Evidence map›Paper›PMID 40698328›Full record

ArticleResearch (Washington, D.C.)2025

Dynamic Multi-Image Weighting for Automated Detection and Diagnosis of Abnormal Urinary Tract on Voiding Cystourethrography with a Deep Learning System: A Retrospective, Large-Scale, Multicenter Study.

Min Wu, Zhanchi Li, YiDong Liu, Zelong Tan, Wenjuan Tang, Xiaoqi Xuan, Hui Feng, Weihua Lao, Ning Ding, BoJun Wang and 2 more

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In one paragraph

Article in Research (Washington, D.C.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Min WuDepartment of Urology, Shanghai Children's Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200062, China.ORCID https://orcid.org/0009-0001-7183-0023
Zhanchi LiDepartment of Urology, Shanghai Punan Hospital of Pudong New District, Punan Branch of Renji Hospital, Shanghai 200000, China.
YiDong LiuDepartment of Urology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Zelong TanDepartment of Electronic Engineering, Tsinghua University, Beijing, China.
Wenjuan TangDepartment of Radiology, Shanghai Children's Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200062, China.
Xiaoqi XuanDepartment of Urology, Wuxi Children's Hospital, Wuxi, China.
Hui FengDepartment of Urology, The Sixth Affiliated Hospital of Harbin Medical University, Harbin, China.
Weihua LaoDepartment of Urology, Guangdong Women and Children Hospital, Guangzhou, China.
Ning DingDepartment of Urology, Jiangxi Provincial Children's Hospital, Nanchang, China.
BoJun WangDepartment of Radiology, Children's Hospital of Fudan University, Shanghai 201102, China.
Zheyuan WangDepartment of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
Likai ZhuangDepartment of Urology, Children's Hospital of Fudan University, Shanghai 201102, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We aimed to develop a voiding cystourethrography (VCUG) diagnostic artificial intelligence model (VCUG-DAM), which relies on a novel architecture to automatically segment and diagnose the bladder, urethra, and ureters using a single VCUG image, while dynamically assessing the relative importance of each image. A total of 7,899 VCUG images from 1,660 patients across 15 Chinese hospitals were collected between 2021 and 2023. In stage 1, we assessed the performances of the VCUG-DAM model. The patient-level area under the curve (AUC) of VCUG-DAM was 0.8772, 0.7752, 0.9443, and 0.9342 for bladder, urethral, left, and right vesicoureteral reflux (VUR), respectively. In stage 2, we explored whether the VCUG-DAM model could improve the diagnostic ability of clinicians. VCUG-DAM improved the clinician's diagnostic performance, with mean AUCs increasing from 0.8185 to 0.9456 for the bladder, 0.6507 to 0.7943 for the urethra, 0.6288 to 0.9641 for the left VUR, and 0.7305 to 0.9506 for the right VUR (all

Identifiers

PMID40698328
PMCPMC12280329

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.