一种深度学习方法来优先考虑结直肠活检的病例
Ciara D White1,2, Runjan Chetty2, John Weldon2
1Department of Histopathology, Mater Misericordiae University Hospital, Dublin, Ireland.
一个新的人工智能 (AI) 模型有效地检测出异常的结直肠组织学,包括癌症和发育不良. 这种人工智能工具通过优先考虑活检,改善诊断工作流程来协助病理学家.
科学领域:
- 数字病理学数字病理学
- 人工智能在组织病理学中的应用
- 结肠直肠癌查 结肠直肠癌查
背景情况:
- 准确和及时诊断结直肠组织学对于患者的治疗结果至关重要.
- 传统的组织病理学分析可能会耗时,并且会受到观察者之间的变化.
- 人工智能 (AI) 具有提高病理学诊断准确性和效率的潜力.
研究的目的:
- 开发和验证一个弱监督的AI模型,用于检测异常结直肠组织学.
- 根据临床意义优先考虑结直肠活检,区分瘤和非瘤发现.
- 评估人工智能模型与数字病理学工作流程的整合.
主要方法:
- 一个弱监督的深度学习模型,Triagnexia Colorectal,在24983张数字化H&E染色的整张幻灯片图像上进行了训练.
- 该模型由多名病理学家在模拟的数字病理学环境中进行了评估.
- 开发了一个带有图形用户界面的AI应用程序,以简化决策.
主要成果:
- 人工智能模型在验证队列中实现了高性能,在第一个队列 (n=100) 上的微平均特异性为0.984和灵敏度为0.949.
- 第二个多机构队列 (n=101) 显示了可比结果,微平均特异性为0.978和灵敏度为0.931.
- 病理学家报告了AI工具的准确性,实用性和易于集成的积极反.
结论:
- 一个高性能的人工智能分类模型用于结直肠活检已经成功开发.
- 人工智能模型可以集成到常规的数字病理学工作流程中.
- 这种人工智能工具可以帮助病理学家确定病例的优先级,并识别显著的异常,如发育不良和癌症.
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