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arXiv · 1903.10578

Augmenting Gastrointestinal Health: A Deep Learning Approach to Human Stool Recognition and Characterization in Macroscopic Images

David Hachuel·Akshay Jha·Deborah Estrin·Alfonso Martinez·Kyle Staller·Christopher Velez
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Abstract

Purpose - Functional bowel diseases, including irritable bowel syndrome, chronic constipation, and chronic diarrhea, are some of the most common diseases seen in clinical practice. Many patients describe a range of triggers for altered bowel consistency and symptoms. However, characterization of the relationship between symptom triggers using bowel diaries is hampered by poor compliance and lack of objective stool consistency measurements. We sought to develop a stool detection and tracking system using computer vision and deep convolutional neural networks (CNN) that could be used by patients, providers, and researchers in the assessment of chronic gastrointestinal (GI) disease.

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David Hachuel, Akshay Jha, Deborah Estrin, Alfonso Martinez, Kyle Staller, Christopher Velez. 2019-03-25. Augmenting Gastrointestinal Health: A Deep Learning Approach to Human Stool Recognition and Characterization in Macroscopic Images. https://arxiv.org/abs/1903.10578

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