Present State and Recent Developments of Artificial Intelligence and Machine Learning in Gastric Cancer Diagnosis and Prognosis: A Systematic Review

Abstract

Objective: The objective of this study is to thoroughly investigate the use of artificial intelligence (AI) and machine learning (ML) techniques for diagnosing and predicting prognosis in gastric cancer, utilizing the latest available data. Methods: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)guidelines, a systematic review investigated AI and ML applications in gastric cancer diagnosis and prognostic prediction. PubMed and Google Scholar were searched from February 2019 to January 2024 using specific syntax. Eligible trials were selected based on inclusion criteria including recent publication, focus on AI and ML in gastric cancer, and reporting diagnostic or prognostic outcomes. Data were extracted and quality assessed independently, with discrepancies resolved through discussion. Due to design heterogeneity, detailed analysis was omitted, and descriptive summaries of included articles were provided. Results: This review included a total of 8 articles. AI and ML techniques, including  convolutional neural networks (CNN) and deep learning models, have played pivotal roles in accurately diagnosing chronic atrophic gastritis, predicting postoperative gastric cancer prognosis, and identifying peritoneal metastasis in gastric cancer patients. These technologies offer potential advantages such as streamlining diagnostic procedures, guiding treatment decisions,  and enhancing patient outcomes in gastric cancer management. Conclusion: In the near future, AI applications may have a significant role in the diagnosis and prognosis prediction of gastric cancer.