ArticleJournal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine2026
AI-Driven Analysis of the Fetal Left Ventricular Outflow Tract: Diagnostic Value and Applications.
Article in Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Deep Learning for Fetal Left Ventricular Outflow Tract Assessment: Opportunities and Challenges.Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine · 2026Article
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Authors and funding
7 authors.
Funding
Abstract
objectivesTo develop and validate a deep-learning pipeline that identifies left ventricular outflow tract (LVOT) views in routine obstetric screening report images and audits 7 predefined LVOT image-quality criteria with an interpretable 0-7 summary score.
methodsWe randomly selected 180 screening report files from low-risk pregnancies (90 second trimester, 20-25 weeks; 90 third trimester, 30-35 weeks). An expert provided file-level reference labels for LVOT presence/absence and for each quality criterion (criterion "present" if met on ≥1 LVOT image within a file), plus the summary score. The algorithm performed LVOT detection, criterion prediction, and identical file-level aggregation. Agreement was assessed using Cohen κ for binary endpoints and the intraclass correlation coefficient (ICC) for the summary score.
resultsThe dataset included 1,097 images. LVOT was absent in 10/180 (5.6%) files and all 10 were correctly classified as absent. Agreement for LVOT presence was substantial (κ = 0.768). Agreement across criteria ranged from κ = 0.657 to 0.957, and agreement for the 0-7 summary score was excellent (ICC = 0.902). For LVOT presence, sensitivity was 97.7% and specificity was 90.0%. Sensitivity was high for criteria 1-5 (96.3%-99.4%); criterion 6 showed sensitivity of 92.5% and specificity of 71.7%; criterion 7 showed sensitivity of 78.4% and specificity of 96.9%.
conclusionsAutomated LVOT identification and criterion-based quality auditing achieved substantial-to-near-perfect agreement with expert review and may enable scalable quality assurance and targeted training in fetal cardiac screening.
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