Artificial Intelligence in the Diagnosis of Hirschsprung Disease: A Scoping Review and Rationale for a Multicentric Approach
Topic overview
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.
Key takeaways
- - 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.
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How to cite: GlobalCastMD. Artificial Intelligence in the Diagnosis of Hirschsprung Disease: A Scoping Review and Rationale for a Multicentric Approach. GlobalCastMD Medical Library. 2026-01-18. https://library.globalcastmd.com/article/11395
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