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

Author:

Duci Miriam,Magoni Alessia,Santoro Luisa,Dei Tos Angelo Paolo,Gamba Piergiorgio,Uccheddu Francesca,Fascetti-Leon Francesco

Abstract

Abstract Purpose Accurate 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. Methods Formalin-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. Results The 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%. Conclusion The 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.

Funder

Università degli Studi di Padova

Publisher

Springer Science and Business Media LLC

Subject

General Medicine,Surgery,Pediatrics, Perinatology and Child Health

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Potential and Promise: Artificial Intelligence in Pediatric Surgery;Journal of Indian Association of Pediatric Surgeons;2024-08-23

2. Diagnosis of Hirschsprung disease by analyzing acetylcholinesterase staining using artificial intelligence;Journal of Pediatric Gastroenterology and Nutrition;2024-08-09

3. The Pathologist's Role in the Diagnosis of Hirschsprung's Disease;European Journal of Pediatric Surgery;2024-07-12

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