AI-derived ejection fraction outperformed clinician EF in predicting all-cause mortality in 27,382 cardiac MRI patients, with p<0.001 overall and in CAD and NICM subgroups.
Does deep-learning AI segmentation of cardiac MRI improve the prediction of all-cause mortality compared to expert clinician segmentation?
Deep-learning AI segmentation of cardiac MRI outperforms expert clinician segmentation in predicting all-cause mortality, supporting its use as a fast, low-cost alternative for risk stratification.
Absolute Event Rate: 0% vs 0%
Abstract Background Ejection fraction (EF), a key measure of cardiac function, is used in healthcare for clinical decision-making and risk stratification. We hypothesized that a deep-learning AI segmentation of EF and other LV volume metrics obtained from cardiac MR outperforms expert clinician segmentation for the prediction of all-cause mortality. Methods We studied a retrospective cohort of 27,382 consecutive patients who underwent CMR in the UK (n=26,106 between 2014-2019) 0.001). Differences remained after adjusting for age and sex. AI left ventricular mass index (LVMi) and left ventricular end diastolic volume indexed (LVEDVi) also had higher prediction (both p0.001). Prognostic models that included EF, LVEDVi, and LVMi had higher prediction than EF alone, and in these models, AI was also superior to clinicians. AI EF was superior to clinicians in CAD (p0.001) and NICM (p=0.027). Differences remained when adjusting for the presence of viability and ischaemia in CAD and focal fibrosis in NICM. LV volumetric parameters were poorly predictive of mortality in HCM (all C-Indices 0.6) and in these clinician EF had slightly higher prediction (p=0.002). Conclusion AI-derived EF shows superior prediction of mortality compared to clinicians in all-comer patients and in patients with CAD and NICM. AI EF is deployed at high speed and low-cost. Findings support the superseding of clinician EF with AI. Figure: Deep learning AI LV segmentation - end diastole in exemplar patient - cardiac amyloidosis.Deep learning AI LV segmentation
Joy et al. (Sat,) reported a other. AI-derived ejection fraction outperformed clinician EF in predicting all-cause mortality in 27,382 cardiac MRI patients, with p<0.001 overall and in CAD and NICM subgroups.