Using Anatomic Intelligence to Localize Mitral Valve Prolapse on Three-Dimensional Echocardiography

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Abstract

Background:

Accurate localization of mitral valve prolapse (MVP) is crucial for surgical planning. Despite improved visualization of the mitral valve by three-dimensional transesophageal echocardiography, image interpretation remains expertise dependent. Manual construction of mitral valve topographic maps improves diagnostic accuracy but is time-consuming and requires substantial manual input. A novel computer-learning technique called Anatomical Intelligence in ultrasound (AIUS) semiautomatically tracks the annulus and leaflet anatomy for parametric analysis. The aims of this study were to examine whether AIUS could improve accuracy and efficiency in localizing MVP among operators with different levels of experience.

Methods:

Two experts and four intermediate-level echocardiographers (nonexperts) retrospectively performed analysis of three-dimensional transesophageal echocardiographic images to generate topographic mitral valve models in 90 patients with degenerative MVP. All echocardiographers performed both AIUS and manual segmentation in sequential weekly sessions. The results were compared with surgical findings.

Results:

Manual segmentation by nonexperts had significantly lower sensitivity (60% vs 90%, P < .001), specificity (91% vs 97%, P = .001), and accuracy (83% vs 95%, P < .001) compared with experts. AIUS significantly improved the accuracy of nonexperts (from 83% to 89%, P = .003), particularly for lesions involving the A3 (from 81% to 94%, P = .006) and P1 (from 78% to 88%, P = .001) segments, presumably related to anatomic variants of the annulus that made tracking more challenging. AIUS required significantly less time for image analysis by both experts (1.9 ± 0.7 vs 9.9 ± 3.5 min, P < .0001) and nonexperts (5.0 ± 0.5 vs 13 ± 1.5 min, P < .0001), especially for complex lesions.

Conclusions:

Anatomic assessment of mitral valve pathology by three-dimensional transesophageal echocardiography is experience dependent. A semiautomated algorithm using AIUS improves accuracy and efficiency in localizing MVP by less experienced operators.

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