scholarly journals Regularities of the Natural Evolution of the Proliferative Process in Diabetic Retinopathy. The Concept of Remission of Proliferative Diabetic Retinopathy

2021 ◽  
Vol 18 (4) ◽  
pp. 763-768
Author(s):  
S. V. Sdobnikova

The data analyzed in this review indicate that an important feature of the natural evolution of diabetic retinopathy (DR) is the possibility of reverse development of its main signs, including newly formed vessels. The term “spontaneous remission”, proposed by M.D. Davis, may be correct for stating this condition. Spontaneous remission can be persistent and its frequency can significantly exceed the generally accepted 10 %. Signs of remission of proliferative diabetic retinopathy (PDR), regardless of the cause of occurrence (spontaneous or resulting from treatment) are: absence of ophthalmoscopically detectable neovessels; increased/appearance of the fibrous component of proliferation, which is accompanied by traction deformation of the retina. Therefore, the scale reflecting the stages of evolution of newly formed vessels and the scale of severity reflecting the degree of threat to visual functions in PDR cannot be identical. Since the development and regression of neovessels is a reflection of multidirectional processes, the identification of the phase of PDR evolution is fundamental in the formation of research design. Due to the possibility of using artificial intelligence for the analysis of “big data”, the effectiveness of the approach to the study of DR will largely be determined by the adequacy of the grouping of the source data. In this regard, the analysis of previous experience is relevant, which allows us to improve some principles of systematization of results. Conclusion: The statement of the phase of evolution of neovessels in PDR is fundamental in epidemiological and scientific studies. The identification of signs indicating the likelihood of spontaneous remission of DR/PDR will allow us to provide a differentiated approach to treatment, as well as to study the association with the dynamics of the patient’s somatic status.

2022 ◽  
pp. 30-57
Author(s):  
Richard S. Segall

The purpose of this chapter is to illustrate how artificial intelligence (AI) technologies have been used for COVID-19 detection and analysis. Specifically, the use of neural networks (NN) and machine learning (ML) are described along with which countries are creating these techniques and how these are being used for COVID-19 diagnosis and detection. Illustrations of multi-layer convolutional neural networks (CNN), recurrent neural networks (RNN), and deep neural networks (DNN) are provided to show how these are used for COVID-19 detection and prediction. A summary of big data analytics for COVID-19 and some available COVID-19 open-source data sets and repositories and their characteristics for research and analysis are also provided. An example is also shown for artificial intelligence (AI) and neural network (NN) applications using real-time COVID-19 data.


Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 602-P
Author(s):  
NISHIT UMESH PAREKH ◽  
MALAVIKA BHASKARANAND ◽  
CHAITHANYA RAMACHANDRA ◽  
SANDEEP BHAT ◽  
KAUSHAL SOLANKI

Sign in / Sign up

Export Citation Format

Share Document