Deployment of an Artificial Intelligence Histology Tool to Aid Qualitative Assessment of Histopathology Using the Nancy Histopathology Index in Ulcerative Colitis

Data de publicação: 16/09/2024 Data Ahead of Print: 01/09/2024

Autores da FMUP

Fernando José Magro Dias

Autor
Maria De Fátima Machado Henriques Carneiro

Autor

Participantes de fora da FMUP

Rubin, David T.
Kubassova, Olga
Weber, Christopher R.
Adsul, Shashi
Freire, Marcelo
Biedermann, Luc
Koelzer, Viktor H.
Bressler, Brian
Xiong, Wei
Niess, Jan H.
Matter, Matthias S.
Kopylov, Uri
Barshack, Iris
Mayer, Chen
Maharshak, Nitsan
Greenberg, Ariel
Hart, Simon
Dehmeshki, Jamshid
Peyrin-Biroulet, Laurent

Unidades de investigação

Biomedicina
Laboratório Associado RISE- Rede de Investigação em Saúde

Investigador

Fernando Carlos De Landér Schmitt
Patologia
RISE-Health

Investigador

Fernando Carlos De Landér Schmitt

Abstract

Background Ulcerative colitis (UC) is a chronic inflammatory bowel disease characterized by increased stool frequency, rectal bleeding, and urgency. To streamline the quantitative assessment of histopathology using the Nancy Index in UC patients, we developed a novel artificial intelligence (AI) tool based on deep learning and tested it in a proof-of-concept trial. In this study, we report the performance of a modified version of the AI tool.Methods Nine sites from 6 countries were included. Patients were aged >= 18 years and had UC. Slides were prepared with hematoxylin and eosin staining. A total of 791 images were divided into 2 groups: 630 for training the tool and 161 for testing vs expert histopathologist assessment. The refined AI histology tool utilized a 4-neural network structure to characterize images into a series of cell and tissue type combinations and locations, and then 1 classifier module assigned a Nancy Index score.Results In comparison with the proof-of-concept tool, each feature demonstrated an improvement in accuracy. Confusion matrix analysis demonstrated an 80% correlation between predicted and true labels for Nancy scores of 0 or 4; a 96% correlation for a true score of 0 being predicted as 0 or 1; and a 100% correlation for a true score of 2 being predicted as 2 or 3. The Nancy metric (which evaluated Nancy Index prediction) was 74.9% compared with 72.3% for the proof-of-concept model.Conclusions We have developed a modified AI histology tool in UC that correlates highly with histopathologists' assessments and suggests promising potential for its clinical application. This multicenter study deployed an artificial intelligence tool based on machine learning to scan histopathology slides from ulcerative colitis biopsies and assign a Nancy Histopathology Index. The performance of the tool was similar to that of a panel of expert histopathologists.

Dados da publicação

ISSN/ISSNe:: 1078-0998, 1536-4844
Tipo:: Article
Páginas:: -
DOI:: 10.1093/ibd/izae204
PubMed:: 39284932

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Keywords

ulcerative colitis; artificial intelligence; histopathology