AI derived myocardial left ventricular volumes outperforms clinician segmentation for prediction of all-cause mortality

Abstract

Abstract Background Measuring heart structure and function drives much cardiac decision making and therapy. Precise quantification is therefore of high importance, but manual (clinician) segmentation introduces measurement variability. Purpose Automated segmentation of cardiac MRI (CMR) left ventricular (LV) volumes provides improved precision but how this translates into clinical outcomes has yet to be defined. In order to test predictive accuracy in a wide range of adverse LV morphologies, we applied AI LV segmentation to patients with coronary disease or cardiomyopathy. Methods Scans were analysed from a retrospective clinical CMR database and mortality data was obtained from a national database. Clinician measurement of indexed LV end-diastolic volume (LVEDVi), LV mass (LVMi), myocardial contraction fraction (MCF: ratio of stroke volume to myocardial volume; a measure of myocardial efficiency) and EF were recorded from clinical reports (all senior clinicians with >5 years level 3 experience) and compared to AI measurement using a segmentation algorithm. This has previously been shown to exceed human precision and required no manual correction (1,2). Random survival forests were built to identify overall predictive value of AI vs clinician derived LV volumes. These are machine-learning algorithms for predicting time-to-event outcomes and can capture complex relationships between predictors and outcome. Random survival forests were built from AI and clinician models with EF, EDVi, LVMi and MCF as predictors for all-cause mortality. A train:test (80:20) split, stratified for outcome was employed. Concordance (C)-indices (measuring predictive accuracy) and permutation importances (measuring significance of each predictor within the model) (Table) with 1000 bootstrapped 95% confidence intervals and p-values were derived. Results 8,299 patients were analysed. Patients were 60(51-70) years old and 37% female. 49% had coronary disease, 25% had DCM and 19% had HCM. At a median follow-up of 5.3 years (IQR 4.2-6.6 years) there were 1,384 deaths (17%). AI derived LV-parameter models had higher predictive accuracy than clinician derived models (C-Indices: 0.66±0.002 vs 0.65±0.005, p<0.001). A subgroup analysis of patients undergoing ischaemia testing for coronary disease was performed (n=3,560) and markers of ischaemia (positive vs negative) and infarction (non-viable vs viable) were included in the models. AI had a higher predictive accuracy compared to clinicians when including these additional predictors (C-Indices: 0.68±0.03 vs 0.63±0.01, p<0.001). Conclusion AI derived LV volumes outperforms clinicians in prediction of all-cause mortality reflecting improved precision. Improvements in predictive accuracy are likely to be clinically important when applied to populations and high-risk groups. Manual clinician segmentation should be superseded by clinician-supervised AI.

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