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.

Keywords: application; artificial intelligence; early gastric cancer; improving; screening.

FIGURE 1

The red font in the figure represents the keywords with the highest frequency in the included literature, the circle represents the articles published in that year, the size represents the number of articles published, and the color of the line represents the year. The reports on endoscopy and EGC were mainly concentrated between 1999 and 2010, with less and less relevant literature published in this field after that point. In the last decade, the topic of combining endoscopy with EGC has no longer been a research topic of interest.

FIGURE 2

The closer the font is to the center of the figure, the more attention is paid. In addition, the size of the circle indicates the number of relevant publications. The top-down color indicates the year. The diagnosis gets the most attention, followed by the various digestive diseases that surround the diagnosis.

FIGURE 3

This figure shows the 25 keywords with the highest frequency in the literature and the attention paid to these keywords over time. It’s not hard to see that convolutional neural networks are beginning to attract attention.

FIGURE 4

Based on combined images, the studies related to AI are concentrated in the last 3 years, and there is an obvious growth trend.

FIGURE 5

This figure is based on the literature analysis of artificial intelligence and endoscopy. It can be seen that the research hotspots under this topic are the classification of gastric cancer and computer-aided examination and diagnosis. At the same time, convolutional neural networks also appear in hot spots, indicating that convolutional neural networks are showing an increasing trend in the application of artificial intelligence.

FIGURE 6

By analyzing the figure, it can be seen that when combined with literature on early gastric cancer and artificial intelligence, both of these topics have occurred in recent years. Additionally, this year’s study focused on endoscopic screening.

FIGURE 7

According to this figure, we can see that the convolutional neural network is currently attracting a lot of attention and is closely associated with gastric cancer.

FIGURE 8

It can be seen from the revised figure that the literature studies on the combination of early gastric cancer, artificial intelligence, and endoscopy have taken place in recent 3 years, and the main research hotspots in 2022 are focused on screening, i.e., applying artificial intelligence to endoscopy to screen early gastric cancer.

FIGURE 9

Combined with literature on early gastric cancer, artificial intelligence, and endoscopy, convolutional neural networks occupy the center and become an absolute research hotspot.

FIGURE 10

A CNN model was trained to identify whether or not the content of a given picture was an airplane. We assumed that the characteristics of the aircraft were the tail, engine, and fuselage and set the characteristics as the convolution kernel. The image was then converted into a matrix that a computer could recognize. The eigenmatrix of the sample was obtained by a convolution operation between the convolution kernel and the sample image. A non-linear activation function was then used to perform a non-linear activation operation on the eigenmatrix to improve the sparsity of the network and reduce the interdependence of parameters. The pooling layer was used to reduce the dimension of the feature matrix, compress the image features, remove the redundant information, and reduce the amount of calculation. Finally, we converted the calculated eigenspace mapping sample marker space into a one-dimensional vector through the full connection layer to obtain the complete image features. After completing the above steps, we also established an error function to determine the accuracy of the output. The convolution kernel parameters were adjusted to reduce the error and obtain the actual features of aircraft images.

FIGURE 11

EndoAngel-assisted endoscopy. The image on the left shows high-risk lesions, and the image on the right shows low-risk lesions. During endoscopy, AI automatically identified and evaluated the lesion. If it detected a high-risk lesion, a red prompt box appeared, while a blue prompt box appeared for low-risk lesions. The prompt box not only helps the endoscopist quickly identify the lesion but also helps doctors carry out an accurate sampling biopsy.