Development and Validation an AI Model to Improve the Diagnosis of Deep Infiltrating Endometriosis for Junior Sonologists

Jing Xu, Xu J, Aohua Zhang, Zhang A, Zhijuan Zheng, Zheng Z, Cao J, Junyan Cao, Xinling Zhang, Zhang X
Ultrasound in medicine & biology · 2025 · vol. 51(7) , pp. 1143–1147 · doi:10.1016/j.ultrasmedbio.2025.03.012 · PMID:40251088 · W4409531402
article OA: closed CC0 ⤵ 2 in-corpus citations
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This study developed and validated an AI model to enhance the diagnostic accuracy of deep infiltrating endometriosis by junior sonologists.

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Abstract

OBJECTIVE: This study aims to develop and validate an artificial intelligence (AI) model based on ultrasound (US) videos and images to improve the performance of junior sonologists in detecting deep infiltrating endometriosis (DE). METHODS: In this retrospective study, data were collected from female patients who underwent US examinations and had DE. The US image records were divided into two parts. First, during the model development phase, an AI-DE model was trained employing YOLOv8 to detect pelvic DE nodules. Subsequently, its clinical applicability was evaluated by comparing the diagnostic performance of junior sonologists with and without AI-model assistance. RESULTS: The AI-DE model was trained using 248 images, which demonstrated high performance, with a mAP50 (mean Average Precision at IoU threshold 0.5) of 0.9779 on the test set. Total 147 images were used for evaluate the diagnostic performance. The diagnostic performance of junior sonologists improved with the assistance of the AI-DE model. The area under the receiver operating characteristic (AUROC) curve improved from 0.748 (95% CI, 0.624-0.867) to 0.878 (95% CI, 0.792-0.964; p < 0.0001) for junior sonologist A, and from 0.713 (95% CI, 0.592-0.835) to 0.798 (95% CI, 0.677-0.919; p < 0.0001) for junior sonologist B. Notably, the sensitivity of both sonologists increased significantly, with the largest increase from 77.42% to 94.35%. CONCLUSION: The AI-DE model based on US images showed good performance in DE detection and significantly improved the diagnostic performance of junior sonologists.

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Condition tags

endometriosisdie_deep_infiltrating

MeSH descriptors

Artificial Intelligence Artificial Intelligence Artificial Intelligence Artificial Intelligence Artificial Intelligence Artificial Intelligence Artificial Intelligence Artificial Intelligence Artificial Intelligence Artificial Intelligence Artificial Intelligence Artificial Intelligence Artificial Intelligence Artificial Intelligence Artificial Intelligence Artificial Intelligence Artificial Intelligence Artificial Intelligence Artificial Intelligence Artificial Intelligence

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Papers in the corpus that this work cites (lower rings, blue) and that cite this one (upper rings, green). Dot size scales with the paper's in-corpus citation count — bigger dot = more influential within the endo/adeno field. Click a dot to open that paper. [ expand to 2 hops ] — adds papers reached through this work's immediate citers/citees. Heavier; up to 60 extra dots.

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europepmc
last seen: 2026-09-06T09:34:12.023084+00:00
openalex
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