Machine learning approach for biopsy-based identification of eosinophilic esophagitis reveals importance of global features
This work addresses the time-consuming and laborious manual diagnosis of EoE for clinicians, though it appears incremental as it applies an existing deep learning method to a new medical dataset.
The researchers tackled the problem of automating the diagnosis of eosinophilic esophagitis (EoE) from biopsy slides, achieving an accuracy of 85%, sensitivity of 82.5%, and specificity of 87% using a deep convolutional neural network.
Goal: Eosinophilic esophagitis (EoE) is an allergic inflammatory condition characterized by eosinophil accumulation in the esophageal mucosa. EoE diagnosis includes a manual assessment of eosinophil levels in mucosal biopsies - a time-consuming, laborious task that is difficult to standardize. One of the main challenges in automating this process, like many other biopsy-based diagnostics, is detecting features that are small relative to the size of the biopsy. Results: In this work, we utilized hematoxylin- and eosin-stained slides from esophageal biopsies from patients with active EoE and control subjects to develop a platform based on a deep convolutional neural network (DCNN) that can classify esophageal biopsies with an accuracy of 85%, sensitivity of 82.5%, and specificity of 87%. Moreover, by combining several downscaling and cropping strategies, we show that some of the features contributing to the correct classification are global rather than specific, local features. Conclusions: We report the ability of artificial intelligence to identify EoE using computer vision analysis of esophageal biopsy slides. Further, the DCNN features associated with EoE are based on not only local eosinophils but also global histologic changes. Our approach can be used for other conditions that rely on biopsy-based histologic diagnostics.