ArticleUpdates in surgery2026
Development and clinical assessment of a novel AI-based diagnostic model for Hirschsprung's disease.
Article in Updates in surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Advancing hirschsprung disease diagnosis: a systematic review of the development and application of artificial intelligence in histopathological analysis.Pediatric surgery international · 2026Pooled it
- 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
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study aimed to develop an AI-based diagnostic model for Hirschsprung's disease (HD) using deep learning on contrast enema (CE) images, with the goal of improving diagnostic accuracy while reducing invasiveness. The dataset included 725 CE images from histopathologically confirmed HD patients from 2013 to 2022. Employing Python and PyTorch, a deep learning model based on the YOLOv8 algorithm was trained and validated, emphasizing key metrics like mean average precision (mAP), precision, recall, and F1 score. This model exhibited high precision (0.87477) and recall (0.87317), with an mAP50 score of 0.91. External validation showed promising results, including a sensitivity of 86.96%, a specificity of 72.22%, and an overall accuracy of 80.49%. This AI model offers a less-invasive and accurate alternative to traditional HD diagnostics, especially beneficial for initial screening in pediatric gastroenterology, with the potential to enhance healthcare diagnostics through AI integration.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.