[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100576909":3},{"organization":4,"armGroups":7,"interventions":14,"overallOfficials":10,"centralContacts":19,"locations":25,"responsibleParty":74,"collaborators":10,"id":79,"slug":80,"hasResults":81,"nctId":82,"briefTitle":83,"officialTitle":84,"acronym":10,"eligibilityCriteria":85,"healthyVolunteers":86,"sex":87,"minAge":10,"maxAge":10,"enrollmentInfo":88,"targetDuration":10,"studyType":91,"phases":10,"briefSummary":92,"conditions":93,"keywords":95,"overallStatus":28,"whyStopped":10,"lastUpdateSubmitDate":99,"lastUpdatePostDateStruct":100,"startDateStruct":103,"completionDateStruct":105,"leadSponsor":107,"locationsCount":108},{"fullName":5,"class":6},"The Eye Hospital of Wenzhou Medical University","OTHER",[8],{"label":9,"type":10,"description":11,"interventionNames":12},"AI-Based Genotype Prediction Using EHR and Multimodal Data",null,"This cohort consists of patients whose historical health data, including electronic health records (EHR), clinical lab results, and multimodal imaging data (such as X-rays, MRIs, and CT scans), will be analyzed by an AI-based prediction model to predict their genotype. There are no active interventions in this cohort, as the study aims to use non-genetic health data to infer genetic information. Participants will not undergo genetic testing but will provide their health data for analysis by the AI system. The goal of this group is to assess the accuracy of the AI model in predicting genotypes and identifying genetic predispositions to various diseases based on available health data.",[13],"Other: AI-Predictng Model",[15],{"type":6,"name":16,"description":17,"armGroupLabels":18,"otherNames":10},"AI-Predictng Model","The intervention in this study involves an AI-based predictive model designed to analyze and integrate patient electronic health records (EHR), clinical lab results, and multimodal imaging data (e.g., X-rays, MRIs, CT scans). The AI model is trained to predict a patient's genotype based on these non-genetic data sources. This model uses machine learning algorithms to detect patterns and infer genetic information that would traditionally require direct genetic testing. There are no active treatments or genetic tests involved in this intervention; rather, the AI system serves as a tool to predict genetic information from available clinical data, offering a non-invasive and potentially more accessible alternative to genetic testing.",[9],[20],{"name":21,"role":22,"phone":23,"phoneExt":10,"email":24},"Fei Liu, MD","CONTACT","+86 13810512704","liufei_2359@163.com",[26,44,54,64],{"facility":27,"status":28,"city":29,"state":30,"zip":10,"country":31,"countryCode":32,"cosmosGeoPoint":33,"geoPoint":38,"contacts":39},"Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University","RECRUITING","Guangzhou","Guangdong","China","CN",{"type":34,"coordinates":35},"Point",[36,37],113.25,23.11667,{"lat":37,"lon":36},[40],{"name":41,"role":22,"phone":42,"phoneExt":10,"email":43},"Yunfang Yu","+86 020-81332199","yuyf9@mail.sysu.edu.cn",{"facility":45,"status":28,"city":29,"state":30,"zip":10,"country":31,"countryCode":32,"cosmosGeoPoint":46,"geoPoint":48,"contacts":49},"Sun Yat-sen University Cancer Hospital",{"type":34,"coordinates":47},[36,37],{"lat":37,"lon":36},[50],{"name":51,"role":22,"phone":52,"phoneExt":10,"email":53},"Yuxing Lu","+86 13161233730","yxlu0613@gmail.com",{"facility":55,"status":56,"city":57,"state":58,"zip":10,"country":31,"countryCode":32,"cosmosGeoPoint":59,"geoPoint":63,"contacts":10},"First Affiliated Hospital of Wenzhou Medical University","COMPLETED","Wenzhou","Zhejiang",{"type":34,"coordinates":60},[61,62],120.66682,27.99942,{"lat":62,"lon":61},{"facility":65,"status":28,"city":57,"state":58,"zip":10,"country":31,"countryCode":32,"cosmosGeoPoint":66,"geoPoint":68,"contacts":69},"Second Affiliated Hospital of Wenzhou Medical University",{"type":34,"coordinates":67},[61,62],{"lat":62,"lon":61},[70],{"name":71,"role":22,"phone":72,"phoneExt":10,"email":73},"Sian Liu","+86-0577-88002888","liusan@mail3.sysu.edu.cn",{"type":75,"investigatorFullName":76,"investigatorTitle":77,"investigatorAffiliation":78,"oldNameTitle":10,"oldOrganization":10},"PRINCIPAL_INVESTIGATOR","Kang Zhang","Chief Scientist","Wenzhou Medical University","100576909","ai-driven-genotype-prediction-using-ehr-and-multimodal-data-100576909",false,"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.",true,"ALL",{"count":89,"type":90},100000,"ESTIMATED","OBSERVATIONAL","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.",[94],"Genotype",[96,97,94,98],"AI","AI-prediction","Multimodal data","2025-04-16",{"date":101,"type":102},"2025-04-17","ACTUAL",{"date":104,"type":102},"2023-07-01",{"date":106,"type":90},"2025-06",{"name":5,"class":6},4]