Key result
Cardioverse algorithm cuts pre-TAVR assessment time ~80% vs expert observers in bicuspid aortic stenosis.
Why the study?
Bicuspid aortic valve stenosis complicates TAVR planning and no validated automated measurement algorithm was available for anatomical assessment.
Does the Cardioverse deep learning algorithm reduce assessment time and maintain accuracy compared to expert observers in patients with bicuspid aortic stenosis undergoing TAVR?
Cohort (n=1,147)
Blinded observers
Yes
Does the Cardioverse deep learning algorithm reduce assessment time and maintain accuracy compared to expert observers in patients with bicuspid aortic stenosis undergoing TAVR?
Effect estimate: 80% reduction
Absolute Event Rate: 241% vs 1251%
p-value: p=<0.001
The Cardioverse AI algorithm significantly accelerates pre-TAVR anatomical assessment for bicuspid aortic valves while maintaining expert-level accuracy, potentially streamlining clinical workflows.
May streamline pre-TAVR workflows in bicuspid stenosis; hypothesis-generating and requires prospective trials before practice change.
Aims Bicuspid aortic valve (BAV) stenosis complicates transcatheter aortic valve replacement (TAVR) planning, with no validated automated measurement algorithm available. We developed Cardioverse, the first fully automated deep learning algorithm for BAV anatomical assessment in TAVR planning. Methods and results We conducted a large-scale, multicenter retrospective study encompassing 1,147 consecutive patients with BAV undergoing TAVR across 16 high-volume Chinese centers (March 2019–February 2023). Cardioverse was trained on this cohort and evaluated in an internal (n = 437) and external (n = 110) validation cohorts. Our novel Cardioverse algorithm demonstrated exceptional segmentation performance across all anatomical targets with Dice similarity coefficients >0.97 for coronary cusps and ostia. Critically, the algorithm achieved unprecedented workflow efficiency gains: 80% reduction in assessment time [241.0 IQR (181.0, 297.0) vs. 1251.0 IQR (872.0, 1408.0) seconds], 85% reduction in user interactions [57.0 IQR (45.0, 78.0) vs. 382.5 IQR (285.5, 475.0) clicks], and 87% reduction in manual effort [7.5 IQR (4.8, 9.3) vs. 57.2 IQR (43.5, 68.4) meters mouse movement] compared to expert observers (P < 0.001). Importantly, accuracy was maintained across all BAV phenotypes with correlation coefficients >0.91 for all critical measurements, including annular dimensions, calcification quantification, and aortic root morphology assessment. Conclusion Cardioverse transforms pre-TAVR assessment for BAV patients, offering a validated solution combining accuracy and efficiency. It reduces assessment time from over 20 min to < 5 min, addressing the need for standardized, rapid, and reliable BAV evaluation. Its robust performance across diverse BAV phenotypes makes it a crucial tool for enhancing TAVR planning consistency. Clinical trial registration ClinicalTrials.gov Protocol Registration System (NCT05044338).
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Mao et al. (2025) conducted a cohort in Bicuspid aortic valve stenosis (n=1,147). Cardioverse deep learning algorithm vs. Expert observers was evaluated on Assessment time (80% reduction, p=<0.001). The Cardioverse deep learning algorithm reduced pre-TAVR assessment time by 80% compared to expert observers in patients with bicuspid aortic stenosis.
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