人工智能模型用于检测常规腹腔皮层CT检查中的结肠直肠癌:一项培训和外部测试研究
Seung-Seob Kim1, Hyunseok Seo2, Kihwan Choi3
1Department of Radiology, Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea.
AJR. American journal of roentgenology
|February 12, 2025
概括
一个人工智能 (AI) 模型可以在常规CT扫描上检测结直肠癌 (CRC),从而有可能减少错过诊断. 这种AI工具在腹腔皮层CT检查上识别CRC方面具有显著的实用性.
科学领域:
- 放射学和医学成像学 医学成像学
- 人工智能在医学中的应用
- 在瘤学瘤学.
背景情况:
- 大肠直肠癌 (CRCs) 有时在常规腹腔皮层CT扫描期间被放射科医生遗漏.
- 需要在标准CT检查中改进CRC的检测方法.
研究的目的:
- 开发和评估一种人工智能 (AI) 模型,用于在腹腔皮层CT扫描上检测结直肠癌 (CRC).
- 评估人工智能模型在检测CT可见CRC而不进行肠道准备时的性能.
主要方法:
- 一项涉及3945名患者的回顾性研究,包括一组培训和内部/外部测试.
- 一个基于变压器的物体检测网络被调整为创建用于自动CRC检测的AI模型.
- 用ROC分析,灵敏度和特异性来评估AI性能,并与放射科医生读者进行比较.
主要成果:
- 人工智能模型的AUC达到0.867 (内部) 和0.808 (外部测试集).
- 敏感度为79.6% (内部) 和80.8% (外部),特异性为91.2% (内部) 和90.9% (外部).
- 人工智能模型检测到放射科医生错过的CRC,并在某些指标上显示出与人类读者相比或更高的性能.
结论:
- 开发的AI模型在常规腹腔皮层CT检查上对结直肠癌的自动检测具有显著的实用性.
- 这种人工智能工具有可能降低临床实践中错过CRC的比率.
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