AI in Endoscopic Gastrointestinal Diagnosis: A Systematic Review of Deep Learning and Machine Learning Techniques

Gastrointestinal (GI) diseases are most common worldwide and the death rate can be reduced by early detection. Endoscopy is widely regarded as the gold standard for diagnosing and managing digestive disorders, affecting both the upper and lower GI tracts. Endoscopy is performed to uncover biopsy tissues used to check the presence of cancerous or benign cells, Helicobacter pylori (H. pylori) infection, or perform colonoscopy in case of the removal of polyps. A systematic review was conducted on databases like PubMed, Scopus, Google Scholar, and IEEE Explore, including research papers published up to May 2023, through the systematic search, 33 papers were identified. This review offers valuable insights to physicians and technological guidance to future researchers by examining GI tract diseases. It provides a detailed analysis of Machine learning (ML) techniques like preprocessing, segmentation, feature extraction, and classification. Additionally, Deep Learning (DL) approaches like transfer learning (TL), Convolution Neural Networks (CNN), optimization, transformer, and reinforcement learning have been analyzed for GI diagnosis. The DL approach has increased its use in GI diseases and CNN was the most commonly used architecture. Lastly, the review highlights the research published in the specialized GI fields and provides technological suggestions and insights for future research prospects. Overall, this study broadens the body of knowledge regarding the existing Artificial Intelligence (AI) techniques in gastroenterology as a manual for creating and assessing AI models.