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相关实验视频

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了解手术场景的人工智能:系统性审查和报告质量的元分析.

Matthias Carstens1,2, Shubha Vasisht3, Zheyuan Zhang1

  • 1Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, USA.

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概括

对于手术场景理解 (SSU) 的人工智能 (AI) 显示出希望,但面临着有限的临床实施. 在数据多样性,验证和临床相关性方面存在研究缺口,这阻碍了人工智能在外科手术中的实际应用.

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科学领域:

  • * 分析外科视频数据的计算方法.
  • *人工智能 (AI) 在医学成像中的应用.

背景情况:

  • *手术场景理解 (SSU) 利用人工智能解释手术中的视觉数据,如腹腔镜视频.
  • *尽管有报道说人工智能能够识别手术元素,但临床采用仍然很低.
  • * 人工智能驱动的实时支持在手术室的潜力是公认的,但尚未实现.

研究的目的:

  • *系统地审查和分析当前计算SSU的状态.
  • * 确定数据策划,模型设计,验证和临床适用性方面的研究缺口.
  • * 评估SSU在现实世界实施方面的进展.

主要方法:

  • *对188项关于手术内微创腹部手术数据的研究的系统综述和元分析.
  • *纳入标准侧重于计算式SSU方法,可训练模型,正式验证和性能指标.
  • *分析涉及数据特征,模型开发,验证策略和临床翻译工作.

主要成果:

  • *大多数研究使用了小型的单中心数据集,通常来自腹腔镜胆囊切除术,缺乏多样性和元数据.
  • *研究主要是描述性的,对临床相关性,局限性,代码可用性和模型不确定性报告不足.
  • *验证方法往往是基本的,缺乏外部测试和临床专家参与;只有11项研究涉及临床翻译.

结论:

  • * 显著的研究缺口阻碍了SSU的临床实施.
  • * 需要多样化,多机构的数据集和强大的验证协议.
  • *临床驱动的开发对于实现SSU在外科实践中的全部潜力至关重要.