TY - JOUR
T1 - Diagnostic test accuracy of artificial intelligence analysis of cross-sectional imaging in pulmonary hypertension
T2 - a systematic literature review
AU - Hardacre, Conor J
AU - Robertshaw, Joseph A
AU - Barratt, Shaney L
AU - Adams, Hannah L
AU - MacKenzie Ross, Robert V
AU - Robinson, Graham Re
AU - Suntharalingam, Jay
AU - Pauling, John D
AU - Rodrigues, Jonathan Carl Luis
PY - 2021/11/1
Y1 - 2021/11/1
N2 - 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.
AB - 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.
KW - Artificial Intelligence
KW - Humans
KW - Hypertension, Pulmonary/diagnostic imaging
KW - Image Interpretation, Computer-Assisted/methods
KW - Lung/blood supply
KW - Magnetic Resonance Imaging/methods
KW - Reproducibility of Results
KW - Sensitivity and Specificity
KW - Tomography, X-Ray Computed/methods
UR - https://research-information.bris.ac.uk/en/publications/2a2643e9-2ecd-475f-aa3a-fa2962398cb3
U2 - 10.1259/bjr.20210332
DO - 10.1259/bjr.20210332
M3 - Article (Academic Journal)
C2 - 34541861
SN - 0007-1285
VL - 94
JO - British Journal of Radiology
JF - British Journal of Radiology
IS - 1128
M1 - 20210332
ER -