TY - JOUR
T1 - Artificial intelligence for ultrasound scanning in regional anaesthesia
T2 - a scoping review of the evidence from multiple disciplines
AU - Bowness, James S.
AU - Metcalfe, David
AU - El-Boghdadly, Kariem
AU - Thurley, Neal
AU - Morecroft, Megan
AU - Hartley, Thomas
AU - Krawczyk, Joanna
AU - Noble, J. Alison
AU - Higham, Helen
N1 - Publisher Copyright:
© 2024 The Author(s)
PY - 2024/5
Y1 - 2024/5
N2 - Background: Artificial intelligence (AI) for ultrasound scanning in regional anaesthesia is a rapidly developing interdisciplinary field. There is a risk that work could be undertaken in parallel by different elements of the community but with a lack of knowledge transfer between disciplines, leading to repetition and diverging methodologies. This scoping review aimed to identify and map the available literature on the accuracy and utility of AI systems for ultrasound scanning in regional anaesthesia. Methods: A literature search was conducted using Medline, Embase, CINAHL, IEEE Xplore, and ACM Digital Library. Clinical trial registries, a registry of doctoral theses, regulatory authority databases, and websites of learned societies in the field were searched. Online commercial sources were also reviewed. Results: In total, 13,014 sources were identified; 116 were included for full-text review. A marked change in AI techniques was noted in 2016–17, from which point on the predominant technique used was deep learning. Methods of evaluating accuracy are variable, meaning it is impossible to compare the performance of one model with another. Evaluations of utility are more comparable, but predominantly gained from the simulation setting with limited clinical data on efficacy or safety. Study methodology and reporting lack standardisation. Conclusions: There is a lack of structure to the evaluation of accuracy and utility of AI for ultrasound scanning in regional anaesthesia, which hinders rigorous appraisal and clinical uptake. A framework for consistent evaluation is needed to inform model evaluation, allow comparison between approaches/models, and facilitate appropriate clinical adoption.
AB - Background: Artificial intelligence (AI) for ultrasound scanning in regional anaesthesia is a rapidly developing interdisciplinary field. There is a risk that work could be undertaken in parallel by different elements of the community but with a lack of knowledge transfer between disciplines, leading to repetition and diverging methodologies. This scoping review aimed to identify and map the available literature on the accuracy and utility of AI systems for ultrasound scanning in regional anaesthesia. Methods: A literature search was conducted using Medline, Embase, CINAHL, IEEE Xplore, and ACM Digital Library. Clinical trial registries, a registry of doctoral theses, regulatory authority databases, and websites of learned societies in the field were searched. Online commercial sources were also reviewed. Results: In total, 13,014 sources were identified; 116 were included for full-text review. A marked change in AI techniques was noted in 2016–17, from which point on the predominant technique used was deep learning. Methods of evaluating accuracy are variable, meaning it is impossible to compare the performance of one model with another. Evaluations of utility are more comparable, but predominantly gained from the simulation setting with limited clinical data on efficacy or safety. Study methodology and reporting lack standardisation. Conclusions: There is a lack of structure to the evaluation of accuracy and utility of AI for ultrasound scanning in regional anaesthesia, which hinders rigorous appraisal and clinical uptake. A framework for consistent evaluation is needed to inform model evaluation, allow comparison between approaches/models, and facilitate appropriate clinical adoption.
KW - artificial intelligence
KW - evaluation
KW - medical devices
KW - regional anaesthesia
KW - regulation
KW - standardisation
KW - ultrasound
UR - http://www.scopus.com/inward/record.url?scp=85186945380&partnerID=8YFLogxK
U2 - 10.1016/j.bja.2024.01.036
DO - 10.1016/j.bja.2024.01.036
M3 - Review article
C2 - 38448269
AN - SCOPUS:85186945380
SN - 0007-0912
VL - 132
SP - 1049
EP - 1062
JO - British Journal of Anaesthesia
JF - British Journal of Anaesthesia
IS - 5
ER -