aceAI-assisted histopathological diagnosis of Hirschsprung disease
Source article: Advancing hirschsprung disease diagnosis: a systematic review of the development and application of artificial intelligence in histopathological analysis
Abstract: 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…
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This PRISMA 2020 systematic review of thirteen studies from 2016-2025 examined machine and deep learning for Hirschsprung disease diagnosis from histopathological images, evaluating architectures, workflows, and performance with QUADAS-AI and PROBAST.
It matters because reported gains of >90% detection and 50-95% time reduction could reduce subjectivity and specialist burden, but the predominance of small, internally partitioned datasets without external testing leaves reliability and clinical integration uncertain as of the August 2026 publication date.
- Systematic review following PRISMA 2020 guidelines searched seven databases and included thirteen studies from 2016-2025.
- Analysis tracked progression from traditional image processing to convolutional neural networks and transformer models.
- Methodological quality and bias were assessed using QUADAS-AI and PROBAST frameworks.
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.
The rundown
The review found a technical shift from traditional processing to CNNs and transformers, with deep learning outperforming conventional methods on ganglion cell detection and time savings.
Bias assessment with QUADAS-AI and PROBAST identified that nine of thirteen studies lacked external validation and used patch-level splits, prompting calls for large multi-centre datasets, diverse staining techniques, and transparent reporting.
Most included studies had small samples, used patch-level partitioning, and lacked external test sets, raising overfitting and data leakage concerns that limit generalizability.
Sources
- Peer-reviewedPediatric Surgery International2026-08-29
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