Artificial Intelligence: Innovation to Assist in the Identification of Sono-anatomy for Ultrasound-Guided Regional Anaesthesia

Artificial intelligence in ultrasound-guided regional anesthesia: A scoping review

“…The main benefits included an automatic detection and tracking of nerve structure, overall good performance, assistance in successful recognition of specific anatomical structures, confirming the correct placement of the needle, ultrasound view to anesthetists and standardization of clinical procedure, a real-time interpretation of anatomic structures for immediate decision-making during blocks, provides automatized nerve block using the remote control system, successful detection of vertebral regions at the real-time speed ( 9 – 15 , 18 – 21 , 26 , 27 ). It was reported that artificial intelligence can provide assistance for both novice trainees and experienced clinicians unfamiliar with ultrasound techniques.…”

“…The ultrasound-guided approach does not increase as the automated ultrasoundguided neuraxial technique takes less than a minute. The automated approach was reported to result in a high rate of first attempt success rate that could reduce the complications from multiple entry attempts (19, [25][26][27][28]. In another study, DL-assisted ultrasound-guided imaging for scapular nerve block in scapular fracture surgery was .…”

Artificial intelligence, machine learning and deep learning in musculoskeletal imaging: Current applications

“…Different studies showed that AI may help to automatically segment nerve and blood vessels in order to facilitate ultrasound-guided regional anesthesia. 13,16,17 In fact, automated medical image analysis can be trained to recognize the wide variety of appearances of the anatomical structures and could be used to enhance the interpretation of anatomy by facilitating target identification (e.g., peripheral nerves and fascial planes). 18,19 For example, a model has been well developed for peripheral nerve block in the adductor canal.…”

“…24 in another study, the same authors developed a 10-point scoring system to report scan quality of developmental dysplasia of the hip ultrasound, and examined its effect on an automated assessment by an AI system-MEDO-Hip. 16 Scoring was based on iliac wing straightness and angulation; visibility of labrum, os ischium, femoral head motion and other artifacts. Four readers from novice to expert separately scored the quality of 107 scans with this 10-point scale.…”

“…Their technique also showed excellent agreement with manual assessment in terms of ICC and Cohen's kappa coefficient for ilium, os ischium and femoral head (0.81–0.89 and 0.56–0.66), and moderate to good agreement for labrum (0.65 and 0.33). Furthermore, in another study, the same authors developed a 10‐point scoring system to report scan quality of developmental dysplasia of the hip ultrasound, and examined its effect on an automated assessment by an AI system‐MEDO‐Hip 16 . Scoring was based on iliac wing straightness and angulation; visibility of labrum, os ischium, femoral head motion and other artifacts.…”