基于深度学习的对活体肝脏供体CT血管图的综合评估:从血管细分到体积分析
Namkee Oh1, Jae-Hun Kim2, Jinsoo Rhu1
1Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul.
International journal of surgery (London, England)
|June 13, 2024
概括
深度学习模型为活体供体肝移植提供了一致和高效的肝体积评估,改善了手术前规划和供体安全. 这种人工智能工具与实际的移植重量有很强的相关性.
科学领域:
- 医疗成像医学成像
- 人工智能在医学中的应用
- 手术规划 手术规划
背景情况:
- 准确的肝体积和血管系统评估对于活体供体肝移植 (LDLT) 的安全性和外科结果至关重要.
- 传统的手动细分方法耗时且容易变化.
- 深度学习 (DL) 模型为一致和高效的体积评估提供了一个有希望的替代方案.
研究的目的:
- 开发和评估一种深度学习 (DL) 模型,用于精确的手术前评估活体肝脏捐赠者的肝脏体积和血管.
- 为了比较DL模型的性能与外科医生的传统手动细分.
主要方法:
- 一个3D残余的U-Net深度学习模型被开发和训练使用活体肝脏捐赠者的手术前CT血管学数据.
- DL模型对肝叶和血管进行了细分,计算了肝脏体积.
- 通过比较DL衍生体积和血管细分与外科医生的手动细分以及实际的移植重量来评估性能.
主要成果:
- DL模型的准确性很高,右叶子的子相似系数为0.94,左叶子为0.91.
- 来自DL模型的肝体积估计与外科医生的评估非常相匹配 (平均差异为9.18毫升).
- 与外科医生的估计 (R-平方:0.68) 相比,DL模型与实际移植重量 (R-平方:0.76) 的相关性更强.
结论:
- 开发的DL模型是提高肝移植手术前规划的可靠工具.
- 与手动方法相比,该模型在体积评估中提供了更好的一致性和效率.
- 需要进一步验证,以确认DL模型在各种临床环境中的通用性.
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