Artificial Intelligence in the Diagnosis of Hirschsprung Disease: A Scoping Review and Rationale for a Multicentric Approach

jpedsurg.org

Hirschsprung disease (HD) is a congenital disorder characterized by absence of enteric ganglion cells, leading to functional bowel obstruction. Despite advances in diagnosis, global disparities persist, particularly in low- and middle-income countries (LMICs), where access to pathology remains limited. Artificial intelligence (AI) offers potential to improve diagnostic accessibility through histopathologic analysis.

jpedsurg.org shows its articles on its own site.

Read the article on jpedsurg.org ↗

Article · Jan 2026 · 1 min read

In brief

This scoping review examines how artificial intelligence can enhance diagnosis of Hirschsprung disease, particularly through automated histopathologic analysis. The authors advocate for a multicentric approach to address diagnostic disparities in low-resource settings where pathology expertise is limited.

- AI-based histopathologic analysis may bridge diagnostic gaps for Hirschsprung disease in resource-limited settings. - Global disparities in HD diagnosis persist due to limited pathology access in LMICs. - Multicentric data collection is essential to train robust AI models across diverse populations and practice settings. - AI tools could reduce dependence on specialized pathologists for ganglion cell identification in rectal biopsies. - Standardized imaging protocols are needed to ensure AI diagnostic accuracy across different institutions.

Written by the GCMD Library team from the article.

Read it at the source ↗