计算机辅助诊断使用大规模的视觉语言模型在消化内镜的查中
Yilin Li1, Zhonghua Du2, Renbo Li1
1School of Control Science and Engineering, Shandong University, Jinan, 250012, Shandong, China.
Journal of imaging informatics in medicine
|August 12, 2025
概括
人工智能 (AI) 通过分析图像,在消化内镜中帮助计算机辅助诊断 (CAD). 这种人工智能模型提高了诊断的准确性和速度,协助医疗保健专业人员进行查计划.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 胃肠病学 胃肠病学
背景情况:
- 消化道疾病的高患病率需要在中国进行大规模的内镜查.
- 人员与患者的比例有限,在内镜查计划中造成了诊断工作负载的挑战.
研究的目的:
- 开发和评估一种基于人工智能的计算机辅助诊断 (CAD) 方法,用于消化内镜查.
- 利用预先训练的视觉语言模型来加强内镜图像的分类.
主要方法:
- 开发了一种使用预先训练的大规模视觉语言模型的CAD方法.
- 该模型融合了视觉和文本信息,用于对内镜图像进行分类.
- 训练数据包括19种疾病类别和4种正常情况的41,191张多中心图像.
主要成果:
- 拟议的人工智能模型在比较评估中表现优于八种最先进的模型.
- 在人类-人工智能合作中,与经验丰富的内镜师相比,该模型显示出优越的诊断性能.
- 人工智能协助导致诊断任务的决策时间更快.
结论:
- 人工智能驱动的CAD方法对于分析大规模内镜数据集是有效的.
- 人工智能有可能显著提高消化内镜的诊断效率和准确性.
- 这项研究推进了人工智能辅助诊断,朝着人工智能驱动的内镜临床现实迈进.
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