The POCUS AI program will produce code demonstrating the feasibility of automated interpretation of point- of-care ultrasound (POCUS) across multiple applications and will provide a foundation for future work. POCUS can quickly and accurately address a broad range of medical needs directly on the battlefield when in the hands of highly-trained medical personnel. However, a lack of POCUS competency among military medics has led to underutilization. Automated interpretation by artificial intelligence (AI) could significantly reduce training burdens and increase POCUS usage. The POCUS AI program will address this need in a cost-effective manner by advancing AI techniques that use minimal training data and can serve multiple distinct POCUS applications. The algorithms will be demonstrated on four DoD-priority battlefield medical needs: detection of pneumothorax, measurement of optic nerve sheath diameter, nerve block guidance, and verification of endotracheal intubation.

Our team is leveraging task-general POCUS domain knowledge obtained from subject matter experts (SMEs) and unlabeled ultrasound images to generate task-specific POCUS AI models from limited labeled training data. The approach utilizes teachable AI, sparse coding, small-data classifiers, and knowledge-based explainable AI to output timely results on mobile devices. The final models can also explain their outputs, reducing medical diagnosis errors (false negatives/positives) and increasing model adoption among military medics.

Relevant Publications

Hannan, D., Nesbit, S. C., Wen, X., Smith, G., Zhang, Q., Goffi, A., Chan, V., Morris, M.J., Hunninghake, J. C., Villalobos, N. E., Kim, E., Weber, R. O., & MacLellan, C. J. (2024). Interpretable Models for Detecting and Monitoring Elevated Intracranial Pressure. In Proceedings of The International Symposium on Biomedical Imaging.

Hannan, D., Nesbit, S.C., Wen, X., Smith, G., Zhang, Q., Goffi, A., Chan, V., Morris, M.J., Hunninghake, J.C., Villalobos, N.E., Kim, E., Weber, R.O., MacLellan, C.J. (2023). MobilePTX: Sparse Coding for Pneumothorax Detection Given Limited Training Examples. In Proceedings of The Thirty-Fifth Annual Conference on Innovative Applications of Artificial Intelligence.

Smith, G.*, Zhang, Q.*, MacLellan, C.J. (2022). Do it Like the Doctor: How We Can Design a Model That Uses Domain Knowledge to Diagnose Pneumothorax. In Proceedings of the AAAI 2022 Spring Symposium on Machine Learning and Knowledge Engineering for Hybrid Intelligence (AAAI-MAKE 2022). (* are co-first authors)