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Health·G Space·Evidence-backed gain·Published 2026-08-22

Radiological assessment of pulmonary vascularity in congenital heart disease: standardized clinician assessment versus deep learning-based prediction

Abstract: 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…

TRV-2026-0848Peer-reviewedPermanent record — cite & verify
Radiological assessment of pulmonary vascularity in congenital heart disease: standardized clinician assessment versus deep learning-based prediction

Cardio surgery 2 by Dominique Cappronnier. CC BY-SA 3.0 · https://creativecommons.org/licenses/by-sa/3.0

The quick read

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.

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.

Main points
  • 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.
Gain

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.

The rundown

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-systemic flow ratio (Qp:Qs) were evaluated by three experts and three trainees in pediatric cardiology and radiology using nine prespecified criteria.

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