Evaluation of the impact of assistive artificial intelligence on ultrasound scanning for regional anaesthesia

BackgroundRegional anesthesia is increasingly used in acute postoperative pain management. Ultrasound has been used to facilitate the performance of the regional block, increase the percentage of successfully performed procedures and reduce the complication rate. Artificial intelligence (AI) has been studied in many medical disciplines with achieving high success, especially in radiology. The purpose of this review was to review the evidence on the application of artificial intelligence for optimization and interpretation of the sonographic image, and visualization of needle advancement and injection of local anesthetic.MethodsTo conduct this scoping review, we followed the PRISMA-S guidelines. We included studies if they met the following criteria: (1) Application of Artificial intelligence-assisted in ultrasound-guided regional anesthesia; (2) Any human subject (of any age), object (manikin), or animal; (3) Study design: prospective, retrospective, RCTs; (4) Any method of regional anesthesia (epidural, spinal anesthesia, peripheral nerves); (5) Any anatomical localization of regional anesthesia (any nerve or plexus) (6) Any methods of artificial intelligence; (7) Settings: Any healthcare settings (Medical centers, hospitals, clinics, laboratories.ResultsThe systematic searches identified 78 citations. After the removal of the duplicates, 19 full-text articles were assessed; and 15 studies were eligible for inclusion in the review.ConclusionsAI solutions might be useful in anatomical landmark identification, reducing or even avoiding possible complications. AI-guided solutions can improve the optimization and interpretation of the sonographic image, visualization of needle advancement, and injection of local anesthetic. AI-guided solutions might improve the training process in UGRA. Although significant progress has been made in the application of AI-guided UGRA, randomized control trials are still missing.

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“…- Assessment of the utility of ScanNav to identify structures, teaching and learning UGRA, and increase operator confidence ( 16 );…”

Section: Resultsmentioning

confidence: 99%

“…- Assessment of UGRA expert perception of risks of the use of ScanNav (risk of block failure, unwanted needle trauma (eg, arteries, nerves, and pleura/peritoneum ( 16 );…”

Section: Resultsmentioning

confidence: 99%

Artificial intelligence in ultrasound-guided regional anesthesia: A scoping review

Viderman

Dossov²,

Seitenov³

2022

Front. Med.

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“…One such example is that of artificial intelligence, with devices which can highlight anatomical structures of interest on ultrasound in real time 8 9. These devices have shown early promise in supporting the practice of non-experts in UGRA, though it will be important that their development is informed by a spectrum of professional judgment and feedback 10 11…”

Section: Discussionmentioning

confidence: 99%

Recommendations for anatomical structures to identify on ultrasound for the performance of intermediate and advanced blocks in ultrasound-guided regional anesthesia

Ashken

Bowness

Macfarlane

et al. 2022

Reg Anesth Pain Med

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Recent recommendations describe a set of core anatomical structures to identify on ultrasound for the performance of basic blocks in ultrasound-guided regional anesthesia (UGRA). This project aimed to generate consensus recommendations for core structures to identify during the performance of intermediate and advanced blocks. An initial longlist of structures was refined by an international panel of key opinion leaders in UGRA over a three-round Delphi process. All rounds were conducted virtually and anonymously. Blocks were considered twice in each round: for “orientation scanning” (the dynamic process of acquiring the final view) and for “block view” (which visualizes the block site and is maintained for needle insertion/injection). A “strong recommendation” was made if ≥75% of participants rated any structure as “definitely include” in any round. A “weak recommendation” was made if >50% of participants rated it as “definitely include” or “probably include” for all rounds, but the criterion for strong recommendation was never met. Structures which did not meet either criterion were excluded. Forty-one participants were invited and 40 accepted; 38 completed all three rounds. Participants considered the ultrasound scanning for 19 peripheral nerve blocks across all three rounds. Two hundred and seventy-four structures were reviewed for both orientation scanning and block view; a “strong recommendation” was made for 60 structures on orientation scanning and 44 on the block view. A “weak recommendation” was made for 107 and 62 structures, respectively. These recommendations are intended to help standardize teaching and research in UGRA and support widespread and consistent practice.

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“…7 Assistive artificial intelligence (AI) technology could play a role in the future practice of UGRA through supporting ultrasound scanning. 8,9 ScanNav Anatomy Peripheral Nerve Block (Intelligent Ultrasound, Cardiff, UK) uses deep learning based on the U-Net architecture 10 to produce a colour overlay on real-time B-mode ultrasound and highlight structures of interest in UGRA (Fig 1; Supplementary files AeE). The AI models in this system have been trained on more than 800,000 ultrasound images.…”

Section: Editor's Key Pointsmentioning

confidence: 99%

Assistive artificial intelligence for ultrasound image interpretation in regional anaesthesia: an external validation study

Bowness¹,

Laurent²,

Hernandez³

et al. 2023

British Journal of Anaesthesia

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Evaluation of the impact of assistive artificial intelligence on ultrasound scanning for regional anaesthesia

Cited by 32 publications

References 20 publications

Artificial intelligence in ultrasound-guided regional anesthesia: A scoping review

Artificial intelligence in ultrasound-guided regional anesthesia: A scoping review

Recommendations for anatomical structures to identify on ultrasound for the performance of intermediate and advanced blocks in ultrasound-guided regional anesthesia

Assistive artificial intelligence for ultrasound image interpretation in regional anaesthesia: an external validation study

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