■排尿膀胱尿道造影(VCUG)是诊断和分级膀胱输尿管反流(VUR)的金标准。然而,来自排尿膀胱尿道图的VUR分级是高度主观的,可靠性低。这项研究旨在开发一种深度学习模型,以提高VCUG上VUR分级的可靠性,并将其性能与临床医生的性能进行比较。
■在这项中国的回顾性研究中,VCUG图像是在2019年1月至2022年9月期间从我们的机构收集的,作为用于训练的内部数据集,以及4个外部数据集作为用于验证的外部测试集。样本分为训练集(N=1000)和验证集(N=500),内部测试集(N=168),和外部测试集(N=280)。基于集成学习的模型,深VCUG,使用Res-Net101和投票方法来预测VUR等级。使用热图评估了分级性能,接收器工作特性曲线下面积(AUC),灵敏度,特异性,准确度,内部和外部测试集中的F1分数。在外部测试集中探索了有和没有Deep-VCUG辅助预测VUR等级的四名临床医生(2名儿科泌尿科医师和2名放射科医师)的表现。
■总共收集了1948张VCUG图像(内部数据集=1668;多中心外部数据集=280)。为了评估单边VUR分级,深度VCUG在内部和外部测试集中实现了0.962(95%置信区间[CI]:0.943-0.978)和0.944(95%[CI]:0.921-0.964)的AUC,分别,对于双边VUR分级,Deep-VCUG还实现了0.960(95%[CI]:0.922-0.983)和0.924(95%[CI]:0.887-0.957)的高AUC。使用投票方法的Deep-VCUG模型在基于VCUG图像的分类方面优于单个模型和临床医生。此外,在Dee-VCUG的协助下,初级和高级临床医生的分类能力明显提高。
■Deep-VCUG模型是一个可推广的模型,目标,和基于VCUG成像的膀胱输尿管反流分级的准确工具,并对临床医生对VUR分级适用性有很好的帮助。
■本研究得到了中国自然科学基金的资助,上海医学院“福清学者”学生科研计划,复旦大学,大湾区精准医学研究院(广州)项目。
UNASSIGNED: Voiding cystourethrography (VCUG) is the gold standard for the diagnosis and grading of vesicoureteral reflux (VUR). However, VUR grading from voiding cystourethrograms is highly subjective with low reliability. This
study aimed to develop a deep learning model to improve reliability for VUR grading on VCUG and compare its performance to that of clinicians.
UNASSIGNED: In this retrospective
study in China, VCUG images were collected between January 2019 and September 2022 from our institution as an internal dataset for training and 4 external data sets as external testing set for validation. Samples were divided into training (N = 1000) and validation sets (N = 500), internal testing set (N = 168), and external testing set (N = 280). An ensemble learning-based model, Deep-VCUG, using Res-Net 101 and the voting methods was developed to predict VUR grade. The grading performance was assessed using heatmaps, area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, and F1 score in the internal and external testing set. The performances of four clinicians (2 pediatric urologists and 2 radiologists) with and without the Deep-VCUG assisted to predict VUR grade were explored in external testing sets.
UNASSIGNED: A total of 1948 VCUG images were collected (Internal dataset = 1668; multi-center external dataset = 280). For assessing unilateral VUR grading, the Deep-VCUG achieved AUCs of 0.962 (95% confidence interval [CI]: 0.943-0.978) and 0.944 (95% [CI]: 0.921-0.964) in the internal and external testing sets, respectively, for bilateral VUR grading, the Deep-VCUG also achieved high AUCs of 0.960 (95% [CI]: 0.922-0.983) and 0.924 (95% [CI]: 0.887-0.957). The Deep-VCUG model using voting method outperformed single model and clinician in terms of classification based on VCUG image. Moreover, Under the Dee-VCUG assisted, the classification ability of junior and senior clinicians was significantly improved.
UNASSIGNED: The Deep-VCUG model is a generalizable, objective, and accurate tool for vesicoureteral reflux grading based on VCUG imaging and had good assistance with clinicians to VUR grading applicability.
UNASSIGNED: This
study was supported by Natural Science Foundation of China, \"Fuqing Scholar\" Student Scientific Research Program of Shanghai Medical College, Fudan University, and the Program of Greater Bay Area Institute of Precision Medicine (Guangzhou).