{"title":"人工智能和机器学习在胃癌诊断和预后中的应用现状和最新发展:系统综述","authors":"Rushin Patel, Mrunal Patel, Zalak Patel, Himanshu Kavani, Afoma Onyechi, Jessica Ohemeng-Dapaah, Dhruvkumar Gadhiya, Darshil Patel, Chieh Yang","doi":"10.9734/jcti/2024/v14i1241","DOIUrl":null,"url":null,"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.\nMethods: 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.\nResults: 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.\nConclusion: In the near future, AI applications may have a significant role in the diagnosis and prognosis prediction of gastric cancer.","PeriodicalId":509152,"journal":{"name":"Journal of Cancer and Tumor International","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Present State and Recent Developments of Artificial Intelligence and Machine Learning in Gastric Cancer Diagnosis and Prognosis: A Systematic Review\",\"authors\":\"Rushin Patel, Mrunal Patel, Zalak Patel, Himanshu Kavani, Afoma Onyechi, Jessica Ohemeng-Dapaah, Dhruvkumar Gadhiya, Darshil Patel, Chieh Yang\",\"doi\":\"10.9734/jcti/2024/v14i1241\",\"DOIUrl\":null,\"url\":null,\"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.\\nMethods: 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. 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引用次数: 0
摘要
研究目的本研究旨在利用现有的最新数据,深入研究人工智能(AI)和机器学习(ML)技术在胃癌诊断和预后预测中的应用:按照系统综述和荟萃分析首选报告项目(PRISMA)指南,对人工智能和机器学习技术在胃癌诊断和预后预测中的应用进行了系统综述。使用特定语法检索了2019年2月至2024年1月期间的PubMed和Google Scholar。根据纳入标准筛选出符合条件的试验,包括近期发表、关注胃癌中的人工智能和ML、报告诊断或预后结果。数据提取和质量评估均由双方独立完成,不一致之处通过讨论解决。由于设计存在异质性,因此省略了详细分析,只提供了纳入文章的描述性摘要:本综述共纳入 8 篇文章。包括卷积神经网络(CNN)和深度学习模型在内的人工智能和 ML 技术在准确诊断慢性萎缩性胃炎、预测胃癌术后预后和识别胃癌患者腹膜转移方面发挥了关键作用。这些技术具有潜在的优势,如简化诊断程序、指导治疗决策、提高胃癌患者的治疗效果等:在不久的将来,人工智能应用可能会在胃癌诊断和预后预测方面发挥重要作用。
Present State and Recent Developments of Artificial Intelligence and Machine Learning in Gastric Cancer Diagnosis and Prognosis: A Systematic Review
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.