aceAdvancing hirschsprung disease diagnosis: a systematic review of the development and application of artificial intelligence in histopathological analysis
Hirschsprung's disease (HD) is characterised by absence of ganglion cells in the distal large intestine, requiring accurate histopathological diagnosis. Conventional diagnostic methods are time-consuming, subjective, and demand specialised expertise. While artificial intelligence (AI) shows promise for improving diagnostic capacity, its clinical utility requires rigorous evaluation. Following PRISMA 2020 guidelines, this systematic review evaluated machine and deep learning techniques for HD diagnosis from histo…
Deep learning models for Hirschsprung disease histopathology achieved over 90% ganglion cell detection and cut diagnostic time by 50-95%, increasing accuracy and accelerating clinical decision-making.
69% of studies showed high risk of bias from small sample sizes, patch-level data partitioning, and no external test sets, raising concerns about overfitting and data leakage.
Most included studies had small samples, used patch-level partitioning, and lacked external test sets, raising overfitting and data leakage concerns that limit generalizability.
Evidence
- Peer-reviewedPediatric Surgery International2026-08-29
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Truvace Impact Record TRV-2026-0942, v1: “Advancing hirschsprung disease diagnosis: a systematic review of the development and application of artificial intelligence in histopathological analysis.” Truvace, 2026-08-31. /record/TRV-2026-0942 (accessed at citation time). sha256 43b5a574f0b5b328…
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