Table 1.

Study and cohort information.

Author, country, year	Study goal	Study population (diagnosis)	Sample size Gender (males %)	Region of body studied
Bowness et al., 2021 (18)	Assess the AI anatomy identification	Healthy population	244	Interscalene-supraclavicular level brachial plexus block Rectus sheath block Axillary level brachial plexus Erector spinae plane block Suprainguinal fascia iliaca block Adductor canal block Popliteal level sciatic nerve block
Alkhatib et al., 2018, France (9)	To study nerve structure and ultrasound images tracking	–	10 6 (60%) males 4 (40%) females	Median nerve identification
Alkhatib et al., 2019, France (10)	To study the deep-learning performance for nerve tracking in ultrasound images	-	42	Median & sciatic nerves
Gungor et al., 2021 (11)	To study the accuracy of real-time (AI) -based anatomical identification	Healthy population	40 20 (50%) males 20 (50%) females	Block regions: Supraclavicular, infraclavicular, and transversus abdominis plane (TAP)
Hetherington et al., 2017 (19)	Detect the lower vertebral level	–	20	Anesthesia in the lower vertebrae regions (sacrum, intervertebral gaps, and vertebral bones)
Huang et al., 2019 China (20)	femoral nerve on ultrasound images	–	–	Femoral nerve
Mwikirize et al., 2018 (12)	CNN-based framework for needle detection in curvilinear 2D US	bovine/porcine lumbosacral spine phantom	–
Oh et al., 2019, Singapore (13)	Success rate of spinal anesthesia	Obstetric women	100	Spinal anesthesia
Pesteie et al., 2017 (14)	Precise needle target localization	–	33	–
Smistad et al., 2018, Norway (15)	Identification of musculocutaneous, median, ulnar, and radial nerve) and blood vessels	Healthy volunteers	49	Axillary nerve block: four nerves (musculocutaneous, median, ulnar, and radial nerve) and blood vessels
Tran et al., 2010, Canada (21)	Features of the lumbar anatomy	Parturients in labor or scheduled for cesarean delivery	20	Epidural anesthesia
Bowness et al., 2022 (16)	Assessment of the utility of ScanNav to identify structures, teaching and learning UGRA and increase operator confidence. 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	Healthy volunteers	2	Nine peripheral nerve block regions The upper limb (the “interscalene-,” “upper trunk-,” “supraclavicular-,” “axillary-level brachial plexus” regions; “Erector spinae plane block,” “rectus sheath plane block regions”; the “suprainguinal level fascia iliaca plane,” “adductor canal and popliteal-level sciatic nerve blocks.”
Bowness et al., 2022 (22)	Expert-level AI model performance evaluation	Healthy adult subjects	40	Upper-extremity blocks: “upper trunk of the brachial plexus,” “interscalene-level brachial plexus,” “supraclavicular-level brachial plexus,” “axillary-level brachial plexus”
				Thoraco-abdominal blocks: erector spinae plane, rectus sheath block. Lower-extremity blocks: “suprainguinal fascia iliaca,” “adductor canal and distal femoral triangle,” “popliteal-level sciatic nerve blocks.”
Yang et al., 2022 (23)	Development a deep learning algorithm to locate the “interscalene brachial plexus” based on ultrasound images to aid anaesthesiologists.	Patients	1076 (dataset −11 392 images	Interscalene brachial plexus
Liu et al., 2021 (17)	To identify difference in accuracy between deep learning-powered ultrasound guidance and regular ultrasound images; the use of artificial intelligence to optimize regional anesthesia puncture path; to identify the effectiveness of ultrasound-guided imaging “scapular nerve block” surgical pain of the fracture	Patients	100	“Scapular nerve block”