Towards Automating Junctional Hemorrhage Control Using AI for Interpretation of Human Tissue

Junctional hemorrhage has a high fatality rate due to how difficult it is to control rapid bleeding from major vessels. The available methods to stop junctional blood loss are prone to placement errors as well as failure during transport and during prolonged field care. On the battlefield, medical imaging with a portable ultrasound can be leveraged for visualization of the underlying tissue and application of compression at the anatomical junction to effectively stop blood flow. In this work, we developed AI models for anatomical landmark tracking using a perfused human cadaver model. These AI models were paired with an end-user clinical application to guide proper placement and compression, improving junctional hemorrhage control on the future battlefield. The trained U-Net semantic segmentation model demonstrated strong performance across predictions for both validation and hold-out, blind subjects. Overall pixel accuracy across the dataset was 98.9% for training and 98.6% for blind subjects. The artery and vein predictions achieved the highest class-specific training intersection-over-union scores, both at 0.73. This segmentation model trained to interpret human tissue provides evidence that ultrasound visualization can help guide compression at anatomical junctions. Future work will focus on improving blind performance for implementation of this AI model into closed-loop control of hardware prototypes, delivering real-time predictions and control.