[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-studies-list:{\"overallStatus\":[\"RECRUITING\",\"AVAILABLE\",\"NOT_YET_RECRUITING\"],\"leadSponsorName\":\"The Eye Hospital of Wenzhou Medical University\",\"orderBy\":\"LastUpdateSubmitDate:desc\",\"size\":25,\"offset\":0}":3,"health-study-condition:":239},{"pageToken":4,"total":5,"offset":6,"count":5,"results":7},null,10,0,[8,44,72,92,107,129,151,173,193,215],{"id":9,"slug":10,"hasResults":11,"nctId":12,"briefTitle":13,"officialTitle":14,"acronym":4,"eligibilityCriteria":15,"healthyVolunteers":16,"sex":17,"minAge":4,"maxAge":4,"enrollmentInfo":18,"targetDuration":4,"studyType":21,"phases":22,"briefSummary":24,"conditions":25,"keywords":27,"overallStatus":31,"whyStopped":4,"lastUpdateSubmitDate":32,"lastUpdatePostDateStruct":33,"startDateStruct":36,"completionDateStruct":38,"leadSponsor":40,"locationsCount":43},"100574113","ophthalmic-ai-assisted-medical-decision-making-100574113",false,"NCT06755060","Ophthalmic AI-Assisted Medical Decision-Making","A Study on Ophthalmic Multimodal AI-Assisted Medical Decision-Making Based on Imaging and Electronic Medical Record Data","Inclusion Criteria:\n\n1. Age Criteria: No age restrictions apply for inclusion in the study.\n2. Ophthalmic Disease Diagnosis: Eligible patients must have a diagnosis of one or more ophthalmic conditions, with the diagnosis confirmed by a qualified ophthalmologist.\n3. Imaging and Clinical Data Requirements: Patients must be able to provide complete ophthalmic imaging data and electronic medical records (EMR) that are comprehensive and accessible for the purposes of the study.\n4. Informed Consent: All patients, or their legal representatives in the case of minors or individuals unable to provide informed consent, must sign a consent form that clearly outlines the study's objectives, procedures, potential risks and discomforts, data usage, and the rights and responsibilities of participants. In the case of minors or those unable to consent, informed consent must be obtained from the patient's legal guardian.\n5. Treatment Adherence: Participants must demonstrate the ability to understand and adhere to the study's requirements, including compliance with follow-up visits, examination schedules, and treatment recommendations. Patients must agree to participate in regular assessments and data collection, including imaging exams, laboratory tests, and follow-up evaluations as required by the study protocol.\n6. Clinical Physician Assessment: The attending physician must determine that the patient meets all inclusion criteria and has the capacity to comply with the necessary treatment, diagnostic tests, and follow-up protocols throughout the study duration.\n\nExclusion Criteria:\n\n1. Acute or Severe Ocular Diseases: Patients with acute ocular conditions requiring immediate medical intervention, which necessitate exclusion from interventional studies due to the urgency of their treatment.\n2. Serious Systemic Diseases: Patients with serious systemic illnesses that may interfere with the treatment of ocular diseases, impact the effectiveness of the intervention, or complicate the interpretation of study outcomes.\n3. Prior Exposure to Study Interventions: Patients who have previously undergone the intervention being studied or participated in other experimental treatments within ongoing clinical trials, as this may introduce bias or confound the study results.\n4. Incomplete Imaging or Clinical Data: Patients who are unable to provide complete or adequate ophthalmic imaging data or lack a comprehensive electronic medical record (EMR), which are essential for the integrity of the study data.\n5. Pregnancy or Lactation: Pregnant or breastfeeding women, for whom there may be potential risks associated with ocular treatment or imaging procedures. Such cases will be evaluated on an individual basis to ensure patient safety.\n6. Mental Health or Cognitive Impairment: Patients diagnosed with significant mental health disorders or cognitive impairments that prevent them from fully understanding the nature and risks of the study, or from complying with the treatment regimen and follow-up procedures.\n7. Drug Allergies or Severe Reactions: Patients with known allergies or severe adverse reactions to any medications or ophthalmic treatments likely to be used during the study, which could pose a health risk to the patient.\n8. Current Participation in Other Clinical Trials: Patients who are concurrently involved in other interventional clinical trials (especially those related to ophthalmology), as this may lead to conflicting treatments or interfere with the assessment of the study's outcomes.\n9. Inability to Comply with Follow-up Requirements: Patients who, due to logistical, health-related, or personal factors, are unable to comply with the required follow-up visits, treatment regimens, or data collection, which are essential for the study's longitudinal analysis.\n10. Other Clinical Exclusions: Patients whose participation, based on the clinical judgment of the treating physician, may not be in their best interest due to their health condition or other factors, or who may experience adverse outcomes from participating in the study.",true,"ALL",{"count":19,"type":20},100000,"ESTIMATED","INTERVENTIONAL",[23],"NA","This is a multi-center, prospective clinical study designed to evaluate the application and effectiveness of an AI-assisted medical decision support system, leveraging multimodal data fusion, in ophthalmic clinical practice.",[26],"Ocular Diseases",[28,29,30],"ocular diseases","Ophthalmic Multimodal AI-Assisted Medical Decision-Making","Artificial Intelligence","RECRUITING","2025-08-15",{"date":34,"type":35},"2025-08-20","ACTUAL",{"date":37,"type":35},"2024-12-01",{"date":39,"type":20},"2026-12-30",{"name":41,"class":42},"The Eye Hospital of Wenzhou Medical University","OTHER",5,{"id":45,"slug":46,"hasResults":11,"nctId":47,"briefTitle":48,"officialTitle":49,"acronym":4,"eligibilityCriteria":50,"healthyVolunteers":16,"sex":17,"minAge":51,"maxAge":52,"enrollmentInfo":53,"targetDuration":4,"studyType":55,"phases":4,"briefSummary":56,"conditions":57,"keywords":59,"overallStatus":31,"whyStopped":4,"lastUpdateSubmitDate":63,"lastUpdatePostDateStruct":64,"startDateStruct":66,"completionDateStruct":68,"leadSponsor":70,"locationsCount":71},"100576913","ai-driven-cancer-diagnosis-and-prediction-with-ehr-100576913","NCT06791473","AI-Driven Cancer Diagnosis and Prediction With EHR","AI-Based Cancer Diagnosis and Prediction Using Electronic Health Records","Inclusion Criteria:\n\n1、Patients with comprehensive electronic health records (EHRs), including medical history, laboratory test results, imaging data, and genetic data (if available).\n\n2\\. Individuals without severe cognitive impairments or conditions that would prevent them from providing informed consent or participating in the study.\n\n3\\. Parents or guardians must provide informed consent for minors, while adult participants must provide informed consent for themselves.\n\nExclusion Criteria:\n\n1. Patients with incomplete or missing key electronic health record data or insufficient follow-up data.\n2. Individuals with severe cognitive disorders or other terminal illnesses that would prevent meaningful participation.\n3. Pregnant women (although pediatric cancers are being considered, pregnant women would be excluded for safety reasons).","0 Years","90 Years",{"count":54,"type":20},1000000,"OBSERVATIONAL","This is a multi-center, clinical study designed to evaluate the application and effectiveness of an AI-assisted predictive model for identifying and diagnosing cancer, leveraging multimodal health data.",[58],"Tumor",[60,61,62],"tumor","Early Disease Prediction","AI-Assisted Diagnosis","2025-07-25",{"date":65,"type":35},"2025-07-30",{"date":67,"type":35},"2025-01-19",{"date":69,"type":20},"2025-10-01",{"name":41,"class":42},7,{"id":73,"slug":74,"hasResults":11,"nctId":75,"briefTitle":76,"officialTitle":77,"acronym":4,"eligibilityCriteria":78,"healthyVolunteers":16,"sex":17,"minAge":51,"maxAge":52,"enrollmentInfo":79,"targetDuration":4,"studyType":55,"phases":4,"briefSummary":80,"conditions":81,"keywords":4,"overallStatus":31,"whyStopped":4,"lastUpdateSubmitDate":83,"lastUpdatePostDateStruct":84,"startDateStruct":86,"completionDateStruct":88,"leadSponsor":90,"locationsCount":91},"100576906","ai-driven-prediction-of-hospital-acquired-infections-with-ehr-100576906","NCT06791382","AI-Driven Prediction of Hospital-Acquired Infections With EHR","Predicting Hospital-Acquired Infections Using Electronic Health Records: An AI-Assisted Approach","Inclusion Criteria:\n\n1. Patients with complete and accessible EHR data, including medical history, laboratory test results, treatment regimens, clinical observations, and infection history.\n2. Patients who have been admitted to the participating hospital or healthcare facility during the study period.\n3. All participants must provide informed consent to use their health data for research purposes.\n\nExclusion Criteria:\n\n1. Patients with incomplete or missing critical EHR data, such as lab results, medical history, or treatment details, which are necessary for infection prediction.\n2. Patients who have severe cognitive disorders, dementia, or conditions that prevent them from providing informed consent or participating in the study.\n3. Patients who have not been admitted to the hospital during the study period or who are receiving outpatient care only.\n4. Patients with terminal conditions where infection prediction may not be applicable to the clinical goals of the study.",{"count":54,"type":20},"This is a multi-center, clinical study designed to evaluate the application and effectiveness of an AI-assisted predictive model for identifying and diagnosing infection, leveraging multimodal health data.",[82],"Hospital-acquired Infections","2025-04-16",{"date":85,"type":35},"2025-04-17",{"date":87,"type":35},"2023-02-01",{"date":89,"type":20},"2025-05",{"name":41,"class":42},2,{"id":93,"slug":94,"hasResults":11,"nctId":95,"briefTitle":29,"officialTitle":14,"acronym":4,"eligibilityCriteria":96,"healthyVolunteers":11,"sex":17,"minAge":4,"maxAge":4,"enrollmentInfo":97,"targetDuration":4,"studyType":55,"phases":4,"briefSummary":99,"conditions":100,"keywords":101,"overallStatus":31,"whyStopped":4,"lastUpdateSubmitDate":83,"lastUpdatePostDateStruct":102,"startDateStruct":103,"completionDateStruct":105,"leadSponsor":106,"locationsCount":43},"100574123","ophthalmic-multimodal-ai-assisted-medical-decision-making-100574123","NCT06755190","Inclusion Criteria:\n\n1.All patients who have received treatment at multiple centers, including The Eye Hospital of Wenzhou Medical University, First Affiliated Hospital of Wenzhou Medical University, Second Affiliated Hospital of Wenzhou Medical University, ZhuHai Hospital, and Macau University of Science and Technology Hospital.\n\n2.Availability of comprehensive electronic health records (EHR), including: Ophthalmic images (e.g., fundus photography, OCT, or slit-lamp images). Electronic medical records (e.g., diagnosis, treatment, and follow-up notes). Examination results (e.g., visual acuity, intraocular pressure, or laboratory tests). 3.Patients with a clear and confirmed diagnosis of one or more ocular diseases. 4.Patients with sufficient follow-up records to allow assessment of disease progression or prognosis, if applicable.\n\n1. All ophthalmology patients who have previously received treatment at the Department of Ophthalmology, the Eye Hospital of Wenzhou Medical University, First Affiliated Hospital of Wenzhou Medical University, Second Affiliated Hospital of Wenzhou Medical University, Zhuhai People's Hospital, and the University Hospital.\n2. Availability of comprehensive electronic health records (EHR), including: Ophthalmic images (e.g., fundus photography, OCT, or slit-lamp images). Electronic medical records (e.g., diagnosis, treatment, and follow-up notes). Examination results (e.g., visual acuity, intraocular pressure, or laboratory tests).\n3. Patients with a clear and confirmed diagnosis of one or more ocular diseases.\n4. Patients with sufficient follow-up records to allow assessment of disease progression or prognosis, if applicable.\n\nExclusion Criteria:\n\n1. Incomplete or missing critical EHR components.\n2. Cases with ambiguous or unverified diagnoses that cannot be clearly categorized.\n3. Duplicated or redundant data from the same patient.",{"count":98,"type":20},5000000,"This is a multi-center, retrospective clinical study designed to evaluate the application and effectiveness of an AI-assisted medical decision support system, leveraging multimodal data fusion, in ophthalmic clinical practice.",[26],[28,29,30],{"date":85,"type":35},{"date":104,"type":35},"2024-12-20",{"date":89,"type":20},{"name":41,"class":42},{"id":108,"slug":109,"hasResults":11,"nctId":110,"briefTitle":111,"officialTitle":112,"acronym":4,"eligibilityCriteria":113,"healthyVolunteers":11,"sex":17,"minAge":114,"maxAge":115,"enrollmentInfo":116,"targetDuration":4,"studyType":55,"phases":4,"briefSummary":117,"conditions":118,"keywords":120,"overallStatus":31,"whyStopped":4,"lastUpdateSubmitDate":83,"lastUpdatePostDateStruct":122,"startDateStruct":123,"completionDateStruct":125,"leadSponsor":127,"locationsCount":128},"100576911","ai-driven-prediction-of-dialysis-outcome-with-ehr-100576911","NCT06791447","AI-Driven Prediction of Dialysis Outcome With EHR","Predicting Clinical Outcomes in Dialysis Patients Using Electronic Health Records: An AI-Based Approach","Inclusion Criteria:\n\n1. Patients who have been undergoing dialysis (either hemodialysis or peritoneal dialysis) for at least 3 months.\n2. Complete and accessible EHR data, including medical history, laboratory test results, dialysis treatment details, and clinical observations.\n3. Participants must provide informed consent for the use of their health data for research purposes.\n\nExclusion Criteria:\n\n1. Patients with incomplete or missing critical EHR data, including medical history, laboratory results, dialysis data, or treatment details necessary for the study.\n2. Patients who have been on dialysis for less than 3 months, to ensure stable data for outcome prediction.","20 Years","100 Years",{"count":54,"type":20},"This is a multi-center, clinical study designed to evaluate the application and effectiveness of an AI-assisted predictive model for outcome of dialysis patients, leveraging multimodal health data.",[119],"Dialysis Patients",[119,121],"AI-Assisted Prediction",{"date":85,"type":35},{"date":124,"type":35},"2023-01-01",{"date":126,"type":20},"2025-05-01",{"name":41,"class":42},1,{"id":130,"slug":131,"hasResults":11,"nctId":132,"briefTitle":133,"officialTitle":134,"acronym":4,"eligibilityCriteria":135,"healthyVolunteers":16,"sex":17,"minAge":4,"maxAge":4,"enrollmentInfo":136,"targetDuration":4,"studyType":55,"phases":4,"briefSummary":137,"conditions":138,"keywords":140,"overallStatus":31,"whyStopped":4,"lastUpdateSubmitDate":83,"lastUpdatePostDateStruct":144,"startDateStruct":145,"completionDateStruct":147,"leadSponsor":149,"locationsCount":150},"100576909","ai-driven-genotype-prediction-using-ehr-and-multimodal-data-100576909","NCT06791421","AI-Driven Genotype Prediction Using EHR and Multimodal Data","Predicting Patient Genotypes Using Electronic Health Records and Multimodal Data Through AI-Based Models","Inclusion Criteria:\n\n1. Participants must have comprehensive electronic health records (EHR), including medical history, lab results, and relevant imaging data (e.g., X-rays, MRIs, CT scans).\n2. Participants must have existing genetic testing data available for comparison, if applicable.\n3. Participants must be willing to provide consent for the use of their health data in the study.\n4. Participants must have no active intervention related to genetic testing or prediction during the study period.\n5. Participants should have complete and verifiable health data to allow for accurate prediction by the AI model.\n\nExclusion Criteria:\n\n1. Participants without available EHR, lab results, or imaging data.\n2. Participants with ambiguous, inaccurate, or unverifiable genetic testing results that cannot be used for comparison.\n3. Patients with significant discrepancies or missing data that would prevent the AI model from making accurate predictions.",{"count":19,"type":20},"The goal of this clinical study is to explore the potential of using electronic health records (EHR) and multimodal data (such as imaging, lab results, and clinical history) to predict a patient's genotype. The study will evaluate whether predictive models based on this non-genetic data can accurately infer genetic information, which traditionally requires direct genetic testing.",[139],"Genotype",[141,142,139,143],"AI","AI-prediction","Multimodal data",{"date":85,"type":35},{"date":146,"type":35},"2023-07-01",{"date":148,"type":20},"2025-06",{"name":41,"class":42},4,{"id":152,"slug":153,"hasResults":11,"nctId":154,"briefTitle":155,"officialTitle":156,"acronym":157,"eligibilityCriteria":158,"healthyVolunteers":16,"sex":17,"minAge":159,"maxAge":160,"enrollmentInfo":161,"targetDuration":4,"studyType":55,"phases":4,"briefSummary":162,"conditions":163,"keywords":165,"overallStatus":31,"whyStopped":4,"lastUpdateSubmitDate":83,"lastUpdatePostDateStruct":167,"startDateStruct":168,"completionDateStruct":170,"leadSponsor":171,"locationsCount":172},"100576903","early-diagnosis-and-prediction-of-maternal-and-neonatal-diseases-100576903","NCT06791343","Early Diagnosis and Prediction of Maternal and Neonatal Diseases:","Early Prediction and Diagnosis of Maternal and Neonatal Diseases Using Multimodal Health Data","EDPMND","Inclusion Criteria:\n\n1. Pregnant women aged 18 to 45 years.\n2. Women who have received prenatal care at participating centers (e.g., hospitals or clinics).\n3. Availability of comprehensive electronic health records, including prenatal care data, laboratory results, and imaging records.\n4. Willingness to provide consent for participation in the study and the use of historical health data for analysis.\n\nExclusion Criteria:\n\n1. Women under 18 or over 45 years old.\n2. Participants with insufficient follow-up data or missing critical clinical information required for predictive modeling.","18 Years","45 Years",{"count":54,"type":20},"This is a multi-center, clinical study designed to evaluate the application and effectiveness of an AI-assisted predictive model for identifying maternal and neonatal diseases, leveraging multimodal health data.",[164],"Pregnancy-Related and Neonatal Disorders",[166,61,62],"Maternal and Neonatal Health",{"date":85,"type":35},{"date":169,"type":35},"2023-08-01",{"date":89,"type":20},{"name":41,"class":42},3,{"id":174,"slug":175,"hasResults":11,"nctId":176,"briefTitle":177,"officialTitle":178,"acronym":4,"eligibilityCriteria":179,"healthyVolunteers":16,"sex":17,"minAge":4,"maxAge":4,"enrollmentInfo":180,"targetDuration":4,"studyType":55,"phases":4,"briefSummary":182,"conditions":183,"keywords":185,"overallStatus":31,"whyStopped":4,"lastUpdateSubmitDate":83,"lastUpdatePostDateStruct":187,"startDateStruct":188,"completionDateStruct":189,"leadSponsor":191,"locationsCount":192},"100576915","ai-agent-for-automated-diagnosis-and-predicting-using-ehr-and-multimodal-data-100576915","NCT06791499","AI-Agent for Automated Diagnosis and Predicting Using EHR and Multimodal Data","AI-Agent Assisted Automation for Diagnosing and Predicting Patients Using Electronic Health Records and Multimodal Data","Inclusion Criteria:\n\n1. Participants must have comprehensive electronic health records (EHR) available, including demographic information, medical history, and laboratory results.\n2. Participants must have available multimodal imaging data (e.g., X-rays, CT scans, MRIs, ultrasounds) relevant to their health condition.\n3. Participants must have a confirmed diagnosis of one or more diseases or health conditions based on clinical records or imaging data.\n4. Patients must provide consent for the use of their historical health data for research purposes.\n\nExclusion Criteria:\n\n1. Participants with ambiguous or unverifiable diagnoses that cannot be accurately categorized.\n2. Duplicate or redundant patient data (e.g., repeated records of the same patient without clear differentiation).",{"count":181,"type":20},2000000,"The goal of this clinical study is to evaluate the effectiveness of an AI agent in diagnosing and predicting diseases using electronic health records (EHR) and multimodal imaging data. The AI agent leverages advanced machine learning algorithms to process and analyze diverse health data sources, aiming to assist healthcare providers in making more accurate diagnoses and predictions.",[184],"AI Agent",[186],"AI agent",{"date":85,"type":35},{"date":146,"type":35},{"date":190,"type":20},"2025-07",{"name":41,"class":42},6,{"id":194,"slug":195,"hasResults":11,"nctId":196,"briefTitle":197,"officialTitle":198,"acronym":4,"eligibilityCriteria":199,"healthyVolunteers":16,"sex":17,"minAge":51,"maxAge":115,"enrollmentInfo":200,"targetDuration":4,"studyType":55,"phases":4,"briefSummary":201,"conditions":202,"keywords":204,"overallStatus":31,"whyStopped":4,"lastUpdateSubmitDate":208,"lastUpdatePostDateStruct":209,"startDateStruct":211,"completionDateStruct":213,"leadSponsor":214,"locationsCount":150},"100576914","ai-driven-prediction-of-biological-age-with-ehr-100576914","NCT06791486","AI-Driven Prediction of Biological Age With EHR","Predicting Biological Age Using Electronic Health Records: An AI-Based Approach","Inclusion Criteria:\n\n1. Patients with comprehensive and accessible EHR data, including medical history, laboratory results, treatment data, imaging data (if available), and lifestyle factors (e.g., smoking, physical activity, diet).\n2. Patients with no significant cognitive impairments that would prevent them from providing informed consent or participating in the study.\n3. All participants must provide informed consent for the use of their medical data for research purposes.\n\nExclusion Criteria:\n\n1. Patients with incomplete or missing critical EHR data such as medical history, laboratory results, or treatment data that are necessary for predicting biological age.\n2. atients with severe cognitive disorders (e.g., dementia, significant mental disabilities) who are unable to provide informed consent or participate meaningfully in the study.\n3. Patients with terminal illnesses or those with limited life expectancy where biological age predictions may not be relevant for the purposes of the study.",{"count":54,"type":20},"This is a multi-center, retrospective clinical study designed to evaluate the application and effectiveness of an AI-assisted predictive model for predicting biological age using electronic health records (EHR). The study will analyze various health data points, including medical history, laboratory results, and clinical observations, to estimate the biological age of patients. By comparing biological age with chronological age, the study aims to assess the accuracy of the model and its potential in identifying age-related health risks and improving patient care.",[203],"Biological Age",[203,205,206,207],"electronic health records","AI prediction","aging","2025-04-01",{"date":210,"type":35},"2025-04-02",{"date":212,"type":35},"2023-03-01",{"date":210,"type":20},{"name":41,"class":42},{"id":216,"slug":217,"hasResults":11,"nctId":218,"briefTitle":219,"officialTitle":220,"acronym":4,"eligibilityCriteria":221,"healthyVolunteers":16,"sex":17,"minAge":4,"maxAge":4,"enrollmentInfo":222,"targetDuration":4,"studyType":55,"phases":4,"briefSummary":224,"conditions":225,"keywords":227,"overallStatus":31,"whyStopped":4,"lastUpdateSubmitDate":231,"lastUpdatePostDateStruct":232,"startDateStruct":234,"completionDateStruct":236,"leadSponsor":238,"locationsCount":91},"100581124","ai-assisted-medical-decision-making-100581124","NCT06846229","AI-Assisted Medical Decision-Making","A Cohort Study to Evaluate an Artificial Intelligence Model for Assisting Medical Decision-Making Using Real-Time Hospital-Wide Electronic Health Record Data","Inclusion Criteria:\n\n1. Patients admitted to any department of the hospital (e.g., ICU, general wards, emergency, outpatient services) during the study period.\n2. Patients with available real-time electronic health record (EHR) data, including at least two of the following: laboratory results, vital signs, medical history, and imaging data.\n\nExclusion Criteria:\n\nPatients currently enrolled in another clinical trial that could interfere with data collection or outcomes of this study.",{"count":223,"type":20},50000000,"The study builds and applies an AI model to help doctors predict patient diagnoses and outcomes, such as survival or hospital stay. Real-time, multimodal data (labs, vital signs, history, imaging) from hospital records will be used. Patients will be tracked to compare the AI's performance with standard care. The goal is to improve diagnosis and treatment accuracy in a real-world, prospective study.",[226],"Real-world Study",[228,229,141,230],"diagnosis","prediction","outcome","2025-02-27",{"date":233,"type":35},"2025-03-03",{"date":235,"type":35},"2025-02-24",{"date":237,"type":20},"2026-06",{"name":41,"class":42},""]