开发和评估USCnet:一种基于人工智能的模型,用于在手术前预测传染性和非传染性尿病
Jiexin Pan1, Haodong Chen1, Chen Huang1
1Department of Urology, The Second Affiliated Hospital of The Chinese University of HongKong/Longgang District People's Hospital of Shenzhen, Shenzhen, Guangdong, 518172, China.
World journal of urology
|February 27, 2025
概括
这项研究开发了一种人工智能模型,用于在手术前预测传染性和非传染性尿病. 该模型使用临床数据和CT扫描准确区分石头类型,改善患者管理.
科学领域:
- 泌尿器科 泌尿器科 泌尿器科 泌尿器科
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 尿结石 (结石) 是常见和复发的,需要准确的手术前诊断才能有效治疗.
- 目前的方法依赖于术后分析,延迟关键的治疗决策并影响结果.
- 在手术前预测石头类型对于优化尿病的临床管理至关重要.
研究的目的:
- 开发和验证人工智能 (AI) 模型,用于在手术前预测传染性与非传染性尿病.
- 通过使用综合的临床和成像数据,提高结石组成的诊断准确度.
- 为改善手术规划和尿病患者护理提供一个工具.
主要方法:
- 训练并验证了一种AI模型,使用了来自642名尿病患者 (2014年12月 - 2021年11月) 的临床数据和CT成像.
- 集成的视觉和文本转换 (VTT) 和多模式细分注意力融合 (MSAF) 模块用于增强分析.
- 采用多式联络方法,将成像和临床参数结合起来进行预测.
主要成果:
- 人工智能模型在区分石头类型方面实现了79.66%的分类精度和86.74%的曲线下面积 (AUC).
- 与传统的诊断方法和传统的ResNet模型相比,其表现优越.
- 包括临床参数显著提高了该模型对尿病的预测准确度.
结论:
- 开发的AI模型提供了一种高效的方法,用于手术前的尿病类型识别.
- 促进了关于手术策略和术后管理的知情临床决策.
- 代表了泌尿外科实践的宝贵工具,特别是在资源有限的环境中.
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