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.