动态多图像权重用于自动检测和诊断异常的尿路在空白的cystourethrography与深度学习系统:一个回顾性,大规模,多中心研究研究
Min Wu1, Zhanchi Li2, YiDong Liu3
1Department of Urology, Shanghai Children's Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200062, China.
Research (Washington, D.C.)
|July 23, 2025
概括
一个新的人工智能模型,VCUG-DAM,准确地诊断出膀和尿道异常从空白囊回路图像. 这种人工智能工具显著提高了临床医生的诊断能力,并提高了膀外流 (VUR) 的分级一致性.
科学领域:
- 医疗成像医学成像
- 人工智能在医学中的应用
- 泌尿器科 泌尿器科 泌尿器科 泌尿器科
背景情况:
- 排空囊回法 (VCUG) 对于诊断尿路异常,特别是膀回流 (VUR) 非常重要.
- 对VCUG图像的准确和一致的解释对于有效的患者管理至关重要.
- 自动诊断工具有可能提高放射学中的效率和准确性.
研究的目的:
- 开发和评估一种新的人工智能模型 (VCUG-DAM),用于使用VCUG图像进行自动诊断.
- 评估模型在分段和诊断膀,尿道和尿道方面的表现.
- 确定AI模型是否可以提高临床医生对VUR的诊断准确性和一致性.
主要方法:
- 一个深度学习模型 (VCUG-DAM) 使用一种新的架构开发,用于VCUG图像上的多任务诊断.
- 该模型在15家医院的1,660名患者的7,899张VCUG图像上进行了训练和验证.
- 通过将人工智能模型的诊断与临床诊断进行比较,并评估其对临床医生的表现的影响来评估性能.
主要成果:
- VCUG-DAM实现了高诊断性能,AUC为0.8772 (膀),0.7752 (尿道),0.9443 (左侧VUR) 和0.9342 (右侧VUR).
- 人工智能模型在所有评估指标 (膀,尿道,左VUR,右VUR;所有P<0.0001) 上显著提高了临床医生的诊断性能.
- VCUG-DAM加强了对VUR分级的临床间协议,证明了其稳定性和可靠性.
结论:
- 自动化VCUG-DAM在从VCUG图像中诊断尿路异常方面表现出高准确度,可靠性和稳定性.
- 作为一个有效的辅助工具,VCUG-DAM显著提高了临床医生的诊断能力和一致性.
- 这种AI模型有望在临床实践中推进VCUG研究的解释.
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