Deep learning-based detection of eosinophilic esophagitis
| العنوان: | Deep learning-based detection of eosinophilic esophagitis |
|---|---|
| المؤلفون: | Markus Casper, Pedro Guimarães, Andreas Keller, Frank Lammert, Tobias Fehlmann |
| المصدر: | Endoscopy. 54:299-304 |
| بيانات النشر: | Georg Thieme Verlag KG, 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | Delayed Diagnosis, Receiver operating characteristic, business.industry, Candida esophagitis, Deep learning, Gastroenterology, Pattern recognition, Eosinophilic Esophagitis, medicine.disease, Confidence interval, Deep Learning, ROC Curve, Test set, medicine, Humans, Artificial intelligence, business, Eosinophilic esophagitis, Algorithms |
| الوصف: | Background For eosinophilic esophagitis (EoE), a substantial diagnostic delay is still a clinically relevant phenomenon. Deep learning-based algorithms have demonstrated potential in medical image analysis. Here we establish a convolutional neuronal network (CNN)-based approach that can distinguish the appearance of EoE from normal findings and candida esophagitis. Methods We trained and tested a CNN using 484 real-world endoscopic images from 134 subjects consisting of three classes (normal, EoE, and candidiasis). Images were split into two completely independent datasets. The proposed approach was evaluated against three trainee endoscopists using the test set. Model-explainability was enhanced by deep Taylor decomposition. Results Global accuracy (0.915 [95 % confidence interval (CI) 0.880–0.940]), sensitivity (0.871 [95 %CI 0.819–0.910]), and specificity (0.936 [95 %CI 0.910–0.955]) were significantly higher than for the endoscopists on the test set. Global area under the receiver operating characteristic curve was 0.966 [95 %CI 0.954–0.975]. Results were highly reproducible. Explainability analysis found that the algorithm identified the characteristic signs also used by endoscopists. Conclusions Complex endoscopic classification tasks including more than two classes can be solved by CNN-based algorithms. Therefore, our algorithm may assist clinicians in making the diagnosis of EoE. |
| تدمد: | 1438-8812 0013-726X |
| DOI: | 10.1055/a-1520-8116 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9c25f5970cdf4410b7c16eec7d0f2f4a https://doi.org/10.1055/a-1520-8116 |
| رقم الانضمام: | edsair.doi.dedup.....9c25f5970cdf4410b7c16eec7d0f2f4a |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 14388812 0013726X |
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| DOI: | 10.1055/a-1520-8116 |