通过深度学习方法在对比计算机断层扫描图像中定位结肠直肠癌病变
Prasan Kumar Sahoo1,2, Pushpanjali Gupta1, Ying-Chieh Lai3,4
1Department of Computer Science and Information Engineering, Chang Gung University, Guishan, Taoyuan 33302, Taiwan.
Bioengineering (Basel, Switzerland)
|August 26, 2023
概括
人工智能 (AI) 现在可以在未经准备的肠道CT扫描中自动检测结直肠癌. 这种人工智能模型,YOLOv8,实现了高精度,有助于早期偶然癌症检测.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 在瘤学瘤学.
背景情况:
- 腹部计算机断层扫描 (CT) 对胃肠道疾病的评估至关重要,包括结肠直肠癌的检测.
- 在CT扫描中分析结肠可能是由于其结构而具有挑战性的,可能导致错过的偶然结肠癌.
- 目前CT对结直肠癌检测的敏感性取决于瘤大小,这凸显了对改进诊断工具的需求.
研究的目的:
- 开发和评估人工智能 (AI) 模型,用于在CT图像中自动定位结直肠癌.
- 为了应对在未经准备的肠道CT扫描中检测偶然的结肠癌的挑战.
- 用CT成像提高结直肠癌查的诊断准确性和效率.
主要方法:
- 利用了来自190名结直肠癌患者的1558个瘤切片的数据集,由放射科医生和外科医生进行注释.
- 采用并比较深度学习模型,包括RetinaNet,YOLOv3和YOLOv8用于瘤定位.
- 通过结肠镜检查,体检,影像研究和手术记录验证瘤部位.
主要成果:
- 具有超参数调节的YOLOv8模型在患者智能测试中表现出强的表现.
- 获得了0.83 (±0.29) 的灵敏度,0.97 (±0.01) 的特异性和0.96 (±0.01) 的精度.
- 切片式测试得出F1分数为0.97 (±0.002) 和平均平均精度 (mAP) 为0.984.
结论:
- 人工智能,特别是YOLOv8模型,显示出在CT图像中精确定位结直肠癌的巨大潜力.
- 这种人工智能驱动的方法可以帮助检测偶然的结肠癌,改善患者的治疗结果.
- 这项研究强调了人工智能在提高CT用于结直肠癌查的诊断能力方面的价值.
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