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Dhaliwal, J., Erdman, L., Drysdale, E., Rinawi, F., Muir, J., Walters, T.D., et al. (2021) Accurate Classification of Pediatric Colonic Inflammatory Bowel Disease Subtype Using a Random Forest Machine Learning Classifier. Journal of Pediatric Gastroenterology and Nutrition, 72, 262-269.
https://doi.org/10.1097/mpg.0000000000002956

has been cited by the following article:

• TITLE: Artificial Intelligence in the Early Diagnosis of Inflammatory Bowel Disease: A Valuable Tool That Needs Further Refinement

  AUTHORS: Ming-Xian Yan

  KEYWORDS: Inflammatory Bowel Disease, Early Diagnosis, Artificial Intelligence

  JOURNAL NAME: Journal of Biosciences and Medicines, Vol.13 No.4, April 25, 2025

  ABSTRACT: The early diagnosis of inflammatory bowel disease (IBD) is crucial for improving patient prognosis. However, some patients experience diagnostic delays due to atypical clinical presentations. Current methods widely used in clinical practice may be insufficient for early diagnosis of IBD. Artificial intelligence (AI) technology is gradually being applied to the medical diagnosis of diseases. AI systems can accurately identify intestinal pathologies through advanced image analysis; convolutional neural networks have demonstrated particular efficacy in detecting mucosal erosions and ulcers in both standard and capsule endoscopy images. These systems also enhance radiological assessment by reducing image noise and synthesizing weighted magnetic resonance imaging (MRI) sequences, thereby improving image quality and diagnostic information yield. The combination of AI-assisted endoscopy and medical imaging technology has significantly improved the detection rate of intestinal lesions. Nevertheless, limitations persist as training datasets may contain inherent biases and fail to fully represent clinical diversity. In conclusion, while AI applications show promising potential for early IBD diagnosis, they still need to be improved in the future.