This prospective observational study aims to evaluate the agreement between artificial intelligence (AI)-assisted target point identification and experienced anesthesiologists during ultrasound-guided axillary brachial plexus block.
Ultrasound guidance is widely used in regional anesthesia to improve block success and safety. However, accurate identification of anatomical structures and optimal injection points remains operator-dependent. Artificial intelligence-based systems have the potential to assist clinicians by identifying anatomical landmarks in real time.
In this study, AI-generated target points will be compared with those determined by experienced anesthesiologists. The level of agreement between the two methods will be analyzed. Secondary outcomes will include block performance parameters and image quality.
The findings of this study may contribute to understanding the clinical utility of AI in ultrasound-guided regional anesthesia.
Age between 18 and 80 years
American Society of Anesthesiologists (ASA) physical status I-III
Patients scheduled for upper extremity surgery under ultrasound-guided axillary brachial plexus block as part of routine clinical practice
Ability to obtain adequate real-time ultrasound imaging of the axillary region prior to block performance
Inability to clearly visualize the axillary artery and at least one peripheral nerve (median, ulnar, radial, or musculocutaneous) on ultrasound imaging
Presence of significant ultrasound artifacts impairing image interpretation
History of previous surgery in the axillary region causing anatomical distortion
Anatomical deformities or significant anatomical variations in the axillary region