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

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.