使用卷积神经网络预测一次性灵活尿道镜图像中的尿石组成
Kyung Tak Oh1, Dae Young Jun1, Jae Young Choi2
1Department of Urology, Severance Hospital, Urological Science Institute, Yonsei University College of Medicine, Seoul 03722, Republic of Korea.
Medicina (Kaunas, Lithuania)
|August 26, 2023
概括
一个卷积神经网络 (CNN) 从一次性灵活尿道镜 (fURS) 捕获的低分辨率图像中准确预测了尿路结石的组成. 这种人工智能应用程序有望改善尿病治疗,尽管成像能力有限.
科学领域:
- 泌尿器科 泌尿器科 泌尿器科 泌尿器科
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 尿石成分分析对于有效的尿病管理至关重要.
- 单次使用的灵活尿道镜 (fURS) 提供了财务优势,但与可重复使用的镜头相比,图像质量较差.
- 直接从fURS图像中预测石材成分可以增强治疗策略.
研究的目的:
- 评估卷积神经网络 (CNN) 在使用一次性fURS.图像预测尿路结石组成方面的有效性.
- 评估AI在分析低分辨率内镜图像以诊断尿病的性能.
主要方法:
- 从一次性使用的fURS lithotripsy中对207张手术图像进行了回顾性分析.
- 使用Resnet-18进行转移学习,在内镜图像和石头分类数据上训练CNN模型的开发.
- 用于模型培训和验证,将石头分为和非类.
主要成果:
- 美国有线电视新闻网 (CNN) 在预测石材成分方面取得了81.8%的整体准确率.
- 在组中观察到高性能 (回忆率:88.2%,精度:88.2%).
- 该模型表现出良好的分类性能,ROC曲线下的面积为0.82.2.
结论:
- 人工智能,特别是CNN,即使使用一次性fURS的低分辨率图像,也可以有效地预测尿路结石的组成.
- 这项研究是第一个将人工智能应用于一次性fURS图像的研究,证明了它在具有挑战性的内镜条件下的潜力.
- 这些发现表明,人工智能在泌尿病学中具有更广泛的应用,有可能改善诊断能力和治疗结果.
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