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.

Key Words: 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.

INTRODUCTION

Many authors publish scientific articles in which they present results on the reduction of morbidity and mortality from stomach cancer. Although the global burden of stomach cancer in the world tends to decrease, in many countries stomach cancer remains one of the leaders in terms of morbidity and mortality. 44.0% of gastric cancer cases and 48.6% of mortality from it occur in the population of China. A similar situation is noted in many countries of Southeast Asia. Given the current trends in gastric cancer, there is an urgent need to develop and implement a global strategy for screening, early detection, and therapeutic strategies[1]. First of all, it is necessary to implement mass screening and eradication of Helicobacter pylori (H. pylori)[2,3]. Currently, effective models of serological screening at the first stage of gastric cancer prevention have been developed. They include serological screening for H. pylori infection and screening for atrophic gastritis, which is a precancerous disease with a high risk of gastric cancer[4,5]. Mülder et al[6] draw attention to the need to implement global strategies to identify individuals at increased risk of developing gastric cancer. The implementation of preventive measures should be carried out with mandatory consideration of regional differences in the prevalence of precancerous diseases, changes in the gastric mucosa and H. pylori infection. The need to implement population screening methods for identifying an increased risk of gastric cancer is due to the fact that precancerous diseases are observed in asymptomatic individuals[6]. Implementation of screening methods is cost-effective and justified and has a significant advantage over no preventive screening. The incremental cost-effectiveness ratio is 1230 dollars per life-year[7]. China has the largest economic burden of cancer mortality in the world. In 2020, there were 3002899 deaths from cancer in China, and the number of newly diagnosed cancer cases was 4568754. The aging process of China’s population, as well as the increase in the population leading an unhealthy lifestyle, allows us to predict that the incidence and mortality of gastric cancer will increase in the future. The level of global preventive screening to reduce the incidence and mortality of gastric cancer requires enormous labor costs in analyzing the results of screening markers. In addition, in different regions and countries, analytical methods should be adapted to specific features, which complicates the processing of results[8]. Detection and eradication of H. pylori infection must be implemented globally because it is a very deleterious pathogen. A large statistical sample showed that 94.4% of non-cardiac gastric cancer and 75.6% of cardiac gastric cancer are associated with H. pylori infection[9]. Population-based studies have confirmed an increase in gastric cancer incidence rates in young people in some countries. The need to implement global strategies to prevent gastric cancer in young population groups (< 40 years) is obvious[10,11]. Global prevention strategies for gastric cancer must take into account and analyze the impact of many risk factors, such as alcohol consumption, smoking, obesity, physical inactivity, and high blood cholesterol. National human development indices have a significant impact on the prevalence of gastric cancer. To optimize and improve the effectiveness of preventive measures, it is necessary to take into account all the risk factors that have been studied, and it is also necessary to continue studying little-known factors[12]. The global burden of gastric cancer among young people has been found to be significant in developing regions and East Asian countries. Global strategies for the prevention of gastric cancer should include screening and early detection, prevention of risk factors, diagnosis of precancerous diseases and changes in the gastric mucosa, their treatment, and should include strategies for primary and secondary prevention with increased effectiveness[13-16]. Despite the fact that there is a slight decrease in the incidence and mortality of gastric cancer per 100000 population, globally there is an increase in the prevalence and mortality of this disease. For example, in 2021, 1230000 people fell ill and 950000 people died from gastric cancer. The increase in the incidence and mortality of gastric cancer is explained by population growth and aging of the population worldwide. The need to implement effective global strategies for the control and prevention of gastric cancer is very urgent[17]. Predictive, preventive, and personalized medicine should be the basis for a global strategy for the prevention of gastric cancer[18]. Currently, there is an effective modern global strategy for the prevention of gastric cancer. It was created by a collective group of scientists from various countries and regions. The model of the preventive program consists of successive stages of mass population serological screening, endoscopic diagnostics of precancerous diseases of the stomach, morphological verification of the carcinogenesis cascade and treatment measures. Making management decisions at different levels of state health care systems will allow the rapid implementation of the global strategy for the prevention of gastric cancer in practical medicine. As a result of the implementation of all components of the global strategy, a gigantic array of information will be obtained. It is impossible to process and analyze such a quantity of information without the help of artificial intelligence (AI). Endoscopists have the most success using AI to recognize endoscopic images of the stomach. Endoscopic diagnostics using AI and gastroscopes with enhanced imaging technologies in the ultrasonic spectrum of light are often used in practical gastroenterology. The developers of a deep convolutional neural network system called ENDOANGEL used six thousand two hundred and fifty endoscopic images from 760 patients and 98 video clips from 77 people. The ENDOANGEL computer-aided detection system demonstrated a high level of diagnostic accuracy in clinical practice. It had a diagnostic accuracy level for atrophic gastritis and intestinal metaplasia equal to that of experienced expert endoscopists and exceeded the diagnostic accuracy level of inexperienced endoscopists[19]. The position statement of the European Society of Gastrointestinal Endoscopy proposed nine criteria for assessing the effectiveness of AI for the diagnosis and treatment of gastrointestinal neoplasia. If these criteria are met, AI can be used in gastrointestinal endoscopy to implement gastroenterological diagnostic and treatment protocols[20]. The structure of AI used in medicine is shown in Figure 1.

Figure 1  Structure of computer technologies used in medicine.

USE OF MACHINE PROCESSING OF POPULATION SEROLOGICAL SCREENING RESULTS

There is less research on the use of machine learning and AI in laboratory testing. A neural network model based on a serological algorithm was trained using 120 measurements and applied to monitor coronavirus disease 2019 immunity during the pandemic[21]. Machine learning and deep learning models [logistic regression, extreme gradient boosting (XGBoost), LightGBM, random forest (RF), support vector machine (SVM) and CatBoost] have been used to support clinicians in decision-making to control arbovirus infections such as Dengue, Chikungunya and Zika in Latin America, South America and Pakistan[22,23]. The automated machine learning platform Machine Intelligence Learning Optimizer successfully functions to monitor immune responses in active tuberculosis. It is capable of generating and evaluating the performance of thousands of machine learning models. Machine Intelligence Learning Optimizer is a very promising platform for monitoring emerging infectious diseases[24]. The machine learning model (XGBoost + AdaBoost) successfully exploits six miRNAs (miR-106b, miR-146a, miR-15a, miR-18a, miR-21 and miR-93), biomarkers for the prediction and early detection of esophageal adenocarcinoma and Barrett’s esophagus[25]. Successful development and implementation of machine learning models for severe acute respiratory distress syndrome corona virus-2 monitoring facilitates effective prediction of the next pandemic[26]. Two blood work models (BW and BW + MAT) predicted leptospirosis and outperformed traditional serological screening in veterinary medicine. The second model, BW + MAT, incorporated the initial hospitalization MAT titer in addition to the patient features and clinicopathologic parameters[27]. Three machine learning models, RF, SVM and AdaBoost, demonstrated effectiveness in predicting hepatitis C virus infection based on serological testing[28]. SVM model for diagnosis based on serological markers of lung cancer has proven its effectiveness in preventing this disease[29]. The use of machine processing of the results of population serological screening for atrophic gastritis has significantly increased the productivity of gastric cancer prevention[30].

VISUALIZATION AND RECOGNITION OF ENDOSCOPIC GASTRIC IMAGES

AI has a greater use in gastric endoscopy. It is being implemented for image recognition of many types of gastric pathology. H. pylori infection diagnosis has 82% accuracy when using an AI model[31]. The largest number of AI models are created for the recognition of oncological gastric pathology. Convolutional neural network models are used to recognize images of advanced gastric cancer, early gastric cancer, high grade dysplasia, low grade dysplasia, and non-neoplasm. 5017 endoscopic gastric images from 1269 patients were collected to develop an effective diagnostic convolutional neural network model[32]. Modern endoscopic equipment with integrated AI increases the diagnostic capabilities of the examination[33]. Implementation of deep machine learning contributes to improved diagnostic performance in recognizing esophagogastroduodenoscopic images of gastric cancer[34]. Detection of focal pathological gastric lesions requires localization of neoplasms. Yuan et al[35] developed the AIMED model that uses MobileNetV3-large convolutional neural networks. From 160308 images, the authors selected 16031 test esophagogastroduodenoscopic images. The AIMED system allows identifying areas of gastric anatomy with an accuracy of 99.4%, sensitivity of 91.8%, and specificity of 99.7%[35]. The use of deep machine learning will allow AI to supplement accurate diagnosis of gastric cancer with effective treatment methods. The result of a comprehensive treatment and diagnostic approach can be personalized and effective[36]. The preliminary conclusion based on the existing scientific publications on the use of AI technologies in the recognition of endoscopic images of gastric cancer is that it significantly exceeds the visualization capabilities of experienced endoscopists. Further large-scale studies on the use of AI in the recognition of endoscopic images of gastric neoplasms are needed to confirm this position[37]. AI technologies in endoscopy have great potential for recognizing various gastric neoplasms, including early gastric cancer, subepithelial tumors, as well as gastrointestinal stromal tumors and neuroendocrine tumors[38]. The EGC-YOLO AI model was developed by the authors based on more than 40000 images of 1653 patients who underwent gastroscopy to diagnose early gastric cancer. The authors achieved diagnostic superiority of their EGC-YOLO system over other models because they correctly determined the threshold values of the evaluation criteria[39]. The advantages of detecting gastric cancer at the early stage of cancer rather than at the advanced stage are obvious. The five-year survival rate when detected at the advanced stage of cancer is less than 30%, while when detected at the early stage of gastric cancer, it is more than 90%. The accuracy, sensitivity and specificity indicators when using AI, in particular convolutional neural networks with deep machine learning, exceed those of endoscopists. Fu et al[40] propose to introduce endoscopic screening to improve the detection of early gastric cancer. According to Kikuchi et al[41], the use of endoscopic AI models will be useful in reducing the risk of missing pathological gastric changes, which will improve the diagnostic capabilities of endoscopists. The multimodal AI system can accurately detect submucosal tumors, gastric leiomyomas, gastric stromal tumors and gastric ectopic pancreas[42]. The ResNet34 and DeepLabv3 convolutional neural networks, which were trained on 21217 esophagogastroduodenoscopic images of the following gastric diseases: Gastric ulcer, early gastric cancer, high-grade intraepithelial neoplasia, advanced gastric cancer, gastric submucosal tumors, and normal gastric mucosa without lesions, reduced the workload of endoscopists[43]. Convolutional neural network with NBI technology enables better differentiation of gastric cancer from gastritis[44]. AI technologies are used much less frequently for endoscopic diagnostics of precancerous gastric pathology. Deep machine learning models were successfully used to diagnose atrophic gastritis with an accuracy of 96.4%, a sensitivity of 96.2%, a specificity of 96.4% (endoscopists with an accuracy of 64.8%, a sensitivity of 43.5%, a specificity of 75.1%), intestinal metaplasia with an accuracy of 97.6%, a sensitivity of 97.9%, a specificity of 97.5% (endoscopists with an accuracy of 77.6%, a sensitivity of 37.7%, a specificity of 91.3%), gastric cancer with an accuracy of 88.2%, a sensitivity of 97.1%, a specificity of 82.4% (endoscopists with an accuracy of 78.5%, a sensitivity of 86.5%, a specificity of 73.2%)[45]. Lin et al[46] used a convolutional neural network to successfully recognize endoscopic gastric images of atrophic gastritis and intestinal metaplasia. They collected 7037 images from 2741 patients for deep machine learning[46]. Other authors compared four DL neural networks (SOTA) for recognizing atrophic gastritis based on 10961 endoscopic images, 118 video clips from 4050 patients. They selected one GAM-EfficientNet as the most productive. Subsequent comparison with endoscopists demonstrated the superiority of AI over humans[47]. AI-based technologies have good prospects in the recognition of intestinal metaplasia[48]. The use of deep learning-based neural networks GoogLeNet, ResNet and ResNeXt has questionable prospects for the incorrect classification of gastric precancers such as gastric polyps, gastric ulcers and gastric erosions[49]. It is unlikely that AI will be effectively used for endoscopic differential diagnosis between early gastric cancer and gastric ulcer[50]. The use of AI technologies for the recognition of morphological images has good prospects.

VISUALIZATION AND RECOGNITION OF HISTOLOGICAL IMAGES

The use of AI technologies for the recognition of histological gastric images is promising. Parallel evaluation of endoscopic and histological images of gastric pathology will significantly improve the quality of diagnosis gastric neoplasms, such as early gastric cancer, subepithelial tumors, gastrointestinal stromal tumors, and neuroendocrine tumors[37,38]. Recently, a deep learning system was presented to recognize poorly differentiated gastric adenocarcinoma and well-differentiated gastric adenocarcinoma. The accuracy of the method reached 83.9%, 86.4%. According to the authors, such accuracy is high enough to recommend the use of AI in healthcare[51]. A large group of authors from the United States and Finland recently proposed an AI-based tool (Aiforia Technologies, Helsinki, Finland) for the recognition of histological images of eosinophilic esophagitis. This model represents a productive method of semi-automated quantitative analysis that can be effectively used for the morphological assessment of esophageal biopsies[52]. Scientists from Austria and Switzerland developed a deep learning neural network Attach-ANN (trained using WSI BCC) for efficient recognition of histological images of basal cell carcinoma of the skin. The sensitivity indicators were 0.97 [95% confidence interval (CI): 0.95-0.98], specificity: 0.91 (95%CI: 0.86-0.96), receiver operating characteristic analysis: 0.993 (95%CI: 0.990-0.995)[53]. Using a deep learning neural network model, pathologists have been able to classify histological patterns of lung adenocarcinoma and stratify the prognosis of patients with the disease[54]. The high level of accuracy when using convolutional neural network models (receiver operating characteristic analysis is 0.98) allows for effective classification of morphological quantitative assessment of intraglomerular pattern in nephrology[55]. British scientists have high hopes for graph neural networks and multimodal models in recognizing and analytically assessing morphological patterns of the tumor microenvironment[56]. In differential morphological diagnostics of various types of neoplasms, such as breast carcinoma, lymphosarcoma, lung carcinoma, neuroendocrine tumor, skin melanoma, skin mast cell tumor, soft tissue sarcoma, recognition of mitotic figures in large quantities (11937) is of great importance. Mitotic figures in tumor tissues have an important prognostic value. To process a large number of complex histological images, the authors used various scanners. Models based on deep machine learning allowed us to select the best neural network that managed to overcome the deterioration in performance due to domain shifts. Very relevant nuclear detection, segmentation and morphometric profiling using AI technologies[57-59]. Three neural networks including Densely Connected Convolutional Network 121, Residual Network, Inception_v3 and deep learning model can predict breast cancer metastasis based on the morphological image of mastectomy breast tissue samples. The area under the curve (AUC) of Inception_v3 model is 0.975[60]. A deep learning Refine Cascade Network model is proposed to determine the mitotic number which characterizes histological parameters for cancer classification[61]. Deep convolutional neural network (DC-Lym-AF) can recognize tumor-infiltrating lymphocytes that are capable of detecting and killing cancer cells in immunohistochemical images[62]. The applicability of ImageNet convolutional neural networks (Inception ResNet v2, Inception v3, ResNet152, Xception) for recognizing the histological picture of salivary gland cancer was demonstrated for the first time. The use of the Vision Transformer deep machine learning model will improve the efficiency of computer vision and computer diagnostic models for salivary gland neoplasms[63,64]. A promising model is the graph neural network SpaInGNN, which is a new spatial transcriptomics technology that allows characterizing gene expression patterns in tissue[65]. Current machine learning models have limitations in visualization. To overcome these limitations and improve the ability to classify and differentiate normal and abnormal histopathological images, ensemble models are created that combine the solutions of several deep learning models. Currently, ensemble deep learning models are the most effective in histopathology. GasHisSDB is a gastric histopathology image database containing 245196 images. GasHisSDB is divided into sub-databases, each containing two types of images: Normal and abnormal. Seven classic machine learning classifiers have been created for practical testing of image classification tasks. Three convolutional neural network classifiers and a new transformer-based classifier are recommended for use in practical work. When using traditional machine learning, the best accuracy rate is 86.08%, while when using deep learning, the accuracy of the diagnostic method is 96.47%[66-69]. A large number of scientific publications are devoted to the detection of gastrointestinal metaplasia and prediction of the risk of gastric cancer. The use of AI for endoscopic recognition of intestinal metaplasia has become very relevant. Li et al[70] in a systematic review and meta-analysis presented the results of 12 studies with 11173 patients, which demonstrate the effectiveness of various AI models in the diagnosis of intestinal metaplasia. The American Gastroenterological Association’s Clinical Practice Expert Review also provided best practice recommendations for the detection of intestinal metaplasia[71]. According to Liu et al[72], the GAM-EfficientNet models based on the Kyoto Gastritis Score are very promising in recognizing intestinal metaplasia. A unique method for recognizing intestinal metaplasia and gastric adenocarcinoma was proposed based on Raman spectroscopy. The deep machine learning model achieved diagnostic accuracy, sensitivity, specificity, and AUC values of 0.905, 0.942, 0.787, and 0.957, respectively[73]. The use of deep machine learning for pathological recognition of intestinal metaplasia has a high level of evaluation within the diagnostic classification of Operative Link for Gastritis Assessment. AI demonstrates the best overall performance in diagnosing intestinal metaplasia compared to pathologists[74]. Arai et al[75] created a combined model for endoscopic and histological recognition of intestinal metaplasia and atrophy of the gastric mucosa in accordance with the Operative Link for Gastritis Assessment/Operative Link for Intestinal Metaplasia Assessment classification. The precise role of each component within the Correa cascade remains a subject of ongoing discussion and investigation. While the significance of gastrointestinal metaplasia in the progression of gastric carcinogenesis has been questioned by some, Sugano et al[76] propose a contemporary concept of “metaplasia” that supports its involvement. This concept distinguishes between two distinct processes: Trans differentiation, which represents adaptive phenotypic changes in differentiated gastric mucosal cells, and transcommitment (true metaplasia), which involves the reprogramming of differentiation at the stem cell level. Critically, transcommitment can be categorized as complete (type I) or incomplete (types II and III). These trans-committed gastric glands can harbor multiple cell lineages, which contributes to the overall risk of gastric cancer development[76]. The heterogeneity of reprogrammed cell lineages within gastrointestinal metaplasia reflects the spectrum of gastric cancer risk. An elevated risk is associated with cell lineages transitioning between gastric and intestinal phenotypes. Conversely, cell lineages that fully transition to terminal metaplasia typically exhibit a benign outcome and a low risk of malignant transformation[77]. Therefore, the detection and diagnosis of intestinal metaplasia should be assessed differentially. The role of intestinal metaplasia in gastric carcinogenesis and prognosis of gastric cancer risk is associated with trans commitment (true metaplasia), which includes reprogramming of differentiation at the stem cell level.

USING AI FOR DIAGNOSTICS OF PRE-DISEASES, ASYMPTOMATIC DISEASES AND PRE-CLINICAL STAGES OF DISEASES

Detection of gastric cancer at the early cancer stage allows the five-year survival rate to exceed 90% after treatment. The five-year survival rate after treatment for gastric cancer is less than 30% if the cancer is diagnosed at a late stage. Japan was the first in the world to detect gastric cancer at an early stage using X-ray screening in the 1960s. Early gastric cancer cannot be detected based on clinical symptoms due to the lack of clinical symptoms at an early stage. There are also no symptoms at the stage of precancerous diseases with a high risk of gastric neoplasm[78]. The authors of a systematic review and meta-analysis showed that endoscopic screening can reduce gastric cancer mortality and does not affect the incidence and prevalence of gastric cancer. However, endoscopic screening did not significantly reduce mortality compared to expected mortality. In South Korea, where endoscopic screening is implemented under a state government program, it is opportunistic screening[79]. The new screening paradigm based on the introduction of endoscopic screening has many disappointments despite its effectiveness. The availability of endoscopic screening has serious organizational limitations: Financial burden, problem with the availability of endoscopists, invasiveness of the procedure, the need for sedation during implementation. The new screening concept based on serological testing for the detection of atrophic gastritis and the risk of developing gastric cancer convincingly proves its effectiveness. Firstly, serological screening using markers of gastric mucosa atrophy has a lower financial burden, secondly, it is non-invasive, thirdly, it is available in implementation. Therefore, serological screening can be mass, population-based. X-ray screening has not lost its relevance with the simultaneous use of serological screening[80]. Japanese guidelines for endoscopic screening require that it be performed at least twice a year for individuals over 50 years of age. However, the implementation of endoscopic screening for gastric cancer is associated with various difficulties. Only 19% were able to implement endoscopic screening on an opportunistic basis. The Japanese consider the implementation of endoscopic screening unlikely due to a shortage of endoscopists, the inability to reprocess endoscopes, the inability to control quality, and budgetary constraints. In South Korea, combined endoscopic and radiological screening is used to cover a larger number of the population. In Japan, the mortality rate from gastric cancer in 1958 was 55 among men and 33 among women per 100000 people. In 2018, the mortality rates were 44.8 and 24.1 per 100000 people, respectively. The authors associate the decrease in mortality over several decades mainly with a decrease in morbidity as a result of effective anti-Helicobacter measures. A new concept of gastric cancer prevention is created based on cancer risk stratification. Serological screening programs for atrophic gastritis with a high risk of developing gastric cancer are beginning to be intensively implemented into the global strategy for stratification of the risk of gastric cancer with subsequent endoscopic diagnostics and morphological verification of risk levels[81,82]. A systematic approach in the structure of the global strategy for gastric cancer risk stratification allows for increased adherence to population serological screening and increased population coverage with preventive measures[83]. I would like to express my hope that the implementation of a new global strategy for the prevention of gastric cancer consisting of three consecutive stages: Serological population screening for atrophic gastritis, the stage of endoscopic diagnosis of patients with a high risk of gastric cancer and morphological verification of the diagnosis will significantly reduce the incidence and mortality from this disease. The cost-effectiveness of such a strategy is obvious. The cost of 5523 € for serological screening allows for the exclusion of at least one case of gastric cancer over seven years[84]. By analogy with the prevention of gastric cancer, two-stage (laboratory-endoscopic) screening of colorectal cancer is being successfully introduced[85]. Wilson and Jungner[86] in 1968 defined the purpose, objectives and content of screening. In their basic scientific publication “Screening for Diseases” the authors identified very important principles: The condition sought should be an important health problem; there should be an accepted treatment for patients with recognized disease; facilities for diagnosis and treatment should be available; there should be a recognizable latent or early symptomatic stage; there should be a suitable test or examination; the test should be acceptable to the population; the natural history of the condition, including development from latent to declared disease, should be adequately understood; there should be an agreed policy on whom to treat as patients; the cost of case-finding (including diagnosis and treatment of patients diagnosed) should be economically balanced in relation to possible expenditure on medical care as a whole; case-finding should be a continuing process and not a “onceand for all” project. According to the principles of early detection of diseases, the screening examination method must meet the following criteria: Validity; reliability; yield; cost; acceptance; follow-up services. The authors distinguish the following types of screening: Mass (population) screening, selective screening and multiple (or multiphasic) screening. Extrapolation of the listed concepts to modern ideas about effective prevention of gastric cancer allows us to state that serological screening of atrophic gastritis is mass (population) screening, and endoscopic screening of cancerous and precancerous gastric pathology is selective screening after serological. Such a sequence of serological and endoscopic screening is the optimal variant of multiple (or multiphasic) screening[86]. Currently, there are AI models that can be used in gastric cancer prevention programs. They are presented in Table 1.

Table 1 List of studied artificial intelligence models.

Number	Models	Area of application
Laboratory models
1	Machine learning and deep learning models (logistic regression, XGBoost, LightGBM, random forest, support vector machine and CatBoost)	Serological control arbovirus infections such as Dengue, Chikungunya and Zika in Latin America, South America and Pakistan
2	Automated machine learning platform MILO	Monitoring active tuberculosis
3	Machine learning model (XGBoost + AdaBoost)	Early detection of esophageal adenocarcinoma and Barrett's esophagus
4	Machine learning models (BW and BW + MAT)	Serological screening for leptospirosis
5	Three machine learning models, random forest, support vector machine and AdaBoost	Prediction of hepatitis C infection
Endoscopic models
6	Deep convolutional neural network system ENDOANGEL	Endoscopic diagnostics of atrophic gastritis, intestinal metaplasia
7	AIMED model that uses MobileNetV3-large convolutional neural networks	Identification of the area of gastric anatomy
8	EGC-YOLO artificial intelligence model	Detecting gastric cancer at the early stage of cancer
9	MMP-AI system	Detection of submucosal tumors, gastric leiomyomas, gastric stromal tumors and ectopic gastric pancreas
10	GAM-Efficient Net Neural Network	Diagnosis of atrophic gastritis and intestinal metaplasia
Histological models
11	Artificial Intelligence Model Aiforia Technologies, Helsinki, Finland	Diagnosis of eosinophilic esophagitis
12	Deep learning neural network Attach-ANN (trained using WSI BCC)	Diagnosis of basal cell carcinoma of the skin
13	ImageNet convolutional neural networks (Inception ResNet v2, Inception v3, ResNet152, Xception) based on Vision Transformer deep machine learning model	Salivary gland neoplasm diagnostics
14	Graph neural network SpaInGNN	Transcriptomics technology that allows characterizing gene expression patterns in tissue
15	GasHisSDB	Gastric histopathology

XGBoost: Extreme gradient boosting; MILO: Machine Intelligence Learning Optimizer; BW: Blood work models; MAT: Model, BW + MAT, incorporated the initial hospitalization MAT titer in addition to the patient features and clinicopathologic parameters; MMP-AI: Multimodal multipathway artificial intelligence.

AI PROVIDES NEW TECHNOLOGIES FOR SEROLOGICAL, ENDOSCOPIC AND PATHOLOGICAL DIAGNOSTICS IN THE PREVENTION OF GASTRIC CANCER

For the practical implementation of various models of AI, not only diagnostic, but also prognostic capabilities of computer algorithms are of great importance. Usually, several models are developed, then based on comparative characteristics, one, the best model is selected and used in practical medicine. For example, to predict thrombus formation after surgery for gastric cancer, two best models were selected from four based on the following criteria: SVM and XGBoost had AUC of 0.915 and 0.869, respectively, RF and naive Bayesian models - AUC of 0.928[87,88]. Hospitals and clinics are increasingly creating and implementing medical digital technologies, including AI, to effectively conduct their practice. Mayo Clinic is a good example of the use of modern medical technologies based on the novelty achievements. Mayo Clinic created a Council for the review of software as a medical device in 2022. Any medical institution can create this structure in accordance with best practices[89]. The use of modern digital technologies in medicine should be regulated and controlled by bioethical laws. Ethical problems of the implementation of AI models in medicine have six categories: (1) The ethics of machine learning; (2) The ethics of machine accuracy; (3) The ethics associated with the patient; (4) The ethics associated with the doctor; (5) General bioethics; and (6) The role of regulatory organizations[90]. The integration of digital technologies into healthcare has a strong impact on patient rights, such as privacy and data security. Ensuring trust in the results obtained by software and decisions made by AI plays a very important role. Liability and intellectual property are the main legal issues that need to be addressed[91]. Wang et al[92] in a systematic review presented the most effective Consensus Statements and Standards for the Use of AI in Medicine. The authors gave them a reasonably high rating. To confirm their field of view, they used the AGREE II and RIGHT Tools[92]. The introduction of digital technologies and AI convincingly brings the revolution in the field of medicine closer. Bioethical regulation should increase the safety and efficiency of the implementation of the most modern technologies[93].

CONCLUSION

AI technologies are being intensively developed and implemented in practical medicine. The use of neural networks with deep machine learning is very relevant in laboratory diagnostics, diagnostic endoscopy and morphological diagnostics. To successfully solve the problems of stomach cancer prevention, it is necessary to improve many technologies, namely, the detection of gastric neoplasms at an early stage, timely detection of precancerous gastric diseases and precancerous changes in the gastric mucosa, implementation of various methods of population, selective and multiple (or multiphasic) screening. Obstacles include significant financial burden, shortage of medical personnel, long time for the implementation of professional competencies by specialists, human factor productivity. Rapid and widespread implementation of AI technologies will allow us to overcome all obstacles and achieve a significant reduction in the incidence and mortality rate from gastric cancer.