[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100562038":3},{"organization":4,"armGroups":7,"interventions":17,"overallOfficials":23,"centralContacts":27,"locations":33,"responsibleParty":50,"collaborators":10,"id":52,"slug":53,"hasResults":54,"nctId":55,"briefTitle":56,"officialTitle":57,"acronym":10,"eligibilityCriteria":58,"healthyVolunteers":54,"sex":59,"minAge":60,"maxAge":61,"enrollmentInfo":62,"targetDuration":10,"studyType":65,"phases":10,"briefSummary":66,"conditions":67,"keywords":69,"overallStatus":36,"whyStopped":10,"lastUpdateSubmitDate":72,"lastUpdatePostDateStruct":73,"startDateStruct":76,"completionDateStruct":78,"leadSponsor":80,"locationsCount":81},{"fullName":5,"class":6},"University Hospitals Cleveland Medical Center","OTHER",[8,14],{"label":9,"type":10,"description":11,"interventionNames":12},"Image going through AI tool",null,"these are the images going through the AI tool",[13],"Device: AI Based CAD Software (qXR-Ln)",{"label":15,"type":10,"description":16,"interventionNames":10},"image not going through AI tool","these are the images not going through the AI tool",[18],{"type":19,"name":20,"description":21,"armGroupLabels":22,"otherNames":10},"DEVICE","AI Based CAD Software (qXR-Ln)","All x-ray images have already been obtained and will then be run through CAD software for secondary nodule detection",[9],[24],{"name":25,"affiliation":5,"role":26},"Amit Gupta, MD","PRINCIPAL_INVESTIGATOR",[28],{"name":29,"role":30,"phone":31,"phoneExt":10,"email":32},"Lauren Hahn","CONTACT","216-844-9312","Lauren.hahn@uhhospitals.org",[34],{"facility":35,"status":36,"city":37,"state":38,"zip":39,"country":40,"countryCode":41,"cosmosGeoPoint":42,"geoPoint":47,"contacts":48},"University Hospitals","RECRUITING","Cleveland","Ohio","44106","United States","US",{"type":43,"coordinates":44},"Point",[45,46],-81.69541,41.4995,{"lat":46,"lon":45},[49],{"name":29,"role":30,"phone":31,"phoneExt":10,"email":32},{"type":26,"investigatorFullName":25,"investigatorTitle":51,"investigatorAffiliation":5,"oldNameTitle":10,"oldOrganization":10},"Associate Professor, Department of Radiology, School of Medicine Member, Cancer Imaging Program, Case Comprehensive Cancer Center, Cardiothoracic Division Chief department of Radiology","100562038","evaluating-the-real-world-performance-of-an-ai-based-lung-nodule-detection-tool-100562038",false,"NCT06597968","Evaluating the Real World Performance of an AI Based Lung Nodule Detection Tool","Performance Estimation of Triaging Artificial Intelligence Based Computer-Aided Detection Algorithm in Routine Chest Radiography","Inclusion Criteria:\n\n* Chest X-ray images of patients aged 18 - 89 years.\n* Modality: CR\u002FDR\u002FDX.\n* PA\u002Fview\n* Lung nodules measuring 6 mm -30 mm (for chest X-ray images where presence of nodules is required).\n\nExclusion Criteria:\n\n* Incomplete view of the chest.\n* Lateral view\n* Known lung cancer at the time of Chest x-ray images.","ALL","18 Years","89 Years",{"count":63,"type":64},45991,"ESTIMATED","OBSERVATIONAL","chest x-rays will be analyzed by AI software for a secondary read of lung nodules. Chest x-rays will either be sent to the AI tool to be read or to radiologists to read. If the image is sent to the AI tool, the AI software will generate a report on if it detects a lung nodule or not. The image will then be sent to a radiologist to determine if there is agreement or disagreement with the AI tool.",[68],"Lung Nodule",[70,71],"chest x-ray","CAD software","2026-01-27",{"date":74,"type":75},"2026-01-29","ACTUAL",{"date":77,"type":75},"2025-06-24",{"date":79,"type":64},"2026-04-30",{"name":5,"class":6},1]