Evaluation of the effectiveness of artificial intelligence for ultrasound guided peripheral nerve and plane blocks in recognizing anatomical structures.

BACKGROUND: We aimed to assess the accuracy of artificial intelligence (AI) based real-time anatomy identification for ultrasound-guided peripheral nerve and plane block in eight regions in this prospective observational study. METHODS: After obtaining ethics committee approval and written informed consent from 40 healthy volunteers (20 men and 20 women, between 18 and 72 years old), an ultrasound device installed with AI software (Nerveblox, SmartAlfa, Turkey) were used to scan regions of the cervical plexus, brachial plexus, pectoralis (PECS), rectus sheet, femoralis, canalis adductorius, popliteal, and ESP by three anesthesiology trainees. During scanning by a trainee, once software indicates 100 % scan success of associated anatomic landmarks, both raw and labeled ultrasound images were saved, assessed, and validated using a 6-point scale between 0 and 5 by two expert validators. Evaluation scores of the validators for each block were compared according to demographics (gender, age, and BMI) and block type exists. RESULTS: The scores were not different except ESP, femoralis, and cervical plexus regions between the experts. The mean scores of the experts for the PECS, popliteal and rectus sheath were significant between males and females (p < 0.05). In terms of BMI, significant differences in the scores were observed only in the canalis adductorius, brachial plexus, and ESP regions (p < 0.05). CONCLUSIONS: Ultrasound guided AI-based anatomy identification was performed in commonly used eight block regions by the trainees where AI technology can successfully interpret the anatomical structures in real-time sonography which would be valuable in assisting anesthesiologists.

A real-time anatomy identification via tool based on artificial intelligence for ultrasound-guided peripheral nerve block procedures: an accuracy study.

We aimed to assess the accuracy of an artificial intelligence (AI)-based real-time anatomy identification software specifically developed to ease image interpretation intended for ultrasound-guided peripheral nerve block (UGPNB). Forty healthy participants (20 women, 20 men) were enrolled to perform interscalene, supraclavicular, infraclavicular, and transversus abdominis plane (TAP) blocks under ultrasound guidance using AI software by anesthesiology trainees. During block practice by a trainee, once the software indicates 100% scan success of each block associated anatomic landmarks, both raw and labeled ultrasound images were saved, assessed, and validated using a 5-point scale by expert validators. When trainees reached 100% scan success, accuracy scores of the validators were noted. Correlation analysis was used whether the relationship (r) according to demographics (gender, age, and body mass index: BMI) and block type exist. The BMI (kg/m2) and age (year) of participants were 22.2 ± 3 and 32.2 ± 5.25, respectively. Assessment scores of validators for all blocks were similar in male and female individuals. Mean assessment scores of validators were not significantly different according to age and BMI except for TAP block, which was inversely correlated with age and BMI (p = 0.01). AI technology can successfully interpret anatomical structures in real-time sonography while assisting young anesthesiologists during UGPNB practice.