从内镜视频中,可解释的机器学习来评估赛马的上呼吸道
Anas Mohammed Tahir1, Li Guo1, Rabab K Ward1
1Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada.
Computers in biology and medicine
|August 22, 2024
概括
这项研究介绍了一种可解释的AI工具,用于使用内镜视频在马中准确诊断喉半 (LH). 该系统提高了兽医的诊断效率和可靠性.
科学领域:
- 兽医医学 兽医医学 兽医医学
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 喉半 (LH) 是一个常见的上呼吸道 (URT) 问题在赛马.
- 手动评估URT内镜视频是主观的,并因视频质量差而受到挑战.
- 需要客观和高效的诊断工具来准确分类LH.
研究的目的:
- 开发和验证一个可解释的机器学习 (ML) 模型,用于自动化喉半的分级.
- 提高URT评估在马匹实践中的效率和客观性.
- 为兽医提供临床相关和可解释的辅助工具.
主要方法:
- 一个级联的YOLOv8架构被用于语义细分和地标提取从URT内镜.
- 来自地标的时空特征被输入到决策树 (DT) 模型中,用于LH分类 (1-4级).
- 该模型使用5倍交叉验证和外部数据集进行了验证,其中包括可解释性特征.
主要成果:
- ML模型在内部和外部验证数据集中对LH等级进行分类时显示出高灵敏度.
- 对于不同的LH等级,灵敏度值在90%至100%之间.
- 可解释性功能有助于错误检测,视频质量评估和边界病例的识别.
结论:
- 拟议的可解释的ML框架为马喉半症诊断提供了一个强大而有效的解决方案.
- 该工具通过提供准确,可解释和临床相关的评估来帮助兽医.
- 这种方法解决了手动分级的局限性,并提高了马匹呼吸道诊断的诊断能力.
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