[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100657144":3},{"organization":4,"outcomesModule":7,"designInfo":14,"detailedDescription":13,"studyPopulation":17,"armGroups":18,"interventions":13,"overallOfficials":13,"centralContacts":13,"locations":13,"responsibleParty":22,"collaborators":13,"id":26,"slug":27,"hasResults":28,"nctId":29,"briefTitle":30,"officialTitle":31,"acronym":13,"eligibilityCriteria":32,"healthyVolunteers":28,"sex":33,"minAge":34,"maxAge":13,"enrollmentInfo":35,"targetDuration":13,"studyType":38,"phases":13,"briefSummary":39,"conditions":40,"keywords":43,"overallStatus":51,"whyStopped":13,"lastUpdateSubmitDate":52,"lastUpdatePostDateStruct":53,"startDateStruct":56,"completionDateStruct":58,"leadSponsor":60,"locationsCount":13},{"fullName":5,"class":6},"Assiut University","OTHER",{"primaryOutcomes":8,"secondaryOutcomes":13,"otherOutcomes":13},[9],{"measure":10,"description":11,"timeFrame":12},"Area Under the Receiver Operating Characteristic Curve (AUROC)","AUROC will be calculated to assess and compare the diagnostic performance of the study-trained DenseNet-121 model versus the CheXpert-pretrained DenseNet-121 model in detecting thoracic pathologies on frontal chest radiographs. AI predictions will be compared against the reference standard of expert radiologist consensus, with discordant findings adjudicated by chest CT. AUROC values range from 0.5 (no discrimination) to 1.0 (perfect discrimination).","Baseline",null,{"allocation":13,"interventionModel":13,"interventionModelDescription":13,"primaryPurpose":13,"observationalModel":15,"timePerspective":16,"maskingInfo":13},"COHORT","CROSS_SECTIONAL","The study population comprises adult patients aged 18 years and older who undergo frontal chest radiography within institutional radiology departments. Eligible subjects have de-identified, diagnostic-quality frontal chest X-ray images with available reference diagnostic labels verified through expert radiologist consensus and chest CT adjudication. Pediatric cases, non-diagnostic images, incomplete metadata, and duplicate scans are excluded.",[19],{"label":20,"type":13,"description":21,"interventionNames":13},"Adult Frontal Chest Radiography Cohort","This cohort includes adult patients aged 18 years and older who undergo diagnostic-quality frontal chest radiography with available reference diagnostic labels. De-identified chest radiographs from this group are evaluated by two deep learning architectures: a study-trained\u002Ffine-tuned DenseNet-121 convolutional neural network and a CheXpert-pretrained DenseNet-121 model. Model predictions are evaluated against reference standards derived from expert radiologist consensus, with discordant cases adjudicated via chest computed tomography (CT).",{"type":23,"investigatorFullName":24,"investigatorTitle":25,"investigatorAffiliation":5,"oldNameTitle":13,"oldOrganization":13},"PRINCIPAL_INVESTIGATOR","Ibrahim Mohamed Ibrahim","Resident","100657144","ai-based-assessment-of-chest-diseases-on-chest-x-ray-imaging-using-densenet-121-and-a-chexpert-trained-model-100657144",false,"NCT07834879","AI-Based Assessment of Chest Diseases on Chest X-ray Imaging Using DenseNet-121 and a CheXpert-Trained Model","AI-Based Assessment of Chest Diseases on Chest X-ray Imaging Using DenseNet-121 and a CheXpert-Trained Model: A Comparative Diagnostic Accuracy Study","Inclusion Criteria:\n\n* Adult patients aged 18 years or older.\n* Undergoing frontal chest radiography.\n* Diagnostic-quality frontal chest radiographs.\n* Availability of reference diagnostic labels (verified by expert radiologist consensus or chest CT).\n\nExclusion Criteria:\n\n* Pediatric patients (under 18 years of age).\n* Non-diagnostic or poor-quality chest radiographs.\n* Incomplete clinical or imaging metadata.\n* Duplicate radiographic images or repeat patient entries.","ALL","18 Years",{"count":36,"type":37},100,"ESTIMATED","OBSERVATIONAL","Chest X-rays are widely used to detect thoracic and lung conditions, but reviewing high volumes of radiographs can lead to workload strain and variation between interpreters. Artificial intelligence (AI), particularly deep learning neural networks like DenseNet-121, has shown strong potential to assist clinicians with automated image interpretation. However, AI models trained on large international datasets, such as CheXpert, may perform differently across distinct patient populations due to variations in imaging technique, patient demographics, and disease presentation.\n\nThe primary purpose of this study is to compare the diagnostic accuracy of a DenseNet-121 model trained or fine-tuned on local data against a DenseNet-121 model pretrained on the CheXpert dataset for identifying thoracic pathologies. Both models will evaluate de-identified frontal chest radiographs from adult patients. Model predictions will be compared against a reference standard established by expert radiologist consensus, with discordant findings resolved using chest computed tomography (CT). Findings will evaluate whether local model adaptation improves diagnostic precision and workflow efficiency in clinical settings.",[41,42],"Thoracic Diseases","Lung Diseases",[44,45,46,47,48,49,50],"DenseNet-121","CheXpert","Chest X-ray","Deep Learning","Artificial Intelligence","Diagnostic Accuracy","Convolutional Neural Network","NOT_YET_RECRUITING","2026-09-17",{"date":54,"type":55},"2026-09-22","ACTUAL",{"date":57,"type":37},"2026-10",{"date":59,"type":37},"2027-11",{"name":5,"class":6}]