Enhancing diagnosis of Hirschsprung’s disease using deep learning from histological sections of post pull-through specimens: preliminary results

Researchers developed a deep learning U-net model to identify ganglionic cells and hypertrophic nerves in Hirschsprung disease histology, achieving over 91% accuracy. This AI-based approach could standardize HD diagnosis and serve as a training tool for pathologists evaluating complex pediatric surgical specimens.