一个可解释的人工智能对拉曼光谱进行分析,以诊断甲状腺癌
Loredana Bellantuono1,2, Raffaele Tommasi1, Ester Pantaleo2,3
1Dipartimento di Biomedicina Traslazionale e Neuroscienze (DiBraiN), Università degli Studi di Bari Aldo Moro, 70124, Bari, Italy.
Scientific reports
|October 3, 2023
概括
拉曼光谱法是一种非侵入性方法,在诊断甲状腺癌方面表现有前途. 机器学习模型使用光谱数据准确地区分恶性和良性结节,为改进诊断铺平了道路.
科学领域:
- 生物医学光学 生物医学光学
- 频谱学是一种光谱学.
- 机器学习在医学中的应用
背景情况:
- 拉曼光谱为疾病检测提供非侵入性,无标签的生物化学分析.
- 目前用于甲状腺结节的诊断方法可能是侵入性的,可能导致不必要的手术.
- 识别显著的光谱模式仍然是拉曼光谱在临床应用中的挑战.
研究的目的:
- 开发和验证机器学习 (ML) 程序,以使用拉曼光谱来区分良性和恶性甲状腺结节.
- 通过可解释的人工智能 (XAI) 提高基于ML的诊断模型的可解释性.
主要方法:
- 从组织学甲状腺样本收集拉曼光谱.
- 一种数据驱动,标签独立的方法来选择显著的光谱峰值.
- 训练ML算法在所选的峰值的相对突出性.
- 应用XAI技术来解释模型预测.
主要成果:
- 开发的ML模型实现了接收器操作特征曲线 (AUC) 下的面积超过0.9,证明了高诊断性能.
- 这些模型有效地区分了健康/良性和恶性甲状腺结节.
- XAI提供了关于个体光谱特征对诊断预测的贡献的见解.
结论:
- 提出的基于ML的拉曼光谱方法是用于甲状腺癌诊断的高度有效和可解释的工具.
- 这种方法有可能减少误诊率,避免不必要的手术干预.
- 在临床环境中进一步验证可以将拉曼光谱作为标准诊断技术.
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