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. Advances in knowledge: There is a significant shortage of research in this important area. Early detection of PAH would be supported by further research advances on the promising emerging technologies identified.

Automated summary

This systematic review examines the performance of artificial intelligence in cross-sectional imaging for the diagnosis of PAH. Findings suggest that automated methods for identifying PAH on cardiac MRI show high diagnostic accuracy, which could help reduce diagnostic delay in PAH. Further research and advancements in other modalities are needed in this important area.