Ultrasound Trauma Detection on Edge Device

CAAI developed an AI model for the detection of ultrasound image adequacy and positivity for the FAST exam (Focused Assessment with Sonography in Trauma [1]). The results are accepted for publication in the Journal of Trauma and Acute Care Surgery.

We deployed the model (based on Densenet-121 [2]) on an edge device (Nvidia Jetson TX2 [3]) with faster-than-realtime performance (on a video, 19 fps versus the expected 15 fps from an ultrasound device) using TensorRT [4] performance optimizations. The model is trained to recognize adequate views of LUQ/RUQ (Left/Right Upper Quadrant) and positive views of trauma. The video below demonstrates the model prediction for the adequacy of the view.

The device can be used as a training tool for inexperienced Ultrasound operators to aid them in obtaining better (adequate) views and suggest a probability of a positive FAST test.

The project is done in collaboration with the University of Kentucky Department of Surgery. The annotated data is provided by Brittany E. Levy and Jennifer T. Castle.

[1] https://www.ncbi.nlm.nih.gov/books/NBK470479/
[2] Huang G, Liu Z, van der Maaten L, Weinberger KQ. Densely Connected Convolutional Networks. Proceedings – 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017. 2016;2017-January:2261-2269. DOI: 10.48550/arxiv.1608.06993
[3] https://developer.nvidia.com/embedded/jetson-tx2
[4] https://developer.nvidia.com/tensorrt