Diagnostic test accuracy of artificial intelligence analysis of cross-sectional imaging in pulmonary hypertension: A systematic literature review

Objectives: To undertake the first systematic review examining the performance of artificial intelligence (AI) applied to cross-sectional imaging for the diagnosis of acquired pulmonary arterial hypertension (PAH). Methods: Searches of Medline, Embase and Web of Science were undertaken on 1 July 2020. Original publications studying AI applied to cross-sectional imaging for the diagnosis of acquired PAH in adults were identified through two-staged double-blinded review. Study quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies and Checklist for Artificial Intelligence in Medicine frameworks. Narrative synthesis was undertaken following Synthesis Without Meta-Analysis guidelines. This review received no funding and was registered in the International Prospective Register of Systematic Reviews (ID:CRD42020196295). Results: Searches returned 476 citations. Three retrospective observational studies, published between 2016 and 2020, were selected for data-extraction. Two methods applied to cardiac-MRI demonstrated high diagnostic accuracy, with the best model achieving AUC=0.90 (95% CI: 0.85-0.93), 89% sensitivity and 81% specificity. Stronger results were achieved using cardiac- MRI for classification of idiopathic PAH, achieving AUC=0.97 (95% CI: 0.89-1.0), 96% sensitivity and 87% specificity. One study reporting CT-based AI demonstrated lower accuracy, with 64.6% sensitivity and 97.0% specificity. Conclusions: Automated methods for identifying PAH on cardiac-MRI are emerging with high diagnostic accuracy. AI applied to cross-sectional imaging may provide non-invasive support to reduce diagnostic delay in PAH. This would be helped by stronger solutions in other modalities.