使用模糊深度学习来估计口腔癌的存活率
Rachasak Somyanonthanakul1, Kritsasith Warin2, Sitthi Chaowchuen3
1College of Digital Innovation Technology, Rangsit University, Pathum Thani, Thailand.
BMC oral health
|May 2, 2024
概括
一个新的模糊深度学习 (FDL) 模型显著提高了口腔癌存活时间预测的准确性. 这种人工智能方法使用患者临床病理学数据提高了预后,为口腔癌患者提供了更好的结果.
科学领域:
- 在瘤学瘤学.
- 人工智能的人工智能
- 医疗信息学 医疗信息学
背景情况:
- 口腔癌是一个重大的全球健康挑战,导致大量的发病率和死亡率.
- 准确的生存时间估计对于有效的口腔癌患者管理至关重要.
研究的目的:
- 开发和评估一种新的模糊深度学习 (FDL) 模型,用于预测口腔癌患者的生存时间.
- 评估将模糊逻辑与深度学习相结合的有效性,以提高预后准确性.
主要方法:
- 从2011年至2019年期间接受治疗的581名口腔状细胞癌 (OSCC) 患者的临床病理数据的回顾性分析.
- 开发一个深度学习 (DL) 模型用于生存时间分类.
- 将模糊逻辑集成到DL模型中,以创建基于FDL的生存时间估计模型.
主要成果:
- 在估计生存时间类时,FDL模型实现了0.97的显著准确性.
- 该FDL模型显示了1.00的接收机操作特征 (AUC) 曲线下的完美面积.
- FDL模型显著优于传统DL模型 (精度为0.74,AUC为0.84-1.00).
结论:
- 将模糊逻辑集成到深度学习模型中,大大提高了口腔癌生存时间预测的准确性.
- FDL模型提供了一个有前途的工具,可以通过使用现有的临床病理学数据来提高预后准确性.
- 这种人工智能驱动的方法有可能改进治疗策略并改善口腔癌护理中的患者结果.
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