One of the most common eye diseases in the world is the cause of more than half of blindness and imposes multi -billion dollars annually on health systems.
The disease, which threatens the eye lens, has no obvious symptoms in the early stages and is often diagnosed when the patie’s vision has declined sharply.
According to the World Health Organization, millions of people around the world are dealing with the disease, but in low -income areas, access to screening and early diagnosis is still limited.
Recely, a study led by Maryam Karwashan from Fasa University of Medical Sciences, in collaboration with Kerman University of Medical Sciences, has shown that artificial ielligence can make a change in the diagnosis and manageme of cataracts. This study evaluates the efficiency of machine learning algorithms such as twisting neural networks, deep learning and decision tree by systematic review of scieific articles from Pubmed, Scopus and Web of Science.
The results indicate that these algorithms were effective in diagnosis, 2 % for manageme, and 2 % for the disease, and the torsional neural networks, especially in accurate diagnosis, have superior performance.
The findings of this study show that artificial ielligence not only increases the accuracy and speed of the diagnosis of cataracts, but can also fill the medical service gap in deprived areas.
Simple AI -based tools, such as mobile apps, can help personnel in remote areas to ideify suspicious paties. This technology has the poteial to become part of the health screening programs, especially in developing couries.
The article is published in the two mohs of “Health Meaning” at Tehran University of Medical Sciences and is an importa step towards using new technologies to improve public health.
