Fully end-to-end automated bladder segmentation and risk assessment based on bladder morphology features for predicting upper urinary tract dysfunction

The bladder morphology features are robust predictors of UUTD. This fully automated, E2E bladder segmentation and UUTD risk prediction model demonstrated superior performance compared to traditional clinical parameters, providing a quantitative and objective tool for early risk assessment. Large-scale, multicenter prospective studies are required to validate its clinical applicability and robustness in the future.

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Publication Details

Journal
International Journal of Surgery
Published
2025-09-09
DOI
https://doi.org/10.1097/js9.0000000000003431
Primary Topic
Pelvic floor disorders treatments
Type
article
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0.00
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article

Fully end-to-end automated bladder segmentation and risk assessment based on bladder morphology features for predicting upper urinary tract dysfunction

Gongyue Liu, Zhonghan Zhou, Tianyu Xiang, Junjie Ji et al.
International Journal of Surgery
Pelvic floor disorders treatments
article

Fully end-to-end automated bladder segmentation and risk assessment based on bladder morphology features for predicting upper urinary tract dysfunction

Gongyue Liu, Zhonghan Zhou, Tianyu Xiang, Junjie Ji, Ping Zhong, Limin Liao, Xin Liu, Juan Wu, Y. Liu, Hang Tianyang
article en

Abstract

The bladder morphology features are robust predictors of UUTD. This fully automated, E2E bladder segmentation and UUTD risk prediction model demonstrated superior performance compared to traditional clinical parameters, providing a quantitative and objective tool for early risk assessment. Large-scale, multicenter prospective studies are required to validate its clinical applicability and robustness in the future.

International Journal of SurgeryVol. 112(1)
Capital Medical University (CN), China Rehabilitation Research Center (CN), Taishan University (CN), Beijing Dao Pei Hospital (CN)
Openalex Percentile: Top 64%
Pelvic floor disorders treatments
DOIOpen Access PDFOpenAlex
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Fully end-to-end automated bladder segmentation and risk assessment based on bladder morphology features for predicting upper urinary tract dysfunction — Gongyue Liu, Zhonghan Zhou, et al. · International Journal of Surgery (2025) | TGRS Research Map | TGRS