Title: Study of Cardiovascular Diseases in the Retina, Obtained by Optical Coherence Tomography
Abstract: With the rapid development of artificial intelligence technology, the degree of integration of medicine and artificial intelligence is increasingly strengthened, and in the medical field, due to the aggravated aging of population in China, the impact of cardiovascular risk factors on the residents' health has become more and more significant. In our country, diseases of the cardiovascular system are the main cause of death every year, almost on a par with the consequences of COVID-19, cardiovascular diseases annually claim the lives of more than 2.5 million people a year. This number is steadily growing every year. The negative upward trend in the incidence of the cardiovascular system increases the relevance of the proposed work. For this reason, the object of study of the proposed article was the study of pathologies of the cardiovascular system.The main object of the research is an automated artificial intelligence system for the study of three types of cardiovascular diseases (CVD) in the retina (hypertensive retinopathy, retinal artery occlusion, ischemic neuropathy), obtained by photographing the fundus using modern artificial intelligence algorithms (Deep Neural Networks (DNN).This paper aimed to develop an intelligent system for the study of cardiovascular pathologies (hypertensive retinopathy, retinal artery occlusion, ischemic neuropathy) based on images of the retina of the fundus with the possibility of diagnosing some complications.As a result, the study of three types of cardiovascular diseases based on images of optical coherence tomography, implemented with a machine learning module for predicting the risk of developing the disease with diagnostic and consulting functionality. The model showed an accuracy of 92% which obtained results are good performance indicators of the system.
Keywords: optical coherence tomography;artificial intelligence;cardiovascular diseases;retina;neural networks;pattern recognition
Review score: High
Review comment: The article details the experimental approach, including data collection, preprocessing, neural network implementation, and analysis. It discusses the use of optical coherence tomography (OCT) for capturing retinal images and the application of advanced neural network models for image analysis, providing a comprehensive view of the experimental setup and methodology. However, more detailed information on input and output parameters, as well as specific diagrams and real data results, would enhance the experimental clarity. 
Conclusion and Recommendation
The article effectively combines engineering and IT solutions to address a significant healthcare issue, demonstrating a comprehensive methodology and presenting meaningful results. It meets the IEEE's criteria for content, structure, and scientific rigor.
Recommendation: "Accepted with minor revision" - The article should be recommended for publication after including more detailed experimental data, such as specific input/output parameters, additional diagrams, and real data(required only if possible within the short period of time that this article is accepted) results, to further substantiate the findings.