在甲状腺切除术中,一种新的自动化副甲状腺检测和细分方法在甲状腺切除术中
Fan Yu1, Xiaolei Yi2, Zihan Lin3
1Department of Nuclear Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
BMC surgery
|February 12, 2026
概括
一种新的深度学习方法,PG-AI,有助于在甲状腺切除术期间识别副甲状腺 (PG). 这种人工智能工具提供了准确的实时视觉指导,改善了手术期间的PG保存.
科学领域:
- * 医学成像和人工智能.
- * 手术技术的进步.
背景情况:
- * 在甲状腺切除术期间保护副甲状腺 (PGs) 是至关重要的,但具有挑战性.
- * 深度学习 (DL) 显示出增强操作内决策的潜力.
研究的目的:
- * 引入PG-AI,这是一个基于DL的新型系统,用于手术内侧甲状腺腺体的识别.
- *与现有方法和外科专业知识相比,评估PG-AI的性能.
主要方法:
- * 开发PG-AI,集成YOLOX本地化子网和Trans-U-HRNet细分模型.
- *培训和验证来自121名接受开放性甲状腺切除术的患者的976张手术内图像.
- * 用近红外自光 (NIRAF) 成像和外科医生评估进行比较分析.
主要成果:
- *PG-AI在内部验证中实现了91.1%的准确性和86.5%的回忆.
- *可视化识别率为88.7% (内部) 和85.0% (外部).
- *与初级外科医生相比,PG-AI表现优越,与NIRAF达成72.1%的一致意见.
结论:
- *PG-AI在手术期间为PG提供准确的实时细分面具.
- * 该系统提供可靠的手术内视觉指导,帮助外科医生识别和保存PG.
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