Advancing Eosinophilic Esophagitis Diagnosis and Phenotype Assessment with Deep Learning Computer Vision

William Adorno III, Alexis Catalano, Alexis Catalano, Lubaina Ehsan, Hans Vitzhum von Eckstaedt, Barrett Barnes, Emily McGowan, Sana Syed, Donald E. Brown

2021

Abstract

Eosinophilic Esophagitis (EoE) is an inflammatory esophageal disease which is increasing in prevalence. The diagnostic gold-standard involves manual review of a patient’s biopsy tissue sample by a clinical pathologist for the presence of 15 or greater eosinophils within a single high-power field (400x magnification). Diagnosing EoE can be a cumbersome process with added difficulty for assessing the severity and progression of disease. We propose an automated approach for quantifying eosinophils using deep image segmentation. A U-Net model and post-processing system are applied to generate eosinophil-based statistics that can diagnose EoE as well as describe disease severity and progression. These statistics are captured in biopsies at the initial EoE diagnosis and are then compared with patient metadata: clinical and treatment phenotypes. The goal is to find linkages that could potentially guide treatment plans for new patients at their initial disease diagnosis. A deep image classification model is further applied to discover features other than eosinophils that can be used to diagnose EoE. This is the first study to utilize a deep learning computer vision approach for EoE diagnosis and to provide an automated process for tracking disease severity and progression.

Paper Citation

in Harvard Style

III W., Catalano A., Ehsan L., von Eckstaedt H., Barnes B., McGowan E., Syed S. and Brown D. (2021). Advancing Eosinophilic Esophagitis Diagnosis and Phenotype Assessment with Deep Learning Computer Vision. In Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021) - Volume 2: BIOIMAGING; ISBN 978-989-758-490-9, SciTePress, pages 44-55. DOI: 10.5220/0010241900002865

in Bibtex Style

@conference{bioimaging21,
author={William Adorno III and Alexis Catalano and Lubaina Ehsan and Hans Vitzhum von Eckstaedt and Barrett Barnes and Emily McGowan and Sana Syed and Donald E. Brown},
title={Advancing Eosinophilic Esophagitis Diagnosis and Phenotype Assessment with Deep Learning Computer Vision},
booktitle={Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021) - Volume 2: BIOIMAGING},
year={2021},
pages={44-55},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010241900002865},
isbn={978-989-758-490-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021) - Volume 2: BIOIMAGING
TI - Advancing Eosinophilic Esophagitis Diagnosis and Phenotype Assessment with Deep Learning Computer Vision
SN - 978-989-758-490-9
AU - III W.
AU - Catalano A.
AU - Ehsan L.
AU - von Eckstaedt H.
AU - Barnes B.
AU - McGowan E.
AU - Syed S.
AU - Brown D.
PY - 2021
SP - 44
EP - 55
DO - 10.5220/0010241900002865
PB - SciTePress