[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100582369":3},{"organization":4,"armGroups":7,"interventions":23,"overallOfficials":30,"centralContacts":35,"locations":45,"responsibleParty":65,"collaborators":29,"id":67,"slug":68,"hasResults":69,"nctId":70,"briefTitle":71,"officialTitle":72,"acronym":29,"eligibilityCriteria":73,"healthyVolunteers":74,"sex":75,"minAge":76,"maxAge":29,"enrollmentInfo":77,"targetDuration":29,"studyType":80,"phases":81,"briefSummary":83,"conditions":84,"keywords":88,"overallStatus":91,"whyStopped":29,"lastUpdateSubmitDate":92,"lastUpdatePostDateStruct":93,"startDateStruct":96,"completionDateStruct":98,"leadSponsor":100,"locationsCount":101},{"fullName":5,"class":6},"National Taiwan University Hospital","OTHER",[8,14,19],{"label":9,"type":10,"description":11,"interventionNames":12},"A","EXPERIMENTAL","Deep learning system",[13],"Device: Smartphone-based deep learning system",{"label":15,"type":16,"description":17,"interventionNames":18},"B","ACTIVE_COMPARATOR","Board-certified dentist with deep learning system",[13],{"label":20,"type":16,"description":21,"interventionNames":22},"C","non-certified health providers (general practitioners) with deep learning system",[13],[24],{"type":25,"name":26,"description":27,"armGroupLabels":28,"otherNames":29},"DEVICE","Smartphone-based deep learning system","The smartphone-based deep learning system was trained using a dataset of over 50,000 white-light macroscopic images collected between 2006 and 2013 to develop the YOLOv7 model. Lesions were categorized into three referral grades: benign (green), potentially malignant (yellow), and malignant (red).",[9,15,20],null,[31],{"name":32,"affiliation":33,"role":34},"Shao-Yi Cheng, MD, MSc, DrPH","Department of Family Medicine, College of Medicine and Hospital, National Taiwan University","STUDY_CHAIR",[36,41],{"name":32,"role":37,"phone":38,"phoneExt":39,"email":40},"CONTACT","+886-2312-3456","266823","scheng2140@gmail.com",{"name":42,"role":37,"phone":38,"phoneExt":43,"email":44},"I Ann Hsiao, MD","266634","iamiannhsiao@gmail.com",[46],{"facility":47,"status":29,"city":48,"state":29,"zip":49,"country":50,"countryCode":51,"cosmosGeoPoint":52,"geoPoint":57,"contacts":58},"Department of Family Medicine, National Taiwan University Hospital","Taipei","100229","Taiwan","TW",{"type":53,"coordinates":54},"Point",[55,56],121.52639,25.05306,{"lat":56,"lon":55},[59,61,62],{"name":32,"role":37,"phone":60,"phoneExt":39,"email":40},"+886-2-23123456",{"name":42,"role":37,"phone":60,"phoneExt":43,"email":44},{"name":63,"role":64,"phone":29,"phoneExt":29,"email":29},"Yi-Hsuan Lee, MD, MPH","PRINCIPAL_INVESTIGATOR",{"type":66,"investigatorFullName":29,"investigatorTitle":29,"investigatorAffiliation":29,"oldNameTitle":29,"oldOrganization":29},"SPONSOR","100582369","application-and-validation-of-a-smartphone-based-deep-learning-system-for-oral-potentially-malignant-disorders-and-oral-cancer-screening-100582369",false,"NCT06862414","Application and Validation of a Smartphone-based Deep Learning System for Oral Potentially Malignant Disorders and Oral Cancer Screening","Application and Validation of a Smartphone-based Deep Learning System for Oral Potentially Malignant Disorders (OPMD) and Oral Cancer Screening","Inclusion Criteria:\n\n* Adult patients (age ≥18) visiting cancer screening center\n\nExclusion Criteria:\n\n* Unable to cooperate to fully open mouth\u002F navigate tongue\n* Unable to cooperate for the assessment",true,"ALL","19 Years",{"count":78,"type":79},954,"ESTIMATED","INTERVENTIONAL",[82],"NA","The goal of this clinical trial is to learn if smartphone-based deep learning system works to accurately detect oral potentially malignant disorders and oral cancer in adults. It will also learn about if it is as effective as assessments conducted by dentists and non-certified health provider.\n\nWe expect that the deep learning system will have higher sensitivity in detecting oral potentially malignant disorders and oral cancer, where as the dentists and non-certified health providers will exhibit higher specificity in screening.\n\nParticipants will be grouped into three arms: deep learning system (arm A) or board-certified dentist with deep learning system (arm B) or non-certified health providers (general practitioners) with deep learning system (arm C).\n\nOral cancer risk factors, such as habits of smoking or having chewed betel nut or alcohol drinking, would be recorded by anonymous questionnaires.",[85,86,87],"Cancer Screening","Oral Cancer","Oral Potentially Malignant Disorders",[89,90],"Deep learning systems","Artificial Intelligence","NOT_YET_RECRUITING","2025-03-02",{"date":94,"type":95},"2025-03-06","ACTUAL",{"date":97,"type":79},"2025-03",{"date":99,"type":79},"2025-12",{"name":5,"class":6},1]