在完全的机器人食道切除术中,使用无监督学习方法对印ocyanine绿色量化
Lukas Pollmann1, Sebastian Weberskirch1, Moritz Petry2
1Department of General, Visceral and Transplant Surgery, University Hospital Muenster, Muenster, Germany.
Surgery
|May 24, 2025
概括
使用无监督学习的定量绿色氨酸分析可以帮助预测机器人食道切除术后的门泄漏. 这种方法在复杂的手术中有望改善患者的治疗结果.
科学领域:
- 手术瘤学手术瘤学
- 医疗成像医学成像
- 人工智能在医学中的应用
背景情况:
- 术内绿色氨酸 (ICG) 成像评估在食道切除术期间胃管 perfusion.
- 通过ICG识别的延迟输液与静脉泄漏相关.
- 目前的ICG评估显示,用户间的变化很大,需要进行定量分析.
研究的目的:
- 使用无监督的,自组织地图 (SOM) 集群网络,呈现定量氨酸绿色分析.
- 评估这种定量方法在机器人辅助的最小侵入性食道切除术中的有用性.
主要方法:
- 包括70名接受机器人辅助微创食道切除术和ICG成像的患者.
- 记录的ICG成像视频使用无监督的SOM网络进行集群.
- 确定了ICG输液得分,并与静脉泄漏相关联.
主要成果:
- ICG输液得分,患者年龄和体重指数与增加的静脉漏风险相关 (单变分析).
- 伴随性疾病和动脉硬化症的严重程度在患有泄漏和没有泄漏的患者之间没有显著差异.
- 开发的定量ICG分析提供了对静脉漏的预测得分.
结论:
- 用于定量ICG分析的无监督学习可能有助于预测机器人食道切除术后的门泄漏.
- 需要在前性随机对照研究中进一步验证,以澄清临床价值.
- 这种定量方法有可能改善手术决策和患者的治疗结果.
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