":218,"healthyVolunteers":11,"sex":17,"minAge":18,"maxAge":4,"enrollmentInfo":219,"targetDuration":4,"studyType":59,"phases":221,"briefSummary":222,"conditions":223,"keywords":4,"overallStatus":36,"whyStopped":4,"lastUpdateSubmitDate":203,"lastUpdatePostDateStruct":225,"startDateStruct":227,"completionDateStruct":229,"leadSponsor":231,"locationsCount":4},"100639921","ai-in-assessing-aesthetic-outcomes-in-rhinoplasty-100639921","NCT07580573","AI in Assessing Aesthetic Outcomes in Rhinoplasty","Use of Artificial Intelligence in Assessment of Aesthetic Outcomes in Rhinoplasty","Inclusion Criteria:\n\n* Patients age\\> 18 years old.\n* patients schedule for rhinoplasty surgery\n\nExclusion Criteria:\n\n* Pervious nasal trauma that affect anatomical land mark\n* pervious nasal surgery (rhinoplasty or others)\n* patients with psychological disorders.\n* patients with any coagulopathy disorders",{"count":220,"type":21},20,[61],"This study aims to thoroughly assess the predictive accuracy of artificial intelligence-based nasal outcome simulations by comparing AI-generated preoperative predictions with objective postoperative nasal morphology using digital image analysis.\n\nTo assess accuracy of AI-image measurement compared with imageJ software",[224,28],"Rhinoplasty",{"date":226,"type":40},"2026-05-12",{"date":228,"type":21},"2026-06-01",{"date":230,"type":21},"2027-07-01",{"name":232,"class":47},"Assiut University",{"id":234,"slug":235,"hasResults":11,"nctId":236,"briefTitle":237,"officialTitle":238,"acronym":239,"eligibilityCriteria":240,"healthyVolunteers":11,"sex":17,"minAge":241,"maxAge":22,"enrollmentInfo":242,"targetDuration":244,"studyType":23,"phases":4,"briefSummary":245,"conditions":246,"keywords":249,"overallStatus":99,"whyStopped":4,"lastUpdateSubmitDate":266,"lastUpdatePostDateStruct":267,"startDateStruct":269,"completionDateStruct":271,"leadSponsor":273,"locationsCount":81},"100632476","improving-artificial-intelligence-derived-algorithms-for-estimating-length-and-weight-in-neonates-and-infants-up-to-6-months-of-age-nest-100632476","NCT07514182","Improving Artificial Intelligence-derived Algorithms for Estimating Length and Weight in NEonateS and infanTs up to 6 Months of Age (NEST)","Improving Artificial Intelligence-derived Algorithms for Estimating Length and Weight in NEonateS and infanTs up to 6 Months of Age","NEST","Inclusion Criteria:\n\n1. Infants up from birth up to 6 months of postnatal age (including neonates) who have been admitted to the NICU or SCN at the time of screening\n2. Parent(s) should be able to comprehend the content of the study and be willing for their child to undergo video and photo recording, and to allow access to their blood sampling results (haemoglobin) taken as part of standard clinical practice\n3. Written consent from parents and\u002For legally acceptable representative\n\nExclusion Criteria:\n\n1. Infants who were born with gestational age of less than 28 weeks of gestational age\n2. Infants who are intubated (i.e., endotracheal, nasotracheal intubation) at the time of screening\n3. The investigator considers for any reason that the participant would not be suitable for the study\n4. The participant has an existing medical condition that would prevent standardised measurement of length and\u002For head circumference e.g. structural abnormality of the lower limbs, orthopaedic conditions, hydrocephalus\n5. Employees and\u002For children\u002Ffamily members or relatives of employees of Danone Global Research \\& Innovation Center, Danone Asia Pacific Holdings Pte Ltd, or the participating site","0 Days",{"count":243,"type":21},60,"2 Weeks","The NEST study is a prospective, observational research study designed to collect clinical measurements and image data to develop and evaluate artificial intelligence (AI)-derived algorithms for estimating anthropometric parameters in neonates and young infants. The study focuses on infants from birth up to 6 months of age and aims to assess the accuracy of AI-based estimations of length, weight, and head circumference using photographs and\u002For video recordings captured during routine clinical care. These AI-derived measurements will be compared against standard clinical measurements obtained by trained healthcare professionals in neonatal and infant care settings.",[247,248,28],"Growth","Neonates",[248,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265],"Infants","Neonatal Growth","Infant Growth assessment","Anthropometric measurements","Infant length measurements","Infant weight measurement","Head Circumference","Artificial Intelligence","Image based assessment","Digital Health","Photographs and video recordings","Neonatal intensive care unit","Special Care nursery","Observational study","Proof of concept study","Growth monitoring","2026-04-08",{"date":268,"type":40},"2026-04-13",{"date":270,"type":40},"2026-03-26",{"date":272,"type":21},"2026-10",{"name":274,"class":275},"Danone Asia Pacific Holdings Pte, Ltd.","INDUSTRY",{"id":277,"slug":278,"hasResults":11,"nctId":279,"briefTitle":280,"officialTitle":280,"acronym":4,"eligibilityCriteria":281,"healthyVolunteers":89,"sex":17,"minAge":4,"maxAge":4,"enrollmentInfo":282,"targetDuration":4,"studyType":23,"phases":4,"briefSummary":284,"conditions":285,"keywords":289,"overallStatus":99,"whyStopped":4,"lastUpdateSubmitDate":266,"lastUpdatePostDateStruct":290,"startDateStruct":291,"completionDateStruct":293,"leadSponsor":294,"locationsCount":81},"100599663","ai-driven-multimodal-imaging-integration-for-diagnosis-and-prognostication-of-digestive-system-diseases-100599663","NCT07087418","AI-Driven Multimodal Imaging Integration for Diagnosis and Prognostication of Digestive System Diseases","Inclusion Criteria:\n\n* Patients with multimodal-confirmed diagnoses (clinical, imaging, endoscopic, and pathological) of:\n\n  * Inflammatory bowel disease (IBD; Crohn's disease or ulcerative colitis)\n  * Intestinal tuberculosis\n  * Behçet's disease\n* Availability of ≥1 technically adequate CT or MR scan with high-quality colonoscopy performed within ±1 month of imaging.\n\nExclusion Criteria:\n\n* ・Suboptimal imaging quality (e.g., low-dose artifacts, metal artifacts)\n\n  * Inadequate bowel preparation for endoscopy\n  * Incomplete examinations due to poor tolerance",{"count":283,"type":21},5000,"The goal of this observational, retrospective and prospective study is to develop a noninvasive disease assessment system by leveraging artificial intelligence (AI) to comprehensively analyze multi-modal imaging features, including magnetic resonance enterography (MRE) and computed tomography enterography (CTE), for the diagnosis and prognostication of digestive diseases. To this end, the investigators retrospectively enrolled imaging, endoscopic, and clinical data from 21 centers across China to construct and iteratively optimize the AI model. The model's performance will be prospectively validated in two centers, and its accuracy in lesion localization will be verified through real-world deployment in endoscopy suites.",[286,287,28,288],"Digestive Diseases","Radiology","Imaging",[287,288,286,257],{"date":268,"type":40},{"date":292,"type":40},"2025-07-01",{"date":103,"type":21},{"name":295,"class":47},"First Affiliated Hospital, Sun Yat-Sen University",{"id":297,"slug":298,"hasResults":11,"nctId":299,"briefTitle":300,"officialTitle":301,"acronym":4,"eligibilityCriteria":302,"healthyVolunteers":89,"sex":17,"minAge":55,"maxAge":56,"enrollmentInfo":303,"targetDuration":4,"studyType":23,"phases":4,"briefSummary":304,"conditions":305,"keywords":306,"overallStatus":99,"whyStopped":4,"lastUpdateSubmitDate":307,"lastUpdatePostDateStruct":308,"startDateStruct":309,"completionDateStruct":311,"leadSponsor":312,"locationsCount":81},"100632125","ai-based-physiotherapy-evaluation-system-for-range-of-motion-in-oral-cancer-patients-100632125","NCT07509619","AI-based Physiotherapy Evaluation System for Range of Motion in Oral Cancer Patients","Validity and Reliability of an AI-based Physiotherapy Evaluation System for Oromandibular and Neck-Shoulder Range of Motion in Oral Cancer Patients","Inclusion Criteria:\n\n* Healthy adults aged 20 to 70 years\n* No trismus\n* No history of head, neck, or shoulder injury or surgery\n* No history of head and neck cancer-related radiotherapy or chemotherapy\n\nExclusion Criteria:\n\n* Inability to communicate or follow instructions\n* Any condition that may affect movement performance",{"count":220,"type":21},"This study aims to evaluate the validity and reliability of a novel AI-based physiotherapy evaluation system for measuring oromandibular and neck-shoulder range of motion (ROM). Traditional ROM assessments rely on manual measurements, which may be influenced by rater experience and variability. The proposed AI system uses automated keypoint tracking to provide objective and standardized measurements.\n\nIn this cross-sectional study, healthy adult participants will perform standardized ROM tasks. Measurements obtained from the AI system will be compared with those from two independent raters using conventional clinical tools. Repeated measurements will be conducted to assess intra-rater and inter-rater reliability. The agreement between the AI system and human raters will be evaluated to determine the system's clinical applicability.",[64,28],[66,67,68,69,70,71],"2026-04-06",{"date":268,"type":40},{"date":310,"type":40},"2026-04-07",{"date":78,"type":21},{"name":80,"class":47},{"id":314,"slug":315,"hasResults":11,"nctId":316,"briefTitle":317,"officialTitle":318,"acronym":319,"eligibilityCriteria":320,"healthyVolunteers":11,"sex":17,"minAge":18,"maxAge":4,"enrollmentInfo":321,"targetDuration":4,"studyType":59,"phases":322,"briefSummary":323,"conditions":324,"keywords":328,"overallStatus":36,"whyStopped":4,"lastUpdateSubmitDate":332,"lastUpdatePostDateStruct":333,"startDateStruct":335,"completionDateStruct":336,"leadSponsor":337,"locationsCount":81},"100632831","advanced-symptom-palliation-through-integrated-relief-engagement-100632831","NCT07518797","Advanced Symptom Palliation Through Integrated Relief Engagement","Palliative Oncology Symptoms Management Utilizing Beacon© Platform's Analysis of Aggregated Patient-Provided Data - Feasibility Prospective Study","ASPIRE AI","Inclusion Criteria:\n\n* Outpatients treated and followed at the Davidoff Center\n* Stage III-IV cancer\n* Eastern Cooperative Oncology Group (ECOG) 0-3\n* Age: 18 and older\n* Have not been engaged with the ambulatory palliative service over the last three months\n* Speaks and reads a language supported by the study protocol (e.g., Hebrew, Arabic, English)\n* Able to wear a smartwatch\u002Fsensor for prolonged periods and independently operate the app\n* Actively symptomatic patients requiring palliative care (not long-term stable survivors)\n* Enrolled in another clinical study if the existing study allows\n\nExclusion Criteria:\n\n* Patient without active disease\n* Patients are actively engaged with the ambulatory palliative service in the - Davidoff Center or elsewhere\n* Patient with chronic stable symptoms (\\>6 months) related to their cancer or treatment\n* ECOG \\> 3\n* Does not speak and read languages supported by the study protocol (e.g., Hebrew, Arabic, English)\n* Legal incompetence\n* Does not or cannot use a smartphone, or is unable to fill out questionnaires\u002Frecord messages, or is unable to wear a smartwatch\u002Fsensor, or operate the app independently",{"count":91,"type":21},[61],"Beacon is a digital platform that processes objective and subjective aggregated data provided by patients. Objective data is provided by standard wearables, while subjective data is provided by patient-reported outcome measures (PROMs), comprising written and vocal patient reporting.\n\nThe ASPIRE.AI study is a prospective study evaluating the feasibility of clinicians' use of aggregated data that was provided by patients and analyzed through \"Beacon\", and its influence on advanced cancer patients' palliative symptoms management.\n\nApproximately 40 consecutive eligible ambulatory advanced cancer patients first attending the palliative unit in the Davidoff Center will be enrolled. The trial will continue for \\~1 year, with each patient participating in this trial for a total of about 12 weeks.\n\nAll participants will receive the intervention. The intervention comprises the palliative standard of care treatment along with the usage of the Beacon digital platform, which enables comprehensive data collection and aggregation regarding the patient's biopsychosocial status, and thus, the patient's symptom burden.\n\nData collected and aggregated through Beacon includes Beacon data provided by the patients via wearables (smartwatch\u002Fsensors), smartphones, and written and recorded PROMs.\n\nResearchers will then evaluate physician engagement with the platform, Influence on treatment, and the physician user experience rating as well as patients' adherence, satisfaction with Beacon usage, and changes in patients' symptom burden and quality of life.",[325,326,327,28],"Cancer","Symptom","Quality of Life",[329,330,331],"Palliative care","Biopsychosocial","Physician engagement","2026-04-02",{"date":334,"type":40},"2026-04-09",{"date":228,"type":21},{"date":230,"type":21},{"name":338,"class":47},"Tzeela Cohen",{"id":340,"slug":341,"hasResults":11,"nctId":342,"briefTitle":343,"officialTitle":344,"acronym":4,"eligibilityCriteria":345,"healthyVolunteers":89,"sex":17,"minAge":18,"maxAge":346,"enrollmentInfo":347,"targetDuration":4,"studyType":23,"phases":4,"briefSummary":348,"conditions":349,"keywords":4,"overallStatus":99,"whyStopped":4,"lastUpdateSubmitDate":350,"lastUpdatePostDateStruct":351,"startDateStruct":353,"completionDateStruct":355,"leadSponsor":357,"locationsCount":81},"100631836","scientific-validity-assessment-and-optimization-of-ai-generated-a3a4-type-questions-for-the-chinese-medical-licensing-examination-100631836","NCT07505862","Scientific Validity Assessment and Optimization of AI-Generated A3\u002FA4 Type Questions for the Chinese Medical Licensing Examination","Scientific Validity Assessment and Optimization of AI-Generated A3\u002FA4 Type Questions for the Chinese Medical Licensing Examination: An Empirical Analysis Based on the \"Answering-Generating\" Closed Loop and Transformation of Competency Assessment","Inclusion Criteria:\n\n* 1.Professional Status: Medical students currently enrolled in a Standardized Residency Training (SRT) program.\n\n  2.Educational Background: Holders of a Bachelor of Medicine degree or higher, with foundational clinical knowledge.\n\n  3.Informed Consent: Voluntarily participate in the study and provide written informed consent.\n\n  4.Technical Competency: Proficient in using digital platforms to complete assessments and scoring.\n\nExclusion Criteria:\n\n* 1.Conflict of Interest: Individuals involved in the AI model training, prompt engineering, or the creation of the human-authored question bank for this study.\n\n  2.Inability to Complete: Presence of visual\u002Fauditory impairments or severe illness that precludes completion of the assessment within the specified time.\n\n  3.Investigator's Discretion: Any other condition that, in the opinion of the investigator, renders the participant unsuitable for the study.","60 Years",{"count":220,"type":21},"This is a cross-sectional study that primarily employs quantitative analysis, supplemented by qualitative assessment. The research is conducted in two stages: Phase I consists of a model performance comparison experiment, and Phase II involves an item quality evaluation experiment. The entire study adheres to the principles of single-blinding, randomization, and standardization to ensure scientific rigor and reproducibility.\n\nThe single-blind design is implemented during the \"standardized testing\" phase, where the system intersperses AI-generated items with those authored by human experts. Participants remain blinded to the source of each item (AI-generated vs. human-authored) throughout the testing and scoring processes, thereby ensuring the objectivity of the evaluation results.",[28],"2026-03-29",{"date":352,"type":40},"2026-04-01",{"date":354,"type":40},"2025-10-01",{"date":356,"type":21},"2026-03-30",{"name":358,"class":47},"Guangdong Provincial People's Hospital",{"id":360,"slug":361,"hasResults":11,"nctId":362,"briefTitle":363,"officialTitle":364,"acronym":4,"eligibilityCriteria":365,"healthyVolunteers":11,"sex":17,"minAge":366,"maxAge":4,"enrollmentInfo":367,"targetDuration":4,"studyType":59,"phases":369,"briefSummary":370,"conditions":371,"keywords":378,"overallStatus":36,"whyStopped":4,"lastUpdateSubmitDate":386,"lastUpdatePostDateStruct":387,"startDateStruct":389,"completionDateStruct":391,"leadSponsor":393,"locationsCount":4},"100628632","evaluation-of-dora-care-for-supporting-fracture-liaison-services-fls-100628632","NCT07464171","Evaluation of Dora Care for Supporting Fracture Liaison Services (FLS)","A Prospective, Mixed-Methods Feasibility Study to Evaluate the Agreement, Safety, and Acceptability of 'Dora Care', an Automated AI Voice Assistant, for Supporting Clinical Pathways in a Fracture Liaison Service.","Inclusion Criteria:\n\n* Access to and ability to use a telephone for automated conversations as part of a routine, FLS phone pathway. This includes patients who are:\n* Adults aged ≥50 years.\n* Presenting with a new fragility fracture requiring FLS assessment.\n* Able to provide informed consent.\n* English speaking.\n* Reside in the UK.\n\nExclusion Criteria:\n\n* Severe cognitive impairment precluding meaningful telephone interaction or informed consent.\n* Patients already established on long-term osteoporosis treatment.\n* Terminal illness with a life expectancy \\\u003C12 months.\n* Current participation in another clinical trial altering standard of care thus altering the care schedule.","50 Years",{"count":368,"type":21},217,[61],"What is the study about? This study is testing \"Dora\", an AI-powered assistant that can make phone calls to patients, for use in the Fracture Liaison Service (FLS). The FLS is a clinic that helps prevent more bone fractures after an initial \"fragility fracture\" (a break that happens easily, usually due to osteoporosis).\n\nWhy is this being done? FLS clinicians often have to spend a lot of time on routine phone calls for assessments and follow-ups. If Dora can safely and accurately collect patient information, it might save time for staff and still give patients a good experience.\n\nWhat will happen to patients in the study? Invitation and consent - Patients with a new fragility fracture who are eligible will be invited to take part after informed consent.\n\nDora call - Patients will receive an automated phone call from Dora, at the start of their FLS pathway and at follow-up.\n\nAt intake, Dora will ask about risk factors for bone problems (e.g., smoking, alcohol use, family fracture history).\n\nAt follow-up, Dora will ask about medication use, side effects, falls, or new fractures.\n\nClinician call - Soon after, patients will have their usual phone appointment with an FLS clinician, who asks similar questions.\n\nSurveys\u002Finterviews - Patients will be asked to complete a short questionnaire and take part in an optional interview to say how they felt about talking to Dora.\n\nWhat about clinicians? Clinicians involved in the FLS pathway will be asked to complete a short survey and to take part in an optional interview to understand how useful Dora's reports might be in their work.\n\nWho can take part? Patients - Age 50+, English-speaking, with a new fragility fracture, and able to use the phone.\n\nClinicians - Those working in FLS or similar bone health services. How long will it take? Each patient might be involved for up to about 7 months. The whole study will take about a year.",[28,372,373,374,375,376,377],"Osteoporosis","Outpatient","Telemedicine","Automation","Risk Assesment","Bone Health",[28,379,380,381,382,383,374,377,384,385],"Fracture Liason Services","Conversational AI","Triage","Rheumatology","Telephone Consultation","Voice AI","Medical Device","2026-03-05",{"date":388,"type":40},"2026-03-11",{"date":390,"type":21},"2026-02",{"date":392,"type":21},"2027-12",{"name":394,"class":275},"Ufonia",{"id":396,"slug":397,"hasResults":11,"nctId":398,"briefTitle":399,"officialTitle":399,"acronym":4,"eligibilityCriteria":400,"healthyVolunteers":11,"sex":401,"minAge":4,"maxAge":4,"enrollmentInfo":402,"targetDuration":185,"studyType":23,"phases":4,"briefSummary":404,"conditions":405,"keywords":411,"overallStatus":36,"whyStopped":4,"lastUpdateSubmitDate":414,"lastUpdatePostDateStruct":415,"startDateStruct":417,"completionDateStruct":419,"leadSponsor":421,"locationsCount":4},"100613664","a-prospective-validation-study-of-radiomics-in-the-differential-diagnosis-of-uterine-leiomyoma-and-uterine-sarcoma-100613664","NCT07269535","A Prospective Validation Study of Radiomics in the Differential Diagnosis of Uterine Leiomyoma and Uterine Sarcoma","1. Inclusion Criteria:\n\n   1.1 Patients clinically evaluated and radiologically examined (including MRI, particularly T2WI and DWI sequences) who are diagnosed with uterine leiomyoma or considered highly suspected of uterine sarcoma, in combination with preliminary pathological findings.\n\n   1.2 Patients scheduled for surgical treatment or those eligible for long-term standardized follow-up.\n\n   1.3 Patients who are able to understand the study procedures and voluntarily sign the written informed consent form.\n2. Exclusion Criteria:\n\n2.1 Patients with severe organic diseases or a previous confirmed diagnosis of other malignant uterine tumors.\n\n2.2 Patients unable to complete baseline examinations, unable to comply with long-term follow-up, or unwilling to provide written informed consent.","FEMALE",{"count":403,"type":21},500,"In our previous study, based on the multi-center clinical big data collected from January 2012 to January 2025, we have completed the construction of a multimodal early warning model for the malignant transformation of uterine fibroids. The model was mainly based on T2WI and DWI sequences, and was trained and optimized by support vector machine (SVM) algorithm. In the retrospective study and internal validation, the model shows high sensitivity and specificity, which preliminarily proves that it has good application potential in identifying high-risk groups and predicting the risk of malignant transformation of uterine fibroids.\n\nHowever, there are still some limitations in retrospective studies and internal validation results, and its application value, universality and stability in real clinical environment have not been fully verified. Therefore, we plan to conduct a prospective validation study in consecutive patients enrolled after January 2025 to evaluate the clinical performance and generalization of the model in predicting the malignant tendency or risk of malignant transformation of uterine fibroids through practical application in the real population, and further analyze the operability in the actual diagnosis and treatment process and the potential value for patient management. This study will provide reliable evidence for early screening, follow-up management and individualized treatment of high-risk population, and has important clinical and public health significance for improving the early diagnosis rate, reducing the risk of malignant transformation and improving the prognosis of patients with uterine fibroids.",[406,407,28,408,409,410],"Uterine Fibroid","Uterine Sarcoma","Radiomic","Prospective Observational Study","MRI",[412,406,413,28,408,410],"prospective observational study","uterine sarcoma","2025-11-26",{"date":416,"type":40},"2025-12-08",{"date":418,"type":21},"2025-11-30",{"date":420,"type":21},"2050-01-01",{"name":422,"class":47},"Tongji Hospital",{"id":424,"slug":425,"hasResults":11,"nctId":426,"briefTitle":427,"officialTitle":428,"acronym":4,"eligibilityCriteria":429,"healthyVolunteers":89,"sex":17,"minAge":4,"maxAge":4,"enrollmentInfo":430,"targetDuration":4,"studyType":59,"phases":432,"briefSummary":433,"conditions":434,"keywords":4,"overallStatus":36,"whyStopped":4,"lastUpdateSubmitDate":414,"lastUpdatePostDateStruct":441,"startDateStruct":443,"completionDateStruct":445,"leadSponsor":447,"locationsCount":81},"100612437","the-effect-of-artificial-intelligence-supported-intramuscular-and-subcutaneous-injection-training-on-nursing-students-100612437","NCT07253571","The Effect of Artificial Intelligence-Supported Intramuscular and Subcutaneous Injection Training on Nursing Students","The Effect of Artificial Intelligence-Supported Intramuscular and Subcutaneous Injection Training on Knowledge, Reasoning, and Skill Development in Nursing Students: A Quasi-Experimental Study","Inclusion Criteria:\n\n* Being a first-year nursing student,\n* Taking the Fundamentals of Nursing-I course for the first time,\n* Encountering the topic of intramuscular and subcutaneous injections for the first time,\n* Agreeing to participate in the research voluntarily and willingly,\n* Having a smart device and internet access to use the chatbot application regularly,\n* Committing to participate in the research process at least 80% of the time (ensuring at least 2 uses per week for 4 weeks)\n\nExclusion Criteria:\n\n* Having previously taken the Fundamentals of Nursing-I course or having received formal training in injection techniques\n* Being unable to participate in theoretical or practical training during the research process\n* Being unable to continue interactions with the chatbot for technical reasons\n* Incomplete research data or being unable to complete the pre-test\u002Fpost-test process .",{"count":431,"type":21},80,[61],"The complexity of healthcare services and technological advances today have necessitated the adoption of innovative approaches in nursing education. Among these innovative approaches, artificial intelligence (AI) has established itself as a technology that is increasingly present in nursing education processes, offering a supportive, personalized, and interactive learning experience. AI's contributions to nursing students' acquisition of fundamental competencies such as clinical decision-making, skill development, and critical thinking are rapidly increasing. Especially in high-risk, invasive, and clinically skill-intensive applications, AI-supported educational models both enhance learning quality and support patient safety. Intramuscular and subcutaneous injections are among the basic invasive skills that nursing students must learn. These applications require a high level of cognitive and psychomotor competence from students. Incorrect injection practices can lead to complications such as drug absorption problems, nerve damage, hematoma, or infection, making it critically important to teach these skills correctly and safely. In this context, AI-supported education systems stand out as an effective tool for teaching injection skills. Artificial intelligence-based chatbots provide students with both theoretical knowledge and practical guidance. For example, before injecting a muscle group, a student can learn about the anatomy of the muscle, determine the correct angle, and remember precautions against potential complications through the chatbot. Artificial intelligence also reinforces the learning process by instantly answering students' questions, preventing the acquisition of incorrect information. Recent studies emphasize that AI-supported learning tools positively influence students' attitudes toward learning, increasing their motivation and academic satisfaction levels. Accordingly, the integration of AI-based technologies in the process of training future nurses is no longer an option but a necessity. Particularly in complex and delicate skills such as intramuscular and subcutaneous injections, AI-supported chatbots can facilitate student learning, increase skill accuracy, and support clinical safety. Therefore, it is crucial for nursing education programs to combine artificial intelligence technologies with pedagogical foundations to provide student-centered, safe, and effective learning environments.",[435,436,28,437,438,439,440],"AI Chatbot","Nursing Education","Nursing Skills","IM Injection","OSCE (Objective Structured Clinical Examination)","Clinical Reasoning",{"date":442,"type":40},"2025-12-03",{"date":444,"type":21},"2025-12-15",{"date":446,"type":21},"2026-10-15",{"name":448,"class":47},"Akdeniz University",{"id":450,"slug":451,"hasResults":11,"nctId":452,"briefTitle":453,"officialTitle":454,"acronym":455,"eligibilityCriteria":456,"healthyVolunteers":11,"sex":401,"minAge":457,"maxAge":56,"enrollmentInfo":458,"targetDuration":4,"studyType":59,"phases":460,"briefSummary":461,"conditions":462,"keywords":465,"overallStatus":36,"whyStopped":4,"lastUpdateSubmitDate":471,"lastUpdatePostDateStruct":472,"startDateStruct":474,"completionDateStruct":475,"leadSponsor":477,"locationsCount":4},"100608724","ai-based-self-supervised-learning-model-using-non-contrast-breast-mri-for-early-screening-and-clinical-utility-evaluation-100608724","NCT07205276","AI-Based Self-Supervised Learning Model Using Non-Contrast Breast MRI for Early Screening and Clinical Utility Evaluation","Construction of an Early Breast Cancer Screening Warning Model Based on Self-supervised Learning With Plain MRI Scans and Prospective Clinical Utility Evaluation","B-MRI-AI","* Inclusion Criteria:\n\n  1. Female, age 30-70 years\n  2. Completed breast MRI scan, including at least T2WI, DWI, and ADC sequences\n  3. Multimodal data acquired within the same time window (≤90 days)\n  4. A clear clinical outcome: pathologically confirmed or ≥12-24 months of negative follow-up\n  5. The time window between imaging examination and outcome determination was ≤90 days\n  6. Signed informed consent\n* Exclusion Criteria:\n\n  1. Absolute contraindications to MRI (pacemaker, cochlear implant, ocular metal foreign body, etc.)\n  2. Pregnant or lactating women\n  3. Recent history of breast surgery\u002Fradiotherapy (≤6 months) or imaging after neoadjuvant therapy\n  4. Substandard image quality (severe motion artifact, signal-to-noise ratio below threshold)\n  5. Incomplete clinical data or time window exceeded\n  6. Known breast cancer metastasis or recurrence","30 Years",{"count":459,"type":21},30000,[61],"Breast cancer is the most common malignant disease among women worldwide, with rising incidence and younger age at onset in China. Early detection is critical for improving survival, yet current screening methods such as mammography and ultrasound show limited sensitivity in Chinese women, particularly those with dense breast tissue. Contrast-enhanced MRI offers higher diagnostic performance but its use is limited by high costs, safety concerns with gadolinium-based contrast agents, and limited accessibility.\n\nThis investigator-initiated trial aims to evaluate the clinical application of non-contrast multiparametric MRI, combined with advanced artificial intelligence algorithms, for the early detection and diagnosis of breast cancer. The study will collect MRI imaging data from multiple centers and integrate radiomic features across T2-weighted imaging, diffusion-weighted imaging, and apparent diffusion coefficient maps. A deep learning-based model will be developed and validated to improve lesion detection, differential diagnosis, and risk stratification.\n\nThe ultimate goal of this project is to establish a safe, accurate, and scalable breast cancer screening pathway suitable for Chinese women. By reducing dependence on invasive procedures and contrast agents, and by leveraging AI for standardization and efficiency, this approach may significantly improve early detection rates and contribute to better patient outcomes.",[463,464,28],"Breast Cancer Detection","Early Detection of Cancer",[466,467,468,469,470],"Breast MRI","Non-contrast MRI","Radiomics","Deep Learning","Breast Cancer Screening","2025-09-25",{"date":473,"type":40},"2025-10-03",{"date":354,"type":21},{"date":476,"type":21},"2027-12-01",{"name":478,"class":47},"Second Affiliated Hospital, School of Medicine, Zhejiang University",{"id":480,"slug":481,"hasResults":11,"nctId":482,"briefTitle":483,"officialTitle":484,"acronym":4,"eligibilityCriteria":485,"healthyVolunteers":11,"sex":17,"minAge":18,"maxAge":486,"enrollmentInfo":487,"targetDuration":4,"studyType":23,"phases":4,"briefSummary":489,"conditions":490,"keywords":493,"overallStatus":99,"whyStopped":4,"lastUpdateSubmitDate":497,"lastUpdatePostDateStruct":498,"startDateStruct":500,"completionDateStruct":501,"leadSponsor":503,"locationsCount":81},"100604224","predictive-performance-of-a-generative-model-for-corneal-tomography-after-icl-implantation-100604224","NCT07146737","Predictive Performance of a Generative Model for Corneal Tomography After ICL Implantation","Predictive Performance of a Generative Model for Corneal Tomography After Implantable Collamer Lens Implantation","Inclusion Criteria:(1) stable myopia (≤0.50D\u002Fyear change for 2 years), (2) ACD ≥2.80mm, (3) intact corneal endothelium (≥2000 cells\u002Fmm²), and (4) no confounding ocular\u002Fsystemic conditions.\n\nExclusion Criteria:(1) glaucoma-spectrum disorders or retinal vasculopathies, (2) prior corneal\u002Fintraocular surgery, (3) compromised corneal endothelium, (4) uncontrolled systemic diseases, and (5) pregnancy\u002Flactation.","45 Years",{"count":488,"type":21},818,"To evaluate the efficacy of a corneal tomography Imaging model in predicting postoperative vault based on preoperative corneal topography in Implantable Collamer Lens (ICL) surgery.",[491,492,469,28],"ICL","Vault",[494,495,496,257,469],"Implantable Collamer Lens (ICL) implantation surgery","Corneal tomography","vault","2025-08-21",{"date":499,"type":40},"2025-08-28",{"date":292,"type":40},{"date":502,"type":21},"2028-12-31",{"name":504,"class":47},"Second Affiliated Hospital of Nanchang University",{"id":506,"slug":507,"hasResults":11,"nctId":508,"briefTitle":509,"officialTitle":510,"acronym":511,"eligibilityCriteria":512,"healthyVolunteers":89,"sex":17,"minAge":18,"maxAge":4,"enrollmentInfo":513,"targetDuration":4,"studyType":59,"phases":515,"briefSummary":516,"conditions":517,"keywords":521,"overallStatus":36,"whyStopped":4,"lastUpdateSubmitDate":524,"lastUpdatePostDateStruct":525,"startDateStruct":527,"completionDateStruct":529,"leadSponsor":531,"locationsCount":4},"100602523","stepped-wedge-cluster-randomized-trial-of-ai-assisted-cta-detection-for-intracranial-aneurysms-in-regional-hospitals-100602523","NCT07124624","Stepped-Wedge Cluster Randomized Trial of AI-Assisted CTA Detection for Intracranial Aneurysms in Regional Hospitals","Impact of an AI-Driven CT Angiography Model on Intracranial Aneurysm Detection and Clinical Outcomes in Regional Hospitals (IDEAL2): A Nationwide Stepped-Wedge Cluster-Randomized Trial","IDEAL2","Inclusion Criteria:\n\n-Patients in the outpatient setting who are scheduled to undergo head CTA scanning\n\nExclusion Criteria:\n\n* Age \\\u003C 18 years\n* History of cerebrovascular surgery involving any metallic implants (e.g., aneurysm embolization, aneurysm clipping, or vascular stenting)\n* Modified Rankin Scale (mRS) score \\> 3\n* Refuse to sign written informed consent\n* Contraindications to CTA examination\n* CTA scan failure, incomplete imaging data, or image quality insufficient for diagnostic evaluation",{"count":514,"type":21},14400,[61],"This study (IDEAL 2) is a nationwide stepped-wedge cluster-randomized trial designed to prospectively enroll over 14,400 patients undergoing outpatient head CT angiography (CTA). The trial will be conducted across more than 72 regional hospitals in China. Clusters were randomly assigned to nine randomization groups. In accordance with the stepped-wedge design, clusters will sequentially transition from the control condition (standard human diagnosis) to the intervention condition (AI-assisted diagnosis) at regular intervals over a 10-month period, until all clusters receive the intervention. The primary outcome is the detection rate of intracranial aneurysms. Secondary outcomes include patient prognosis and clinical outcomes.",[518,519,28,520],"Intracranial Aneurysm","CT Angiography","Cluster Randomized Trial",[522,28,523],"Intracranial aneurysm","Stepped-wedge cluster-randomized trial","2025-08-15",{"date":526,"type":40},"2025-08-20",{"date":528,"type":21},"2025-10-09",{"date":530,"type":21},"2029-08-31",{"name":532,"class":47},"Jinling Hospital, China",{"id":534,"slug":535,"hasResults":11,"nctId":536,"briefTitle":537,"officialTitle":538,"acronym":4,"eligibilityCriteria":539,"healthyVolunteers":11,"sex":17,"minAge":18,"maxAge":4,"enrollmentInfo":540,"targetDuration":4,"studyType":23,"phases":4,"briefSummary":542,"conditions":543,"keywords":4,"overallStatus":36,"whyStopped":4,"lastUpdateSubmitDate":545,"lastUpdatePostDateStruct":546,"startDateStruct":548,"completionDateStruct":550,"leadSponsor":552,"locationsCount":4},"100601172","multimodal-large-model-driven-risk-and-prognosis-assessment-for-brain-metastases-in-lung-cancer-100601172","NCT07107035","Multimodal Large Model-Driven Risk and Prognosis Assessment for Brain Metastases in Lung Cancer","Multimodal Large Model-Driven Risk and Prognosis Assessment for Brain Metastases in Lung Cancer: A Nationwide, Multicenter, Observational Study","Inclusion Criteria:\n\n* Age≥18 years old;\n* KPS score≥70;\n* Pathologically confirmed lung cancer;\n* Receiving guideline-concordant standard-of-care therapy, defined as: radical surgical resection for early- to mid-stage non-small cell lung cancer (NSCLC); stereotactic radiotherapy for early-stage NSCLC deemed medically inoperable; radical chemoradiotherapy for locally advanced NSCLC; or systemic therapy for advanced-stage NSCLC.\n* Complete systemic imaging before treatment initiation, including contrast-enhanced brain MRI and contrast-enhanced chest CT;\n* Informed consent of the patient.\n\nExclusion Criteria:\n\n* Multiple primary or metastatic tumors (except early skin cancer, cervical carcinoma in situ that has been treated radically, with no recurrence or progression for more than 5 years);\n* Uncontrolled epilepsy, central nervous system disease, or history of mental disorders, judged by the researcher to potentially interfere with the signing of the informed consent form or affect patient compliance;\n* Physical examination findings, clinical laboratory abnormalities, or other uncontrolled medical conditions identified by the investigator as potentially interfering with study results interpretation or increasing the patient's risk of treatment complications\n* Pregnant or lactating women.",{"count":541,"type":21},20000,"The goal of this nationwide, multicenter observational study is to develop and externally validate multimodal large models that can (1) predict the risk of brain metastases and (2) estimate long-term prognosis in patients with non-small cell lung cancer (NSCLC).\n\nThe main questions it aims to answer are:\n\n* Can a multimodal large model that fuses imaging, pathology, genomic, and clinical data accurately identify NSCLC patients at high risk of developing brain metastases?\n* Can a multimodal large model reliably forecast intracranial progression-free survival, progression-free survival, and overall survival across diverse real-world treatment settings? (ie, patients receiving distinct treatment regimens, in different treatment lines and with or without intracranial local therapies).\n\nBecause this is an observational study, there are no investigational treatments; instead, researchers will compare outcomes among patients who receive standard-of-care therapies (surgery, radiotherapy, systemic therapy) to determine how well the model's predictions align with observed events.\n\nParticipants will:\n\n* Allow use of their routinely collected clinical information, imaging (chest CT, brain MRI), pathology slides, and molecular test results for model training and validation\n* Undergo standard-of-care follow-ups\n* Complete optional quality-of-life questionnaires during scheduled visits",[28,544],"NSCLC Brain Metastasis","2025-07-30",{"date":547,"type":40},"2025-08-06",{"date":549,"type":21},"2025-09",{"date":551,"type":21},"2030-07",{"name":553,"class":47},"Fudan University",{"id":555,"slug":556,"hasResults":11,"nctId":557,"briefTitle":558,"officialTitle":559,"acronym":560,"eligibilityCriteria":561,"healthyVolunteers":11,"sex":17,"minAge":562,"maxAge":4,"enrollmentInfo":563,"targetDuration":4,"studyType":23,"phases":4,"briefSummary":565,"conditions":566,"keywords":4,"overallStatus":99,"whyStopped":4,"lastUpdateSubmitDate":570,"lastUpdatePostDateStruct":571,"startDateStruct":573,"completionDateStruct":575,"leadSponsor":577,"locationsCount":579},"100469205","dolce-determining-the-impact-of-optellums-lung-cancer-prediction-solution-100469205","NCT05389774","DOLCE: Determining the Impact of Optellum's Lung Cancer Prediction Solution","DOLCE: Determining the Impact of Optellum's Lung Cancer Prediction (LCP) Artificial Intelligence Solution on Service Utilisation, Health Economics and Patient Outcomes","DOLCE","Inclusion Criteria:\n\nPatients are eligible for the study if all of the following apply:\n\n* Are aged 35 years or above\n* Have baseline CT study with at least one incidentally detected solid or part-solid (must have a solid component \\>=80%) pulmonary nodule that:\n\n  * is not fully calcified\n  * Is 5-30mm inclusive in maximum axial diameter for the whole lesion measured using manual electronic callipers\n* Have baseline CT study that includes at least one series that meets all of the following (training for this will be provided):\n\n  * Is of a type that meets VNC instructions for use\n  * Comprises at least one full-inspiration breath-hold scans without a high degree of contrast media and does not exhibit quality issues (e.g., motion artefacts)\n\nExclusion Criteria:\n\nPatients will be excluded from the study if any of the following apply:\n\n* Have received a diagnosis for cancer in the last 5 years\n* Have thoracic implants that impact the image appearance of the nodule\n* Have more than five reported pulmonary nodules of any size or type excluding fully calcified nodules (this criterion is used as a proxy due to the risk of being an infection or metastasis)\n* Have one or more additional nodules where any of the following applies:\n\n  * Are already undergoing follow-up according to pulmonary nodule management standard care\n  * Pure ground glass opacity (GGO) of \\>=5mm in maximum axial diameter for the whole lesion measured using manual electronic callipers\n  * \\>30mm in maximum axial diameter for the whole lesion measured using manual electronic callipers","35 Years",{"count":564,"type":21},2000,"This study is a multi-centre prospective observational cohort study recruiting patients with 5-30mm solid and part-solid pulmonary nodules that have been detected on CT chest scans performed as part of routine practice. The aim is to determine whether physician decision making with the AI-based LCP tool, generates clinical and health-economic benefits over the current standard of care of these patients.",[28,567,568,569],"Pulmonary Nodule, Solitary","Pulmonary Nodule, Multiple","Lung Cancer","2025-04-04",{"date":572,"type":40},"2025-04-08",{"date":574,"type":40},"2023-03-23",{"date":576,"type":21},"2025-08",{"name":578,"class":47},"Nottingham University Hospitals NHS Trust",10,{"id":581,"slug":582,"hasResults":11,"nctId":583,"briefTitle":584,"officialTitle":585,"acronym":4,"eligibilityCriteria":586,"healthyVolunteers":11,"sex":17,"minAge":18,"maxAge":4,"enrollmentInfo":587,"targetDuration":4,"studyType":59,"phases":589,"briefSummary":590,"conditions":591,"keywords":594,"overallStatus":36,"whyStopped":4,"lastUpdateSubmitDate":596,"lastUpdatePostDateStruct":597,"startDateStruct":599,"completionDateStruct":601,"leadSponsor":603,"locationsCount":4},"100578920","ai-driven-personalized-exercise-feedback-program-on-exercise-adherence-in-traumatic-brain-injury-100578920","NCT06817564","AI-driven Personalized Exercise Feedback Program on Exercise Adherence in Traumatic Brain Injury","Effects of an AI-driven Personalized Exercise Feedback Program on Exercise Adherence and Health Outcomes in Patients with Traumatic Brain Injury","Inclusion Criteria:\n\n* Eligible participants are patients aged over 18 with mild TBI (GCS 13-15)\n* who can walk independently,\n* reside in the Greater Taipei area,\n* and possess sufficient Chinese or Taiwanese language proficiency to understand the trial\n* complete self-administered questionnaires.\n\nExclusion Criteria:\n\n* Exclusion criteria include individuals with severe medical conditions (e.g., respiratory failure, epilepsy, psychiatric disorders), musculoskeletal or neurological impairments\n* hindering physical activity in the 6-minute walk test,\n* cognitive impairments (MMSE \\\u003C 24),\n* frontal lobe injuries or penetrating injury causing significant psychological dysfunction.\n* Patients regularly engaging in moderate-to-high-intensity aerobic exercise or participating in other studies will also be excluded to avoid bias.",{"count":588,"type":21},125,[61],"This study aims to develop and evaluate an AI-driven Personalized Exercise Feedback Program (AI-PEF) to enhance exercise adherence and health outcomes in mTBI patients.\n\nMethods: AI-PEF integrates the transtheoretical model and self-determination theory with machine learning algorithms to provide real-time, personalized feedback. A phased randomized controlled trial will be conducted: Phase I evaluates feasibility and acceptability through Delphi methods with expert consensus and patient feedback; Phase II validates preliminary outcomes with 30 participants in a 2-arm randomized trial; and Phase III assesses the program's impact on adherence, sleep quality, depressive symptoms, and quality of life with 90 participants in a 3-arm randomized trial.",[592,593,28,259],"Traumatic Brain Injury","Exercise",[595],"AI-Driven Personalized Exercise Program","2025-02-08",{"date":598,"type":40},"2025-02-12",{"date":600,"type":21},"2025-03-01",{"date":602,"type":21},"2031-08-31",{"name":604,"class":47},"National Defense Medical Center, Taiwan"]