基于OCT的青光眼和青光眼阶段的诊断使用可解释的机器学习.
Md Mahmudul Hasan1, Jack Phu2,3,4,5, Henrietta Wang2,3,5
1School of Computer Science and Engineering, University of New South Wales, Sydney, NSW, Australia. md_mahmudul.hasan@unsw.edu.au.
Scientific reports
|January 28, 2025
概括
这项研究介绍了一种可解释的AI工具,用于使用光学连贯性断层扫描 (OCT) 图像诊断青光眼. 该工具提高了诊断的准确性,并为眼科专业人员提供了透明的见解.
科学领域:
- 眼科医生 眼科 眼科
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 玻璃眼是一个显著且日益增长的全球健康问题.
- 目前的自动化青光眼诊断依赖于不透明的深度学习模型,阻碍了信任和临床采用.
- 人工智能诊断工具缺乏解释性限制了它们的临床适用性和可靠性.
研究的目的:
- 开发和验证一种可解释的人工智能 (XAI) 工具,用于使用光学连贯断层扫描 (OCT) 图像诊断和分期眼.
- 通过提供可解释的诊断见解,增强AI在眼科中的临床适用性.
- 为了提高自动青光眼检测的准确性和可靠性.
主要方法:
- 使用光学连贯断层扫描 (OCT) 图像从334个正常的眼睛和268个青光眼睛 (按严重程度分类).
- 采用信号处理理论和严格评估的模型解释性.
- 在机器学习 (ML) 模型中开发了一种集成夏普利增量扩展 (SHAP) 的新型算法,用于特征排名和部分依赖性分析 (PDA),用于机器学习 (ML) 模型中的决策边界估计.
主要成果:
- 机器学习模型实现了高AUC值:早期发现0.96,中度发现0.98,高级发现1.00.
- 与临床医生相比,开发的XAI工具显示出更高的诊断准确性,特别是在早期的玻璃眼病中 (10.4-11.2%更高的准确性).
- SHAP和PDA分析提供了全球特征排名和估计的决策边界切线,确保了模型的透明度.
结论:
- 开发的XAI工具提供了一种透明和可解释的方法来诊断和分阶段使用OCT数据.
- 这种用户友好的软件有可能在临床决策中显著帮助眼科医生.
- 该XAI工具超越了传统的机器学习和临床医生的表现在早期发现青光眼,改善患者的结果.
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