Wu Min, Li Zhanchi, Liu YiDong, Tan Zelong, Tang Wenjuan, Xuan Xiaoqi, Feng Hui, Lao Weihua, Ding Ning, Wang BoJun, Wang Zheyuan, Zhuang Likai
Department of Urology, Shanghai Children's Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200062, China.
Department of Urology, Shanghai Punan Hospital of Pudong New District, Punan Branch of Renji Hospital, Shanghai 200000, China.
Research (Wash D C). 2025 Jul 22;8:0771. doi: 10.34133/research.0771. eCollection 2025.
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 < 0.0001). In stage 3, the consistency of the VCUG-DAM for VUR grading was validated. VCUG-DAM improved inter-clinician agreement for VUR grading. The fully automated VCUG-DAM demonstrated high accuracy, reliability, and robustness in multitask diagnoses of urinary tract abnormalities across multiple VCUG images, while improving the diagnostic ability of clinicians as an auxiliary tool.
我们旨在开发一种排尿性膀胱尿道造影（VCUG）诊断人工智能模型（VCUG-DAM），该模型依赖于一种新颖的架构，可使用单一的VCUG图像自动分割并诊断膀胱、尿道和输尿管，同时动态评估每张图像的相对重要性。2021年至2023年期间，我们收集了来自中国15家医院1660例患者的7899张VCUG图像。在第一阶段，我们评估了VCUG-DAM模型的性能。VCUG-DAM模型在患者层面上，膀胱、尿道、左侧和右侧膀胱输尿管反流（VUR）的曲线下面积（AUC）分别为0.8772、0.7752、0.9443和0.9342。在第二阶段，我们探究了VCUG-DAM模型是否能够提高临床医生的诊断能力。VCUG-DAM提高了临床医生的诊断性能，膀胱的平均AUC从0.8185提高到0.9456，尿道从0.6507提高到0.7943，左侧VUR从0.6288提高到0.9641，右侧VUR从0.7305提高到0.9506（均P<0.0001）。在第三阶段，验证了VCUG-DAM对VUR分级的一致性。VCUG-DAM提高了临床医生之间对VUR分级的一致性。全自动的VCUG-DAM在对多张VCUG图像进行泌尿系统异常的多任务诊断中表现出高准确性、可靠性和稳健性，同时作为辅助工具提高了临床医生的诊断能力。
