对一个增强的ResNet-18分类模型的评估,用于呼吸系统细胞学中的快速现场诊断
Wei Gong1, Deep K Vaishnani2, Xuan-Chen Jin3
1Department of Pathology, Lishui Municipal Central Hospital, Lishui, 323000, Zhejiang Province, China.
BMC cancer
|January 3, 2025
概括
一个新的AI模型有助于从Diff-Quik染色的呼吸系统细胞学样本诊断肺癌,显示与人类专家相似的准确性,并在一起使用时提高诊断效率.
科学领域:
- 肺部医学 肺部医学
- 计算病理学计算病理学
- 医疗保健中的人工智能
背景情况:
- 快速现场评估 (ROSE) 对于肺癌诊断至关重要.
- 中国面临的挑战包括有限的Diff-Quik染色熟悉度和细胞病理学家短缺.
- 为ROSE开发人工智能工具在临床上具有价值.
研究的目的:
- 开发一种改进的深度学习模型,用于在ROSE期间评估Diff-Quik染色呼吸系统细胞学样本.
- 与人类细胞病理学家相比,评估AI模型的诊断性能.
- 评估人工智能协助对诊断效率的影响.
主要方法:
- 对116张数字Diff-Quik染色细胞学图像进行了回顾性分析.
- 包括6个诊断类别:癌性,正常,腺癌,状细胞癌,非小细胞癌和小细胞癌.
- 对比人工智能系统的性能与3位具有不同经验水平的细胞病理学家.
主要成果:
- 细胞病理学家的准确性因经验和医院环境而异.
- 人工智能模型的诊断能力与人类专家相提并论.
- 人工智能协助与人类专业知识相结合,提高了诊断效率.
结论:
- 深度学习模型显示出作为呼吸系统细胞学现场诊断的辅助工具的潜力.
- 在细胞诊断过程中,人类的专业知识仍然是不可或缺的.
- 人工智能可以增强,而不是取代ROSE中细胞病理学家的作用.
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