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The Feasibility of Applying Artificial Intelligence to Gastrointestinal Endoscopy to Improve the Detection Rate of Early Gastric Cancer Screening

Journal

FRONTIERS IN MEDICINE
Volume 9, Issue -, Pages -

Publisher

FRONTIERS MEDIA SA
DOI: 10.3389/fmed.2022.886853

Keywords

artificial intelligence; early gastric cancer; screening; improving; application

Funding

  1. Medical Health Science and Technology Project of Zhejiang Province [2021PY083]
  2. Program of Taizhou Science and Technology Grant [20ywb29, 1901ky18]
  3. Major Research Program of Taizhou Enze Medical Center Grant [19EZZDA2]
  4. Open Project Program of Key Laboratory of Minimally Invasive Techniques and Rapid Rehabilitation of Digestive System Tumor of Zhejiang Province [21SZDSYS01, 21SZDSYS09]
  5. Key Technology R&D Program of Zhejiang Province [2019C03040]

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Convolutional neural networks in artificial intelligence have great potential in image recognition and can assist in improving the screening and detection of early gastric cancer. They can enhance accuracy, reduce the burden on doctors, and provide real-time feedback for standardizing procedures. Artificial intelligence also shows promising results in training novice doctors, contributing to increased early gastric cancer detection rates and reduced mortality rates from related diseases in China.
Convolutional neural networks in the field of artificial intelligence show great potential in image recognition. It assisted endoscopy to improve the detection rate of early gastric cancer. The 5-year survival rate for advanced gastric cancer is less than 30%, while the 5-year survival rate for early gastric cancer is more than 90%. Therefore, earlier screening for gastric cancer can lead to a better prognosis. However, the detection rate of early gastric cancer in China has been extremely low due to many factors, such as the presence of gastric cancer without obvious symptoms, difficulty identifying lesions by the naked eye, and a lack of experience among endoscopists. The introduction of artificial intelligence can help mitigate these shortcomings and greatly improve the accuracy of screening. According to relevant reports, the sensitivity and accuracy of artificial intelligence trained on deep cirrocumulus neural networks are better than those of endoscopists, and evaluations also take less time, which can greatly reduce the burden on endoscopists. In addition, artificial intelligence can also perform real-time detection and feedback on the inspection process of the endoscopist to standardize the operation of the endoscopist. AI has also shown great potential in training novice endoscopists. With the maturity of AI technology, AI has the ability to improve the detection rate of early gastric cancer in China and reduce the death rate of gastric cancer related diseases in China.

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