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

Conor J. Hardacre, Joseph A. Robertshaw, Shaney L. Barratt, Hannah L. Adams, Robert V.Mackenzie Ross, Graham Re Robinson, Jay Suntharalingam, John D. Pauling, Jonathan Carl Luis Rodrigues

Research output: Contribution to journalReview articlepeer-review

Abstract

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.

Original languageEnglish
Article number20210332
JournalBritish Journal of Radiology
Volume94
Issue number1128
Early online date29 Sep 2021
DOIs
Publication statusPublished - 31 Dec 2021

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

Fingerprint

Dive into the research topics of 'Diagnostic test accuracy of artificial intelligence analysis of cross-sectional imaging in pulmonary hypertension: A systematic literature review'. Together they form a unique fingerprint.

Cite this