Abstract
Introduction/Hypothesis: Transcatheter aortic valve replacement (TAVR) requires pre-surgical planning via CTA. Our purpose is to compare pre-TAVR CTA expert measurements versus an AI in a pilot study. Methods: An AI model was created using Unett++ with a Resnet18 encoder, and was trained using transfer learning. A total of 52 randomized patients with a preTAVR CTA were identified for AI validation. The average diameter from 3 aortic segments was measured by AI: annulus, sinuses of Valsalva, and sinotubular junction. Of the original 52 subjects, 39 patients had expert measurements for comparison with AI results. Standard statistical analysis was performed. Results: Patients were 65% male and 88% Caucasian. The median age and BMI were 82 years and 28 kg/m2, respectively. A Sorensen-Dice coefficient of 0.93 was attained between the expert and AI measurements. Annulus (Pearson r = 0.911), sinuses of Valsalva (r = 0.919), and sinotubular junction (r = 0.874) average diameter measurements correlated with the experts’ (p<0.001 in all cases). It was shown that there was no proportional bias and that the concordance between AI and expert measurements was good to excellent (intraclass correlation coefficients = 0.909 (95% CI 0.834-0.951), 0.899 (0.766-0.952) and 0.830 (0.694-0.908) for annulus, sinuses of Valsalva and sinotubular junction). Limitations include small pilot sample with uncharacterized generalizability. Conclusion: In this validation study, AI measurements of the aortic annulus, sinuses of Valsalva, and sinotubular junction correlate with expert measurements. There was high inter-relater reliability for all sites. Further study will include expanded patient cohorts and accurately streamlined preoperative assessment for TAVR. Figure. Pre TAVR CT angiogram images in orthogonal projection for aortic annulus (A), sinuses of Valsalva (B), and sinotubular junction (C). “Ground Truth” is expert segmentation and “Threshold 0.5” is AI segmentation.
Published Version
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