ArticleBiomedical engineering systems and technologies, international joint conference, BIOSTEC ... revised selected papers. BIOSTEC (Conference)2021
Advancing Eosinophilic Esophagitis Diagnosis and Phenotype Assessment with Deep Learning Computer Vision.
Article in Biomedical engineering systems and technologies, international joint conference, BIOSTEC ... revised selected papers. BIOSTEC (Conference), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Application of artificial intelligence in eosinophilic esophagitis.Frontiers in immunology · 2025Review
- Eosinophils Instance Object Segmentation on Whole Slide Imaging Using Multi-label Circle Representation.Proceedings of SPIE--the International Society for Optical Engineering · 2024Article
- A Deep Multi-Label Segmentation Network For Eosinophilic Esophagitis Whole Slide Biopsy Diagnostics.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2022Article
- Image-Level Uncertainty in Pseudo-Label Selection for Semi-Supervised Segmentation.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2022Article
- Harnessing artificial intelligence to infer novel spatial biomarkers for the diagnosis of eosinophilic esophagitis.Frontiers in medicine · 2022Article
- Eosinophilic esophagitis: when pathologists make the difference.Jornal de pediatriaArticle
Corrections and comments
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Authors and funding
8 authors.
Funding
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 (400× 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.