Deep Learning-Based Tracking of Neurovascular Features Toward Semi-Automated Ultrasound-Guided Peripheral Nerve Blocks by Non-Specialists.

Peripheral nerve blocks can effectively reduce the use of general anesthesia and opioids in situations where robust pain management is critical, such as severe extremity trauma and hip, femur, and knee surgeries. Despite these benefits, nerve blocks are underutilized due to the high skill required to accurately insert a needle and safely deliver local anesthetic. To overcome this challenge, ultrasound image guidance enabled by artificial intelligence (AI) offers a semi-automated solution for regional anesthesia delivery by non-specialists. As a first step towards realizing an integrated platform for AI-guided nerve blocks, the main objective of this study is to develop and characterize deep learning algorithms to interpret anatomical landmarks on ultrasound images in real time and identify aimpoints for needle placement. Our AI system was trained on over 55,000 images from 20 porcine models and demonstrated an average area under the precision-recall curve of 0.92 (SD = 0.03) for in vivo landmark detection in the femoral nerve region. In prospective live animal testing, aimpoint identification had a 98.3% success rate with an average time of 40.5 s (SD = 33.5). Future work will focus on integrated testing with handheld robotics towards a more accessible method for delivering regional anesthesia in settings from point of injury to medical transport to hospitals.