Huang YH, Lin Q, Jin XY, Chou CY, Wei JJ, Xing J, Guo HM, Liu ZF, Lu Y. Classification of pediatric video capsule endoscopy images for small bowel abnormalities using deep learning models. World J Gastroenterol 2025; 31(21): 107601 [PMID: 40538507 DOI: 10.3748/wjg.v31.i21.107601]
Reader's ID:
08628847
Submitted on:
June 20, 2025, 08:23
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Reader Comments:
This paper explores the application of deep learning models, including DenseNet121, ResNet50, VGG16, and Vision Transformer, for classifying pediatric video capsule endoscopy (VCE) images, aiming to improve diagnostic efficiency for pediatric gastrointestinal diseases. The study is highly relevant to clinical needs, particularly in enhancing diagnostic accuracy and reducing manual review time. The results show that the deep learning models used performed excellently in classifying various lesions, such as normal mucosa, ulcers, and polyps. However, the novelty of the study is somewhat limited, as it mainly applies existing deep learning architectures without providing new methods. The discussion on clinical applications is also brief, lacking in-depth analysis of the model's applicability, limitations, and challenges in real-world clinical settings. Overall, the paper offers valuable insights into the automated diagnosis of pediatric gastrointestinal diseases, but it would benefit from more original data and a deeper exploration of clinical applications.
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