人工智能用于预防和预测结直肠瘤
Kohjiro Tokutake1, Aaron Morelos-Gomez2, Ken-Ichi Hoshi3
1Department of Gastroenterology, Nagano Red Cross Hospital, 5-22-1 Wakasato, Nagano, 380-8582, Japan. k.tokutake@nagano-rch.jp.
Journal of translational medicine
|July 3, 2023
概括
人工智能 (AI) 可以使用非侵入性数据预测结直肠瘤. 这种机器学习 (ML) 模型提供了比传统查更好的歧视,帮助资源有限的医疗保健.
科学领域:
- 在瘤学瘤学.
- 医疗信息学 医疗信息学
- 人工智能的人工智能
背景情况:
- 结肠镜是癌症查的重要工具,但在资源有限的地区,其广泛使用是有限的.
- 需要使用非侵入性查方法来确定需要结肠镜检查的个体.
- 这项研究探讨了人工智能 (AI) 预测结直肠瘤的潜力.
研究的目的:
- 研究人工智能在预测结直肠瘤方面的有效性.
- 开发一种针对结直肠癌的非侵入性查方法.
- 评估机器学习模型在识别结直肠多瘤方面的性能.
主要方法:
- 利用身体检查和血液分析的数据来预测结直肠多发生率.
- 采用基于内核密度估计器 (KDE) 的转换来提高类分离性.
- 开发和评估最佳的机器学习 (ML) 模型来检测聚.
主要成果:
- ML模型实现了马修斯相关系数 (MCC),男性为0.37,女性为0.39.
- 与便隐性血液检测相比,这些模型显示出明显更高的歧视性 (男性MCC0.047,女性MCC0.074).
- 通过适当的多体大小值来评估性能.
结论:
- 开发的ML模型可以指导进一步的结直肠查决策,并估计腺瘤大小.
- KDE特征转换可以对生物标志物和生活方式因素进行评分,以建议针对腺瘤生长的干预措施.
- 人工智能集成可以减少医疗保健提供者的工作量,支持资源稀缺的系统,并通过风险分层优化查结肠镜效率.
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