Automated analysis of small intestinal lamina propria to distinguish normal, Celiac Disease, and Non-Celiac Duodenitis biopsy images

Faust, Oliver ORCID logoORCID:, De Michele, Simona ORCID logoORCID:, Koh, Joel EW, Jahmunah, V ORCID logoORCID:, Lih, Oh Shu, Kamath, Aditya P ORCID logoORCID:, Barua, Prabal Datta, Ciaccio, Edward J, Lewis, Suzanne K, Green, Peter H, Bhagat, Govind ORCID logoORCID: and Acharya, U Rajendra (2023) Automated analysis of small intestinal lamina propria to distinguish normal, Celiac Disease, and Non-Celiac Duodenitis biopsy images. Computer Methods and Programs in Biomedicine, 230. ISSN 1872-7565

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Background and objective Celiac Disease (CD) is characterized by gluten intolerance in genetically predisposed individuals. High disease prevalence, absence of a cure, and low diagnosis rates make this disease a public health problem. The diagnosis of CD predominantly relies on recognizing characteristic mucosal alterations of the small intestine, such as villous atrophy, crypt hyperplasia, and intraepithelial lymphocytosis. However, these changes are not entirely specific to CD and overlap with Non-Celiac Duodenitis (NCD) due to various etiologies. We investigated whether Artificial Intelligence (AI) models could assist in distinguishing normal, CD, and NCD (and unaffected individuals) based on the characteristics of small intestinal lamina propria (LP). Methods Our method was developed using a dataset comprising high magnification biopsy images of the duodenal LP compartment of CD patients with different clinical stages of CD, those with NCD, and individuals lacking an intestinal inflammatory disorder (controls). A pre-processing step was used to standardize and enhance the acquired images. Results For the normal controls versus CD use case, a Support Vector Machine (SVM) achieved an Accuracy (ACC) of 98.53%. For a second use case, we investigated the ability of the classification algorithm to differentiate between normal controls and NCD. In this use case, the SVM algorithm with linear kernel outperformed all the tested classifiers by achieving 98.55% ACC. Conclusions To the best of our knowledge, this is the first study that documents automated differentiation between normal, NCD, and CD biopsy images. These findings are a stepping stone toward automated biopsy image analysis that can significantly benefit patients and healthcare providers.

Item Type: Journal Article
Keywords: Biopsy, Celiac Disease, Computer-aided diagnosis, Explainable artificial intelligence, Inflammation, Lamina propria
Faculty: Faculty of Science & Engineering
SWORD Depositor: Symplectic User
Depositing User: Symplectic User
Date Deposited: 01 Feb 2023 16:34
Last Modified: 01 Feb 2023 16:34

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