ArticlePediatric surgery international2023
Enhancing diagnosis of Hirschsprung's disease using deep learning from histological sections of post pull-through specimens: preliminary results.
Article in Pediatric surgery international, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 2 of them syntheses that pooled it.
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Who cites it
10 citing papers in PubMed, 2 syntheses or guidelines pooled it, 18 citations in OpenAlex.
- Advancing hirschsprung disease diagnosis: a systematic review of the development and application of artificial intelligence in histopathological analysis.Pediatric surgery international · 2026Pooled it
- Machine learning in the diagnosis of Hirschsprung disease: a systematic review and meta-analysis.Pediatric surgery international · 2026Pooled it
- Latest Developments in Artificial Intelligence and Machine Learning Models in General Pediatric Surgery.European journal of pediatric surgery : official journal of Austrian Association of Pediatric Surgery ... [et al] = Zeitschrift fur Kinderchirurgie · 2026Review
- AI in pediatric surgery: a narrative review.Translational pediatrics · 2026Review
- From vision to validation: operational pathways for safe, equitable AI in pediatric surgery.Pediatric surgery international · 2026Article
- Endoscopic Full-Thickness Resection Biopsy Using Ovesco Diagnostic FTRD® as a Tool to Diagnose Hirschsprung's Disease in Children: A Case Series.Visceral medicine · 2025Article
- A roadmap of artificial intelligence applications in pediatric surgery: a comprehensive review of applications, challenges, and ethical considerations.Pediatric surgery international · 2025Review
- Pediatric surgical trainees and artificial intelligence: a comparative analysis of DeepSeek, Copilot, Google Bard and pediatric surgeons' performance on the European Pediatric Surgical In-Training Examinations (EPSITE).Pediatric surgery international · 2025Article
- Current and future state of the management of Hirschsprung disease.World journal of pediatric surgery · 2025Review
- Potential and Promise: Artificial Intelligence in Pediatric Surgery.Journal of Indian Association of Pediatric SurgeonsArticle
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeAccurate histological diagnosis in Hirschsprung disease (HD) is challenging, due to its complexity and potential for errors. In this study, we present an artificial intelligence (AI)-based method designed to identify ganglionic cells and hypertrophic nerves in HD histology.
methodsFormalin-fixed samples were used and an expert pathologist and a surgeon annotated these slides on a web-based platform, identifying ganglionic cells and nerves. Images were partitioned into square sections, augmented through data manipulation techniques and used to develop two distinct U-net models: one for detecting ganglionic cells and normal nerves; the other to recognise hypertrophic nerves.
resultsThe study included 108 annotated samples, resulting in 19,600 images after data augmentation and manually segmentation. Subsequently, 17,655 slides without target elements were excluded. The algorithm was trained using 1945 slides (930 for model 1 and 1015 for model 2) with 1556 slides used for training the supervised network and 389 for validation. The accuracy of model 1 was found to be 92.32%, while model 2 achieved an accuracy of 91.5%.
conclusionThe AI-based U-net technique demonstrates robustness in detecting ganglion cells and nerves in HD. The deep learning approach has the potential to standardise and streamline HD diagnosis, benefiting patients and aiding in training of pathologists.
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