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

James S Bowness, Alan J R Macfarlane, David Burckett-St Laurent, Catherine Harris, Steve Margetts, Megan Morecroft, David Phillips, Tom Rees, Nick Sleep, Asta Vasalauskaite, Simeon West, J Alison Noble, Helen Higham, James S Bowness, Alan J R Macfarlane, David Burckett-St Laurent, Catherine Harris, Steve Margetts, Megan Morecroft, David Phillips, Tom Rees, Nick Sleep, Asta Vasalauskaite, Simeon West, J Alison Noble, Helen Higham

Abstract

Background: Ultrasound-guided regional anaesthesia relies on the visualisation of key landmark, target, and safety structures on ultrasound. However, this can be challenging, particularly for inexperienced practitioners. Artificial intelligence (AI) is increasingly being applied to medical image interpretation, including ultrasound. In this exploratory study, we evaluated ultrasound scanning performance by non-experts in ultrasound-guided regional anaesthesia, with and without the use of an assistive AI device.

Methods: Twenty-one anaesthetists, all non-experts in ultrasound-guided regional anaesthesia, underwent a standardised teaching session in ultrasound scanning for six peripheral nerve blocks. All then performed a scan for each block; half of the scans were performed with AI assistance and half without. Experts assessed acquisition of the correct block view and correct identification of sono-anatomical structures on each view. Participants reported scan confidence, experts provided a global rating score of scan performance, and scans were timed.

Results: Experts assessed 126 ultrasound scans. Participants acquired the correct block view in 56/62 (90.3%) scans with the device compared with 47/62 (75.1%) without (P=0.031, two data points lost). Correct identification of sono-anatomical structures on the view was 188/212 (88.8%) with the device compared with 161/208 (77.4%) without (P=0.002). There was no significant overall difference in participant confidence, expert global performance score, or scan time.

Conclusions: Use of an assistive AI device was associated with improved ultrasound image acquisition and interpretation. Such technology holds potential to augment performance of ultrasound scanning for regional anaesthesia by non-experts, potentially expanding patient access to these techniques.

Clinical trial registration: NCT05156099.

Keywords: artificial intelligence; peripheral nerve block; regional anaesthesia; sono-anatomy; ultrasonography; ultrasound.

Copyright © 2022 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Figures

Fig 1
Fig 1
Examples of ScanNav Anatomy Peripheral Nerve Block colour overlay. ISB (interscalene level brachial plexus block): AS, anterior scalene; C5, C5 nerve root; C6, C6 nerve root; MS, middle scalene. AxBP (axillary level brachial plexus block): AA, axillary artery; AV, axillary vein; CT, conjoint (common) tendon of latissimus dorsi/teres major; McN, musculocutaneous nerve; MN, median nerve; RN, radial nerve; UN, ulnar nerve. ESP (erector spinae block): ESM, erector spinae muscle group (and overlying muscles); TP, transverse process. RSB (rectus sheath block): P, peritoneum; RA, rectus abdominis; RSa, rectus sheath (anterior layer); RSp, rectus sheath (posterior layer). ACB (adductor canal block): FA, femoral artery; SaN, saphenous nerve; SM, sartorius muscle. SNB (popliteal level sciatic nerve block): CPN, common peroneal (fibular) nerve; TN, tibial nerve.
Fig 2
Fig 2
Flow diagram of participant's progress. ACB, adductor canal block; AxBP, axillary level brachial plexus block; ESP, erector spinae plane block; ISB, interscalene level brachial plexus block; Pop-SNB, popliteal level sciatic nerve block; RSB, rectus sheath block; UGRA, ultrasound-guided regional anaesthesia.
Fig 3
Fig 3
Participant confidence score. Distribution of all participant self-rated confidence scores, showing a breakdown of scans performed with or without ScanNav Anatomy Peripheral Nerve Block. PNB, Peripheral Nerve Block.
Fig 4
Fig 4
Expert global rating score. Distribution of all expert global rating scores, showing a breakdown of scans performed with or without ScanNav Anatomy Peripheral Nerve Block. PNB, Peripheral Nerve Block.

References

    1. Sites B.D., Chan V.W., Neal J.M., et al. The American society of regional anesthesia and Pain medicine and the European society of regional anaesthesia and Pain Therapy joint committee recommendations for education and training in ultrasound-guided regional anesthesia. Reg Anesth Pain Med. 2009;34:40–46.
    1. Henderson M., Dolan J. Challenges, solutions, and advances in ultrasound-guided regional anaesthesia. BJA Educ. 2016;16:374–380.
    1. Bowness J., Taylor A. Ultrasound-guided regional anaesthesia: visualising the nerve and needle. Adv Exp Med Biol. 2020;1235:19–34.
    1. Bowness J., Turnbull K., Taylor A., et al. Identifying the emergence of the superficial peroneal nerve through deep fascia on ultrasound and by dissection: implications for regional anesthesia in foot and ankle surgery. Clin Anat. 2019;32:390–395.
    1. Le Saint-Grant A., Taylor A., Varsou O., Grant C., Cezayirli E., Bowness J. Arterial anatomy of the anterior abdominal wall: ultrasound evaluation as a real-time guide to percutaneous instrumentation. Clin Anat. 2021;34:5–10.
    1. Christophe J.L., Berthier F., Boillot A., et al. Assessment of topographic brachial plexus nerves variations at the axilla using ultrasonography. Br J Anaesth. 2009;103:606–612.
    1. Bowness J., El-Boghdadly K., Burckett-St Laurent D. Artificial intelligence for image interpretation in ultrasound-guided regional anaesthesia. Anaesthesia. 2021;76:602–607.
    1. Drukker L., Noble J.A., Papageorghiou A.T. Introduction to artificial intelligence in ultrasound imaging in obstetrics and gynecology. Ultrasound Obstet Gynecol. 2020;56:498–505.
    1. Kainz B., Heinrich M.P., Makropoulos A., et al. Non-invasive diagnosis of deep vein thrombosis from ultrasound imaging with machine learning. NPJ Digit Med. 2021;4:137.
    1. Shokoohi H., LeSaux M.A., Roohani Y.H., Liteplo A., Huang C., Blaivas M. Enhanced point-of-care ultrasound applications by integrating automated feature-learning systems using deep learning. J Ultrasound Med. 2019;38:1887–1897.
    1. Diaz-Gomez J.L., Mayo P.H., Koenig S.J. Point-of-care ultrasonography. N Engl J Med. 2021;385:1593–1602.
    1. Gungor I., Gunaydin B., Oktar S.O., et al. A real-time anatomy identification via tool based on artificial intelligence for ultrasound-guided peripheral nerve block procedures: an accuracy study. J Anesth. 2021;35:591–594.
    1. Bowness J., Varsou O., Turbitt L., Burkett-St Laurent D. Identifying anatomical structures on ultrasound: assistive artificial intelligence in ultrasound-guided regional anesthesia. Clin Anat. 2021;34:802–809.
    1. Bowness J.S., El-Boghdadly K., Woodworth G., Noble J.A., Higham H., Burckett-St Laurent D. Exploring the utility of assistive artificial intelligence for ultrasound scanning in regional anesthesia. Reg Anesth Pain Med. 2022;47:375–379.
    1. Turbitt L.R., Mariano E.R., El-Boghdadly K. Future directions in regional anaesthesia: not just for the cognoscenti. Anaesthesia. 2020;75:293–297.
    1. Bowness J.S., Pawa A., Turbitt L., et al. International consensus on anatomical structures to identify on ultrasound for the performance of basic blocks in ultrasound-guided regional anesthesia. Reg Anesth Pain Med. 2022;47:106–112.
    1. Liu X., Cruz Rivera S., Moher D., et al. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension. Lancet Digit Health. 2020;2:e537–e548.
    1. Bowness J., Turnbull K., Taylor A., et al. Identifying variant anatomy during ultrasound-guided regional anaesthesia: opportunities for clinical improvement. Br J Anaesth. 2019;122:e75–e77.
    1. Available from: (accessed 25 November 2021).
    1. Ultrasound guidelines: emergency, point-of-care and clinical ultrasound guidelines in medicine. Ann Emerg Med. 2017;69:e27–e54.
    1. R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL

Source: PubMed

Sponsors and Collaborators

Medical Conditions

Drug Interventions

3
Subscribe