AI-assisted computed tomography analysis for pre-procedural planning prior to TAVI

Several software programs have specifically been developed to analyse cardiac computed tomography prior to transcatheter aortic valve implantation (TAVI). However, they are not able to perform a complete analysis independently. We report the performance of a fully automated, deep learning-based algorithm for pre-procedural CT analysis as compared to the current clinical standard.

Patients with symptomatic severe aortic stenosis undergoing TAVI were retrospectively enrolled. The pre-procedural dataset was analysed by both a standard TAVI CT-analysis software and by a fully automated CT analysis platform with a deep learning-based algorithm.

Ninety-eight patients were included in the analysis. The mean annulus diameter was 24.4 ± 2.4 mm (conventional = 3mensio, Pie Medical Imaging, 3 M) vs. 24.0 ± 2.4 mm (artificial intelligence = AI), mean absolute error (MAE): 0.64 mm, mean absolute percentage error (MAPE): 2.6%. The mean annulus perimeter was measured at 77.7 ± 7.4 mm (3 M) vs. 76.1 ± 7.5 mm (AI), MAE: 2.26 mm, MAPE: 2.9%. The mean annulus area was calculated at 468.9 ± 92.1 mm² (3 M) vs. 455.6 ± 91.0 mm² (AI), MAE: 22.4 mm², MAPE: 4.8%. The intraclass correlation coefficients (ICCs) of all abovementioned parameter were > 0.95 showing an excellent correlation between the two methods. The distance from the annulus to the left coronary artery depicted to 14.0 ± 3.2 mm (3 M) vs. 12.6 ± 2.8 mm (AI), MAE: 2.1 mm, MAPE: 14.3%. The distance to the right coronary artery was 17.1 ± 2.7 mm (3 M) vs. 16.5 ± 3.2 mm (AI), MAE: 1.7 mm, MAPE: 10.1%. The ICCs of the distances to the coronary ostia showed good correlation between both methods.

In this retrospective analysis, a deep learning-based analysis of pre-procedural CT datasets showed good to excellent correlation with conventional assessment for the preprocedural TAVI CT measurements. AI-based fully automated CT analysis could emerge to a valuable alternative to conventional CT assessment in the pre-procedural-planning for TAVI.