An observational study in Thoracic Diseases and Lung Diseases, sponsored by Assiut University. Not yet recruiting. Open to participants aged 18 Years and older. Per ClinicalTrials.gov, last updated 2026-09-22.
Sponsored by Assiut University · 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.
The 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.
72 studies on the registry are indexed under Thoracic Diseases; 13 are open to participants now.
This study's planned enrollment of 100 is below the median of 122 across 19 observational studies indexed under Thoracic Diseases.
Browse Thoracic Diseases studies →Assiut University is the lead sponsor of 5,078 studies on the registry; 2,178 are open to participants now.
Of its 14 completed or terminated interventional studies of FDA-regulated products, 0 (0%) have results posted.
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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.
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Exclusion Criteria:
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/fine-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).
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).
Time frame: Baseline
No study locations are listed for this record.
Start is estimated for Oct 2026; primary completion for Oct 2027, 12 months later.
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This study is not yet recruiting, as verified in Sep 2026. You cannot join it, but the record below documents what was studied.
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