  Peripheral nerve blocks are crucial to treatment of post-surgical pain and
are associated with reduction in perioperative opioid use and hospital stay.
Accurate interpretation of sono-anatomy is critical for the success of
ultrasound (US) guided peripheral nerve blocks and can be challenging to the
new operators. This prospective study enrolled 227 subjects who were
systematically scanned for supraclavicular and interscalene brachial plexus in
various settings using three different US machines to create a dataset of 227
unique videos. In total, 41,000 video frames were annotated by experienced
anaesthesiologists using partial automation with object tracking and active
contour algorithms. Four baseline neural network models were trained on the
dataset and their performance was evaluated for object detection and
segmentation tasks. Generalizability of the best suited model was then tested
on the datasets constructed from separate US scanners with and without
fine-tuning. The results demonstrate that deep learning models can be leveraged
for real time segmentation of supraclavicular brachial plexus in neck
ultrasonography videos with high accuracy and reliability. Model was also
tested for its ability to differentiate between supraclavicular and adjoining
interscalene brachial plexus. The entire dataset has been released publicly for
further study by the research community.
