aceRadiological assessment of pulmonary vascularity in congenital heart disease: standardized clinician assessment versus deep learning-based prediction
Background Assessment of pulmonary vascularity on chest radiographs (CXRs) in congenital heart disease (CHD) is limited by subjectivity, and existing criteria lack sufficient validation. Artificial intelligence-based deep learning model (DLM) analysis may improve accuracy. We developed and validated structured criteria and a DLM to evaluate pulmonary vascularity and compared it with conventional clinical interpretation. Methods A total of 400 deidentified CXRs from CHD patients with Fick-derived pulmonary-to-sys…
Artificial intelligence-based deep learning model (DLM) analysis may improve accuracy. Results Intraclass correlation between Fick- and DLM-derived Qp:Qs was 0.782 (P 1.5: 90% specificity, 98% sensitivity, area under the receiver operating characteristic curve [AUROC] 98%; predicting Qp:Qs Conclusion Deep learning-based analysis of CXRs enables accurate evaluation of pulmonary vascularity and outperforms structured assessment by clinicians.
Evidence
- Peer-reviewedJournal of Cardiovascular Imaging2026-08-21
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Truvace Impact Record TRV-2026-0848, v1: “Radiological assessment of pulmonary vascularity in congenital heart disease: standardized clinician assessment versus deep learning-based prediction.” Truvace, 2026-08-22. /record/TRV-2026-0848 (accessed at citation time). sha256 6cf66d9fbee45ac8…
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