Advancing hirschsprung disease diagnosis: a systematic review of the development and application of artificial...

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 histopathological images. A search of seven databases identified thirteen eligible studies (2016-2025). Studies were analysed for model architecture, computational workflows and diagnostic performances. Methodological quality and risk of bias were assessed using QUADAS-AI and PROBAST frameworks. HD image analysis has progressed from traditional processing to convolutional neural networks and transformer models. Deep learning outperformed conventional approaches with > 90% in ganglion cell detection and reducing diagnostic time by 50-95%. However, nine studies (69%) exhibited a high risk of bias due to small sample sizes, patch-level data partitioning, and lacking external test sets, raising concerns regarding overfitting and data leakage. AI methods demonstrated strong potential to support HD diagnosis by increasing accuracy, reducing variability and accelerating clinical decision-making. Future research should prioritise large multi-centre datasets, diverse staining techniques, external validation, and transparent reporting to facilitate reliable clinical integration.