应用深度学习算法解读脏,尿道和膀 (KUB) 的X射线来检测结肠癌
Ling Lee1, Chin Lin1,2,3, Chia-Jung Hsu3,4
1School of Medicine, National Defense Medical Center, Taipei, R.O.C, Taiwan.
Journal of imaging informatics in medicine
|November 1, 2024
概括
这项研究开发了一种使用KUBX射线检测结直肠癌 (CRC) 的AI模型,提供了一个具有成本效益的查替代方案. 人工智能在识别CRC,特别是高等级病例方面显示出潜力,并可能改善查可访问性.
科学领域:
- 医疗成像中的人工智能
- 在瘤学瘤学.
- 放射学 放射学是一门学科.
背景情况:
- 早期结肠直肠癌 (CRC) 查对于降低死亡率至关重要.
- 目前的方法,如结肠镜检查,患者的遵从性很低,并且具有侵入性.
- 使用人工智能的先进成像显示出希望,但在采购和成本方面面临挑战.
研究的目的:
- 开发一种深度学习模型 (DLM) 用,尿道和膀 (KUB) 放射图来检测CRC.
- 评估DLM在广泛可访问的KUBX射线上检测CRC的性能.
主要方法:
- 一项回顾性研究使用了来自两个医院队列 (开发,调整,内部和外部验证集) 的KUB放射图.
- 一个121层的DenseNet卷积网络被训练用于CRC检测.
- 模型性能使用接收器操作特征曲线,灵敏度,特异性和AUC进行评估.
主要成果:
- 在内部验证中,DLM的AUC达到0.738 (灵敏度61.3%,特异性74.4%),在外部验证中达到0.656 (47.7%,72.9%).
- 该模型在高档CRC中表现出更好的性能.
- 人工智能阳性预测与长期增加的全因死亡风险相关.
结论:
- 人工智能增强的KUBX射线分析提供了一种潜在的成本效益高的方法,以提高CRC查覆盖率和有效性.
- 这种方法可以作为传统查方法的替代方案.
- 为了临床整合,还需要进行进一步的前性研究.
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