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©The Author(s) 2025.
World J Gastroenterol. Oct 14, 2025; 31(38): 110999
Published online Oct 14, 2025. doi: 10.3748/wjg.v31.i38.110999
Published online Oct 14, 2025. doi: 10.3748/wjg.v31.i38.110999
Feature | Description | Relevance |
Definitive diagnosis | Histological identification of 15 or more eosinophils per HPF[65] | Gold standard for diagnosis |
Endoscopic findings | Rings, linear furrows, exudates, edema[65] | Key visual indicators |
Associated atopic conditions | Asthma, eczema, allergic rhinitis, food allergies[1] | Common co-morbidities, important for management |
Symptoms | Dysphagia, food impaction, chest pain, reflux-like symptoms[1] | Impact on quality of life |
Risk of complications | Increased risk of esophageal strictures and remodeling[1] | Importance of early diagnosis |
Table 2 Summary of artificial intelligence methodologies applicable to eosinophilic esophagitis diagnosis
AI methodology | Description | Applications in EoE |
Supervised ML | Algorithms that rely on labeled training data to learn and predict outcomes | Classifying endoscopic images, predicting eosinophil counts |
DL | Subset of ML utilizing neural networks for pattern recognition | Analyzing endoscopic and histopathological data |
Convolutional neural networks | Special type of DL particularly adept at image recognition | Enhancing diagnostic accuracy in endoscopic images |
Random decision forest | ML algorithm combining multiple data sources for improved decision making | Integrating clinical and endoscopic data for diagnosis |
Natural language processing | AI field focused on interaction between computers and human language | Analyzing electronic health records for diagnostic insights |
Table 3 Comparative table of the main artificial intelligence models applied to endoscopic diagnosis of esophagitis diagnosis
Model | Training dataset | Population size | Validation status | Key limitations | Methodological rigor |
AI-EoE-EREFS (DL/CNN)[48] | Endoscopic images, EREFS-labeled, multi-center | 484 images from 134 patients | Retrospective, some external validation | Retrospective data, limited diversity, mainly binary classification, limited real-world scenarios | High accuracy (AUC = 0.992), but generalizability and real-world robustness uncertain |
CNN[47] | Endoscopic images (EoE vs controls) | 1192 characteristic endoscopic images of 108 patients | Tested against patients and controls | Dataset size not reported, unclear diversity, retrospective design | High accuracy (95%), but limited transparency and unclear reproducibility |
CNN with EREFS integration[49] | Endoscopic images annotated with EREFS | 200 WLIs, including 100 WLIs from EoE patients and 100 WLIs of normal esophagus | Compared to human experts | Training/test data overlap possible, unclear external validation | Sensitivity improved to 85%, specificity 95% with EREFS |
Random decision forest[50] | Clinical + endoscopic data | Not specified | Real-world data, AUC up to 0.94 | Complexity, integration challenges, population diversity not detailed | Robust with multi-source inputs, but needs more transparent validation |
General endoscopic AI (DL/CNN) | Mixed datasets (WLI, NBI, CT images) | Varies; often small | Mostly retrospective, single/multi-center | Data heterogeneity, lack of multi-center, prospective validation | High reported accuracy, but shallow validation, risk of bias due to dataset curation |
- Citation: Issa IA, Youssef O, Issa T. Can artificial intelligence improve the diagnosis and management of patients with eosinophilic esophagitis? World J Gastroenterol 2025; 31(38): 110999
- URL: https://www.wjgnet.com/1007-9327/full/v31/i38/110999.htm
- DOI: https://dx.doi.org/10.3748/wjg.v31.i38.110999