Enhancing diagnosis of Hirschsprung's disease using deep learning from histological sections of post pull-through specimens: preliminary results.

ABSTRACT:

Purpose

Accurate histological diagnosis in Hirschsprung disease (HD) is challenging, due to its complexity and potential for errors. In this study, we present an artificial intelligence (AI)-based method designed to identify ganglionic cells and hypertrophic nerves in HD histology.

Methods

Formalin-fixed samples were used and an expert pathologist and a surgeon annotated these slides on a web-based platform, identifying ganglionic cells and nerves. Images were partitioned into square sections, augmented through data manipulation techniques and used to develop two distinct U-net models: one for detecting ganglionic cells and normal nerves; the other to recognise hypertrophic nerves.

Results

The study included 108 annotated samples, resulting in 19,600 images after data

SUBMITTER: Duci M 

PROVIDER: S-EPMC10687181 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature