在开发,标准化评估和在临床实践中采用人工智能的领先地位:区域麻醉作为一个例子
James S Bowness1, Xiaoxuan Liu2, Pearse A Keane3
1Nuffield Department of Clinical Anaesthesia, University of Oxford, Oxford, UK; Department of Anaesthesia, Aneurin Bevan University Health Board, Newport, UK.
British journal of anaesthesia
|February 1, 2024
概括
一项研究发现,人工智能医学成像中的低Dice指标并不一定意味着临床评估不佳. 这凸显了在医学中评估人工智能的临床视角的需要.
科学领域:
- 医学成像分析分析 医学成像分析
- 医疗保健中的人工智能
- 临床评估 临床评估
背景情况:
- 子指标是一种常用的人工智能 (AI) 指标,用于评估医疗图像的细分精度.
- 以前的研究主要集中在AI指标的技术性能上,没有足够的临床相关性.
研究的目的:
- 在超声波图像细分方面的医生表现的背景下,探索Dice指标的临床相关性.
- 调查像素智能协议 (迪斯度量) 和对图像质量和结构识别的主观临床评估之间的关系.
主要方法:
- 分析医生在超声波图像上识别解剖结构方面的表现.
- 计算子指标以量化分段之间的像素智能一致性.
- 子指标值与对图像解释的主观临床评估的相关性.
主要成果:
- 在超声波图像中识别结构的医生协议被发现是可变的.
- 相对较低的Dice指标0.34对应于主观临床评估中的实质性协议.
- 技术细分精度 (迪斯度量) 可能不直接反映临床实用性或诊断信心.
结论:
- 该研究强调了将临床视角纳入医疗AI工具评估的必要性.
- 临床医生最适合指导AI在医学中的开发和验证,确保其临床相关性.
- 重新思考人工智能评估指标对于将人工智能有效整合到临床实践中至关重要.
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