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

Faust, O; De Michele, S; Koh, JE; Jahmunah, V; Lih, OS; Kamath, AP; Barua, PD; Ciaccio, EJ; Lewis, SK; Green, PH; Bhagat, G; Acharya, UR

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

Faust, O De Michele, S Koh, JE Jahmunah, V Lih, OS Kamath, AP Barua, PD Ciaccio, EJ Lewis, SK Green, PH Bhagat, G Acharya, UR

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Comput Methods Programs Biomed, 2023, 230, pp. 107320
Issue Date:: 2023-03

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Field	Value	Language
dc.contributor.author	Faust, O
dc.contributor.author	De Michele, S
dc.contributor.author	Koh, JE
dc.contributor.author	Jahmunah, V
dc.contributor.author	Lih, OS
dc.contributor.author	Kamath, AP
dc.contributor.author	Barua, PD
dc.contributor.author	Ciaccio, EJ
dc.contributor.author	Lewis, SK
dc.contributor.author	Green, PH
dc.contributor.author	Bhagat, G
dc.contributor.author	Acharya, UR
dc.date.accessioned	2024-04-05T00:02:30Z
dc.date.available	2022-12-18
dc.date.available	2024-04-05T00:02:30Z
dc.date.issued	2023-03
dc.identifier.citation	Comput Methods Programs Biomed, 2023, 230, pp. 107320
dc.identifier.issn	0169-2607
dc.identifier.issn	1872-7565
dc.identifier.uri	http://hdl.handle.net/10453/177483
dc.description.abstract	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.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	Elsevier
dc.relation.ispartof	Comput Methods Programs Biomed
dc.relation.isbasedon	10.1016/j.cmpb.2022.107320
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0903 Biomedical Engineering, 0906 Electrical and Electronic Engineering
dc.subject.classification	Medical Informatics
dc.subject.classification	4003 Biomedical engineering
dc.subject.classification	4601 Applied computing
dc.subject.classification	4603 Computer vision and multimedia computation
dc.subject.mesh	Humans
dc.subject.mesh	Celiac Disease
dc.subject.mesh	Duodenitis
dc.subject.mesh	Artificial Intelligence
dc.subject.mesh	Noncommunicable Diseases
dc.subject.mesh	Biopsy
dc.subject.mesh	Intestinal Mucosa
dc.subject.mesh	Intestinal Mucosa
dc.subject.mesh	Humans
dc.subject.mesh	Duodenitis
dc.subject.mesh	Celiac Disease
dc.subject.mesh	Biopsy
dc.subject.mesh	Artificial Intelligence
dc.subject.mesh	Noncommunicable Diseases
dc.subject.mesh	Humans
dc.subject.mesh	Celiac Disease
dc.subject.mesh	Duodenitis
dc.subject.mesh	Artificial Intelligence
dc.subject.mesh	Noncommunicable Diseases
dc.subject.mesh	Biopsy
dc.subject.mesh	Intestinal Mucosa
dc.title	Automated analysis of small intestinal lamina propria to distinguish normal, Celiac Disease, and Non-Celiac Duodenitis biopsy images.
dc.type	Journal Article
utslib.citation.volume	230
utslib.location.activity	Ireland
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0903 Biomedical Engineering
utslib.for	0906 Electrical and Electronic Engineering
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
utslib.copyright.status	open_access	*
dc.date.updated	2024-04-05T00:02:28Z
pubs.publication-status	Published
pubs.volume	230

Abstract:

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.

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http://hdl.handle.net/10453/177483