[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100651433":3},{"organization":4,"armGroups":7,"interventions":18,"overallOfficials":10,"centralContacts":24,"locations":30,"responsibleParty":46,"collaborators":50,"id":54,"slug":55,"hasResults":56,"nctId":57,"briefTitle":58,"officialTitle":59,"acronym":60,"eligibilityCriteria":61,"healthyVolunteers":56,"sex":62,"minAge":63,"maxAge":10,"enrollmentInfo":64,"targetDuration":10,"studyType":67,"phases":10,"briefSummary":68,"conditions":69,"keywords":72,"overallStatus":33,"whyStopped":10,"lastUpdateSubmitDate":81,"lastUpdatePostDateStruct":82,"startDateStruct":85,"completionDateStruct":87,"leadSponsor":89,"locationsCount":90},{"fullName":5,"class":6},"Taichung Veterans General Hospital","OTHER",[8,14],{"label":9,"type":10,"description":11,"interventionNames":12},"Pulmonary Fibrosis-Positive Cases",null,"Participants with pre-existing chest computed tomography examinations selected as potentially positive for pulmonary fibrosis based on available institutional radiology records. Final pulmonary fibrosis status for the performance analysis will be determined by majority agreement of at least two of three blinded specialists in pulmonology or radiology.",[13],"Device: AccuPulmo CT Portal",{"label":15,"type":10,"description":16,"interventionNames":17},"Pulmonary Fibrosis-Negative Controls","Participants with pre-existing chest computed tomography examinations selected as potentially negative for pulmonary fibrosis based on available institutional radiology records. Final pulmonary fibrosis status for the performance analysis will be determined by majority agreement of at least two of three blinded specialists in pulmonology or radiology.",[13],[19],{"type":20,"name":21,"description":22,"armGroupLabels":23,"otherNames":10},"DEVICE","AccuPulmo CT Portal","AccuPulmo CT Portal is an artificial intelligence-assisted medical imaging software intended to analyze chest computed tomography images and identify imaging findings associated with pulmonary fibrosis. The software estimates the proportion of pulmonary fibrosis within the lung. In this study, a pulmonary fibrosis area greater than 10 percent is classified as positive, and a pulmonary fibrosis area of 10 percent or less is classified as negative.\n\nThe software will be applied retrospectively to de-identified pre-existing chest computed tomography images in an offline research environment. Its output will not be returned to treating physicians and will not affect participant diagnosis, treatment, or clinical management.",[15,9],[25],{"name":26,"role":27,"phone":28,"phoneExt":10,"email":29},"Pin-Kuei Fu, MD","CONTACT","886 4 2359 2525","yetquen@gmail.com",[31],{"facility":32,"status":33,"city":34,"state":10,"zip":35,"country":36,"countryCode":37,"cosmosGeoPoint":38,"geoPoint":43,"contacts":44},"Taichung Veterans General Hospital City: Taichung","RECRUITING","Taichung","407219","Taiwan","TW",{"type":39,"coordinates":40},"Point",[41,42],120.6839,24.1469,{"lat":42,"lon":41},[45],{"name":26,"role":27,"phone":28,"phoneExt":10,"email":29},{"type":47,"investigatorFullName":48,"investigatorTitle":49,"investigatorAffiliation":5,"oldNameTitle":10,"oldOrganization":10},"PRINCIPAL_INVESTIGATOR","Pin-Kuei Fu, MD, PhD","Director, Division of Clinical Trials",[51],{"name":52,"class":53},"V5med Inc.","INDUSTRY","100651433","retrospective-validation-of-accupulmo-ct-portal-for-detecting-pulmonary-fibrosis-on-chest-ct-100651433",false,"NCT07761377","Retrospective Validation of AccuPulmo CT Portal for Detecting Pulmonary Fibrosis on Chest CT","Evaluation of the Accuracy and Effectiveness of the AccuPulmo CT Portal AI-Assisted Interpretation System for the Diagnosis of Pulmonary Fibrosis","ACCUPULMO-FIBR","Inclusion Criteria:\n\n* Participants aged 20 years or older at the time of the chest computed tomography examination\n* Participants who underwent chest computed tomography for pulmonary disease at Taichung Veterans General Hospital between January 1, 2020, and December 31, 2024\n* Availability of a completed clinical radiology report\n* Availability of chest computed tomography images suitable for de-identification and analysis by AccuPulmo CT Portal\n* Availability of sufficient image information to permit blinded specialist assessment of pulmonary fibrosis\n\nExclusion Criteria:\n\n* Missing or incomplete chest computed tomography images\n* Image quality insufficient for pulmonary fibrosis assessment\n* Cardiac implants or other devices that substantially interfere with lung texture assessment\n* Extensive pneumonia that substantially interferes with lung texture assessment\n* Pleural effusion that substantially interferes with lung texture assessment\n* Other image abnormalities or artifacts that preclude reliable evaluation of pulmonary fibrosis","ALL","20 Years",{"count":65,"type":66},900,"ESTIMATED","OBSERVATIONAL","This retrospective observational study evaluates the diagnostic performance of AccuPulmo CT Portal, an artificial intelligence-assisted medical imaging software, for detecting pulmonary fibrosis on pre-existing chest computed tomography images.\n\nA total of 900 chest computed tomography examinations obtained at Taichung Veterans General Hospital between January 1, 2020, and December 31, 2024, will be retrospectively selected. The planned sample includes 300 examinations with pulmonary fibrosis and 600 examinations without pulmonary fibrosis.\n\nAll study images will be de-identified and coded before evaluation. Three qualified specialists in pulmonology or radiology will independently review each image without access to the original radiology report or the artificial intelligence output. The reference standard will be established by majority agreement of at least two of the three specialists.\n\nAccuPulmo CT Portal will retrospectively analyze the coded images. An artificial intelligence-derived pulmonary fibrosis area greater than 10 percent will be classified as positive, and an area of 10 percent or less will be classified as negative. The primary performance measures are sensitivity and specificity. Secondary measures include accuracy, positive predictive value, negative predictive value, and performance across clinically relevant subgroups.\n\nThe software results will not be returned to treating physicians and will not affect participant diagnosis, treatment, or clinical management.",[70,71],"Pulmonary Fibrosis","Interstitial Lung Disease",[73,74,75,70,71,76,77,78,79,80],"Artificial Intelligence","Medical Device Software","Chest Computed Tomography","Computer-Aided Detection","Computer-Aided Triage","Diagnostic Performance","Image Analysis","Deep Learning","2026-08-10",{"date":83,"type":84},"2026-08-12","ACTUAL",{"date":86,"type":84},"2025-10-15",{"date":88,"type":66},"2026-12-31",{"name":5,"class":6},1]