深度学习方法用于使用CT成像来区分不确定的上腺质量
Yashbir Singh1, Zachary S Kelm1, Shahriar Faghani1
1Department of Radiology, Mayo Clinic, Rochester, MN, USA.
Abdominal radiology (New York)
|June 27, 2023
概括
深度学习使用CT扫描精确区分上腺皮质癌 (ACC) 和大上腺腺瘤 (LPAA). 这种人工智能方法有望提高上腺质量评估中的诊断准确性.
科学领域:
- 放射学 放射学是一门学科.
- 人工智能的人工智能
- 在瘤学瘤学.
背景情况:
- 区分1-2期上腺皮质癌 (ACC) 和大,脂质贫乏的上腺腺瘤 (LPAA) 是具有挑战性的,因为CT扫描上重叠的成像特征.
- 准确的区分对于适当的患者管理和治疗规划至关重要.
研究的目的:
- 通过使用单个时间点CT图像,研究深度学习模型在区分1-2阶段ACC和LPAA之间的有效性.
- 为了评估这个分类任务的3D Densenet121模型的诊断性能.
主要方法:
- 一项回顾性队列研究包括48名患有1-2期ACC的患者和43名患有LPAA (>3厘米) 的患者.
- 单个时间点对比度增强的CT图像被用作3D Densenet121深度学习模型的输入.
- 使用五倍交叉验证来评估模型性能,报告了以准确度为重点和灵敏度为重点的检查点.
主要成果:
- 专注于灵敏度的深度学习模型实现了平均准确率为87.2%和100%的灵敏度.
- 这种以精度为重点的模型实现了91%的平均精度和96%的灵敏度.
- 深度学习模型在区分ACC和LPAA方面表现出很高的表现.
结论:
- 深度学习模型显示出在单次点CT图像上区分ACC与大型LPAA的巨大潜力.
- 在广泛采用这种人工智能工具之前,需要进一步的多中心和外部验证.
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