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

Abstract

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.

Comparing artificial intelligence to humans for endoscopic diagnosis of gastric neoplasia: An external validation study

“…With further advances in technology, we do expect to see significant improvements in the performance of AI surpassing that of the endoscopist, and with the support of AI, it would also help different endoscopists achieve more consistent results. [17][18][19] This study also highlights the potential benefits of AI in augmenting endoscopy. In our study, the AI-based system took almost 20 times faster (677.14 vs 42.02 s) to evaluate and classify the 300 images as compared with the average time taken by the endoscopists.…”

“…AI provides a stable reproducible baseline to augment the abilities of endoscopists in this regard. With further advances in technology, we do expect to see significant improvements in the performance of AI surpassing that of the endoscopist, and with the support of AI, it would also help different endoscopists achieve more consistent results 17–19 …”

Applications of Deep Learning for Drug Discovery Systems with BigData

“…Deep learning can be classified into different types depending on the learning method and structure. First, CNNs are mainly used in image recognition [71][72][73]. Images that contain meaningful information for humans are nothing but numerical data for machines.…”

Predictive efficacy of combined tumor markers and gastrin for recurrence after endoscopic submucosal dissection in early gastric cancer patients

“…Endoscopic surgery, including endoscopic mucosal resection (EMR) and endoscopic mucosal dissection (ESD), offers several advan-Am J Transl Res 2024;16 (5):2059-2069 tages over traditional surgical procedures, such as being less invasive, fewer postoperative complications, shorter recovery times, lower healthcare costs, and improved quality of life. Importantly, long-term outcomes are comparable to traditional surgical methods, with fiveyear survival rates often exceeding 90% [5]. Consequently, many global gastric cancer treatment guidelines recommend endoscopic surgery as the preferred treatment strategy for early gastric cancer.…”