Exploring the utility of assistive artificial intelligence for ultrasound scanning in regional anesthesia. Issue 6 (28th January 2022)

• Record Type: Journal Article
• Authors: Bowness, James Simeon; El-Boghdadly, Kariem; Woodworth, Glenn; Noble, J Alison; Higham, Helen; Burckett-St Laurent, David
• Is Part Of: Regional anesthesia and pain medicine. Volume 47:Issue 6(2022)
• Page Start: 375
• Page End: 379
• Publication Date: 2022-01-28
• Subjects: regional anesthesia -- ultrasonography -- technology -- education -- nerve block; Conduction anesthesia -- Periodicals; Pain medicine -- Periodicals; 617.964
• DOI: 10.1136/rapm-2021-103368
• Languages: English
• ISSNs: 1098-7339

Abstract

Abstract : Introduction: Ultrasound-guided regional anesthesia (UGRA) involves the acquisition and interpretation of ultrasound images to delineate sonoanatomy. This study explores the utility of a novel artificial intelligence (AI) device designed to assist in this task (ScanNav Anatomy Peripheral Nerve Block; ScanNav), which applies a color overlay on real-time ultrasound to highlight key anatomical structures. Methods: Thirty anesthesiologists, 15 non-experts and 15 experts in UGRA, performed 240 ultrasound scans across nine peripheral nerve block regions. Half were performed with ScanNav. After scanning each block region, participants completed a questionnaire on the utility of the device in relation to training, teaching, and clinical practice in ultrasound scanning for UGRA. Ultrasound and color overlay output were recorded from scans performed with ScanNav. Experts present during the scans (real-time experts) were asked to assess potential for increased risk associated with use of the device (eg, needle trauma to safety structures). This was compared with experts who viewed the AI scans remotely. Results: Non-experts were more likely to provide positive and less likely to provide negative feedback than experts (p=0.001). Positive feedback was provided most frequently by non-experts on the potential role for training (37/60, 61.7%); for experts, it was for its utility in teaching (30/60, 50%). Real-time and remote experts reported a potentially increased risk in 12/254 (4.7%) vs 8/254 (3.1%, p=0.362) scans, respectively. Discussion: ScanNav shows potential to support non-experts in training and clinical practice, and experts in teaching UGRA. Such technology may aid the uptake and generalizability of UGRA. Trial registration number: NCT04918693 .