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Interpretable Models for Detecting and Monitoring Elevated Intracranial Pressure
Conference proceeding   Open access

Interpretable Models for Detecting and Monitoring Elevated Intracranial Pressure

Darryl Hannan, Steven C. Nesbit, Ximing Wen, Glen Smith, Qiao Zhang, Alberto Goffi, Vincent Chan, Michael J. Morris, John C. Hunninghake, Nicholas E. Villalobos, …
2024 IEEE International Symposium on Biomedical Imaging (ISBI), pp 1-4
27 May 2024
DOI: https://doi.org/10.1109/ISBI56570.2024.10635474
url
http://arxiv.org/abs/2403.02236View
SubmittedarXiv.org - Non-exclusive license to distribute,  Open

Abstract

Biomedical Imaging Optical imaging Pipelines Predictive models Pressure measurement Subject matter experts Ultrasonic imaging Ultrasonic variables measurement Computer Vision Machine Learning
Detecting elevated intracranial pressure (ICP) is crucial in diagnosing and managing various neurological conditions. These fluctuations in pressure are transmitted to the optic nerve sheath (ONS), resulting in changes to its diameter, which can then be detected using ultrasound imaging devices. However, interpreting sonographic images of the ONS can be challenging. In this work, we propose two systems that actively monitor the ONS diameter throughout an ultrasound video and make a final prediction as to whether ICP is elevated. To construct our systems, we leverage subject matter expert (SME) guidance, structuring our processing pipeline according to their collection procedure, while also prioritizing interpretability and computational efficiency. We conduct a number of experiments, demonstrating that our proposed systems are able to outperform various baselines. One of our SMEs then manually validates our top system's performance, lending further credibility to our approach while demonstrating its potential utility in a clinical setting.

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