Role of artificial intelligence in screening and medical imaging of precancerous gastric diseases

Abstract

Serological screening, endoscopic imaging, morphological visual verification of precancerous gastric diseases and changes in the gastric mucosa are the main stages of early detection, accurate diagnosis and preventive treatment of gastric precancer. Laboratory - serological, endoscopic and histological diagnostics are carried out by medical laboratory technicians, endoscopists, and histologists. Human factors have a very large share of subjectivity. Endoscopists and histologists are guided by the descriptive principle when formulating imaging conclusions. Diagnostic reports from doctors often result in contradictory and mutually exclusive conclusions. Erroneous results of diagnosticians and clinicians have fatal consequences, such as late diagnosis of gastric cancer and high mortality of patients. Effective population serological screening is only possible with the use of machine processing of laboratory test results. Currently, it is possible to replace subjective imprecise description of endoscopic and histological images by a diagnostician with objective, highly sensitive and highly specific visual recognition using convolutional neural networks with deep machine learning. There are many machine learning models to use. All machine learning models have predictive capabilities. Based on predictive models, it is necessary to identify the risk levels of gastric cancer in patients with a very high probability.

Keywords: Precancerous gastric diseases; Atrophic gastritis; Serological screening; Risk of gastric cancer; Medical imaging; Artificial intelligence; Convolutional neural networks; Deep machine learning

Core Tip: Prevention of gastric cancer compose of consistent measures to identify precancerous gastric diseases and changes in the gastric mucosa. The first stage is population serological screening for atrophic gastritis. The next stages are endoscopic and morphological visualization of precancerous changes of varying severity. Evaluation of the results of population serological screening is not possible without machine data processing. Accuracy of visualization of endoscopic (macroscopic) and histological (microscopic) images is not possible without the use of convolutional neural networks and deep machine learning. The development and implementation of artificial intelligence will significantly increase the effectiveness of preventive measures.