以人工智能为辅助的副甲状腺识别手术内成像的诊断性能:系统性审查
Daniel Gomez-Carrillo1, Raj Roy1, Azeem Izhar1
1Department of Surgery, University of Alabama at Birmingham, Birmingham, AL, USA.
American journal of surgery
|February 6, 2026
概括
人工智能 (AI) 计算机视觉系统对外甲状腺在手术期间的识别非常有希望,提高了准确性,并可能减少像低血症和神经损伤这样的并发症.
科学领域:
- 手术技术 手术技术
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 在外科手术中发现副甲状腺是具有挑战性的,通常会导致低血症和神经损伤.
- 基于人工智能 (AI) 的计算机视觉系统提供了一个潜在的解决方案.
研究的目的:
- 系统地审查人工智能模型的文献,用于手术内甲状腺局部化和可行性评估.
- 评估AI系统在甲状腺外科手术中的诊断性能.
主要方法:
- 根据PRISMA指南进行了全面的文献搜索.
- 包括使用机器学习或深度学习模型进行甲状腺识别的研究.
- 评估了各种成像方式,包括近红外自光学,RGB视频和氨酸绿色血管学.
主要成果:
- 十二项研究符合纳入标准,重点关注甲状腺附带腺体的局部化,鉴定和 perfusion 评估.
- 人工智能系统表现出高的诊断准确性,敏感性和特异性通常超过了副甲状腺识别的85%.
- 系统使用各种成像技术进行手术内分析.
结论:
- 由人工智能驱动的系统显示出在多种成像方式中增强外科副甲状腺识别的巨大潜力.
- 进一步的研究涉及多中心数据集,外科医生整合和临床结果试验是必要的,以实现常规采用.
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