Artificial intelligence-assisted ultrasound-guided regional anaesthesia: An explorative scoping review

IF 2 Q2 ORTHOPEDICS Journal of Experimental Orthopaedics Pub Date : 2024-08-14 DOI:10.1002/jeo2.12104

Martina Marino, Rebecca Hagh, Eric Hamrin Senorski, Umile Giuseppe Longo, Jacob F. Oeding, Bengt Nellgard, Anita Szell, Kristian Samuelsson

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Abstract

Purpose

The present study reviews the available scientific literature on artificial intelligence (AI)-assisted ultrasound-guided regional anaesthesia (UGRA) and evaluates the reported intraprocedural parameters and postprocedural outcomes.

Methods

A literature search was performed on 19 September 2023, using the Medline, EMBASE, CINAHL, Cochrane Library and Google Scholar databases by experts in electronic searching. All study designs were considered with no restrictions regarding patient characteristics or cohort size. Outcomes assessed included the accuracy of AI-model tracking, success at the first attempt, differences in outcomes between AI-assisted and unassisted UGRA, operator feedback and case-report data.

Results

A joint adaptive median binary pattern (JAMBP) has been applied to improve the tracking procedure, while a particle filter (PF) is involved in feature extraction. JAMBP combined with PF was most accurate on all images for landmark identification, with accuracy scores of 0.83, 0.93 and 0.93 on original, preprocessed and filtered images, respectively. Evaluation of first-attempt success of spinal needle insertion revealed first-attempt success in most patients. When comparing AI application versus UGRA alone, a significant statistical difference (p < 0.05) was found for correct block view, correct structure identification and decrease in mean injection time, needle track adjustments and bone encounters in favour of having AI assistance. Assessment of operator feedback revealed that expert and nonexpert operator feedback was overall positive.

Conclusion

AI appears promising to enhance UGRA as well as to positively influence operator training. AI application of UGRA may improve the identification of anatomical structures and provide guidance for needle placement, reducing the risk of complications and improving patient outcomes.

Level of Evidence

Level IV.

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人工智能辅助超声引导区域麻醉：探索性范围综述

目的本研究回顾了有关人工智能（AI）辅助超声引导区域麻醉（UGRA）的现有科学文献，并评估了所报道的术中参数和术后结果。方法 2023 年 9 月 19 日，电子检索专家利用 Medline、EMBASE、CINAHL、Cochrane Library 和 Google Scholar 数据库进行了文献检索。所有研究设计均在考虑之列，对患者特征或队列规模没有限制。评估的结果包括人工智能模型跟踪的准确性、首次尝试的成功率、人工智能辅助和非辅助 UGRA 的结果差异、操作者反馈和病例报告数据。结果采用了联合自适应中值二元模式（JAMBP）来改进跟踪程序，而粒子滤波器（PF）则参与了特征提取。在所有图像上，JAMBP 与粒子滤波器相结合的地标识别准确率最高，在原始图像、预处理图像和滤波图像上的准确率分别为 0.83、0.93 和 0.93。对脊柱针插入的首次尝试成功率进行的评估显示，大多数患者的首次尝试成功率都很高。在比较人工智能应用和单独使用 UGRA 时，发现在正确的区块视图、正确的结构识别以及平均注射时间减少、针轨调整和骨接触方面，人工智能辅助有显著的统计学差异（p < 0.05）。对操作员反馈的评估显示，专家和非专家操作员的反馈总体上是积极的。结论人工智能似乎有望增强 UGRA，并对操作员培训产生积极影响。人工智能在 UGRA 中的应用可改善解剖结构的识别，并为穿刺针的放置提供指导，从而降低并发症风险并改善患者预后。证据等级 IV 级。

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