Oxford Simulation, Teaching and Research Centre, University of Oxford, Oxford, UK; Department of Anaesthesia, Aneurin Bevan University Health Board, Newport, UK.
Department of Anaesthesia, Royal Cornwall Hospitals NHS Trust, Truro, UK.
Br J Anaesth. 2023 Feb;130(2):217-225. doi: 10.1016/j.bja.2022.06.031. Epub 2022 Aug 18.
Ultrasonound is used to identify anatomical structures during regional anaesthesia and to guide needle insertion and injection of local anaesthetic. ScanNav Anatomy Peripheral Nerve Block (Intelligent Ultrasound, Cardiff, UK) is an artificial intelligence-based device that produces a colour overlay on real-time B-mode ultrasound to highlight anatomical structures of interest. We evaluated the accuracy of the artificial-intelligence colour overlay and its perceived influence on risk of adverse events or block failure.
Ultrasound-guided regional anaesthesia experts acquired 720 videos from 40 volunteers (across nine anatomical regions) without using the device. The artificial-intelligence colour overlay was subsequently applied. Three more experts independently reviewed each video (with the original unmodified video) to assess accuracy of the colour overlay in relation to key anatomical structures (true positive/negative and false positive/negative) and the potential for highlighting to modify perceived risk of adverse events (needle trauma to nerves, arteries, pleura, and peritoneum) or block failure.
The artificial-intelligence models identified the structure of interest in 93.5% of cases (1519/1624), with a false-negative rate of 3.0% (48/1624) and a false-positive rate of 3.5% (57/1624). Highlighting was judged to reduce the risk of unwanted needle trauma to nerves, arteries, pleura, and peritoneum in 62.9-86.4% of cases (302/480 to 345/400), and to increase the risk in 0.0-1.7% (0/160 to 8/480). Risk of block failure was reported to be reduced in 81.3% of scans (585/720) and to be increased in 1.8% (13/720).
Artificial intelligence-based devices can potentially aid image acquisition and interpretation in ultrasound-guided regional anaesthesia. Further studies are necessary to demonstrate their effectiveness in supporting training and clinical practice.
NCT04906018.
超声用于在区域麻醉期间识别解剖结构，并引导针插入和局部麻醉剂注射。ScanNav 解剖周围神经阻滞（英国加的夫的智能超声）是一种基于人工智能的设备，它在实时 B 模式超声上生成彩色覆盖层，突出显示感兴趣的解剖结构。我们评估了人工智能彩色覆盖层的准确性及其对不良事件或阻滞失败风险的感知影响。
超声引导区域麻醉专家从 40 名志愿者（九个解剖区域）中获取了 720 个视频，而没有使用该设备。随后应用了人工智能彩色覆盖层。另外三位专家独立审查了每个视频（以及原始未修改的视频），以评估彩色覆盖层与关键解剖结构（真阳性/阴性和假阳性/阴性）的准确性，以及突出显示是否会改变对不良事件（神经、动脉、胸膜和腹膜的针损伤）或阻滞失败的感知风险。
人工智能模型在 93.5%的病例中识别出感兴趣的结构（1519/1624），假阴性率为 3.0%（48/1624），假阳性率为 3.5%（57/1624）。在 62.9-86.4%的病例中（302/480 至 345/400），突出显示被判断为降低了对神经、动脉、胸膜和腹膜的不必要针损伤的风险，而在 0.0-1.7%的病例中（0/160 至 8/480）则增加了风险。报告称 81.3%的扫描（585/720）降低了阻滞失败的风险，1.8%（13/720）增加了风险。
基于人工智能的设备有可能辅助超声引导区域麻醉中的图像采集和解释。需要进一步的研究来证明它们在支持培训和临床实践方面的有效性。
NCT04906018。
