Role of artificial intelligence in PCOS detection

Abstract

Polycystic Ovary Syndrome (PCOS) is an endocrinal disorder which affects females aged between 12 and 45 years. It is the disorder in which the cyst is formed in the ovary. The oocyte does not get mature at its natural which and form a fluid-filled sacs known as cyst. When there are many cysts in the ovary, it is then known as polycystic ovary. It may affect both the ovaries. It causes absent menstrual cycles, weight gain, hirsutism, pigmentation, and decrease in hair volume. There are some studies which say that this is a lifestyle disorder, but the main reason is not known yet. PCOS leads to an obstacle in conceiving. It can be suppressed by some changes in lifestyle patterns such as daily exercise and food patterns. Artificial intelligence (AI) is a science and engineering subject that deals with intelligent behavior. It is a subfield of computer science that has improved human existence in a variety of ways. AI is a combination of reasoning, learning, problem-solving perception, and language understanding. A general introduction to the subject of AI creates a new revolution in the world and creates a great scope in future to describe machines that mimic human nature in association with “cognitive” functions of human mind, such as “learning” and “problem-solving.”AI is the technique in which the human work is totally handled by machines. In various domains, AI has recently outperformed humans, and there is enormous potential in healthcare. The health-care system deals with a massive volume of data that is difficult to examine using standard approaches. AI's success in health-care offers improved illness prevention, detection, diagnosis, and treatment. There are many inventions in machines which can take over the manual work. The AI can reduce the percentage of human error and provides the best and fast result. Together, human people and innovation may pave the road for better health-care services. The ability for a system to automatically learn and improve is provided by machine learning, a branch of AI logically planned. Its main objective is to create new machine learning algorithms that allow users to access specific datasets and use the information for analysis and research the unstructured. Applications of machine learning support significant change, particularly in businesses such as health care that deal with data identification, image recognition, prediction, and identification. Much critical attention has been paid to PCOS screening. In order to address this problem, the current study was created to investigate a noninvasive way to aid in PCOS screening. Our research demonstrates that the suggested algorithm successfully detects PCOS (mean area under the curve of 0.978), suggesting that deep learning may be a potent technique for PCOS identification. In addition, research findings may suggest the exceptional potential of using scleral pictures to diagnose diseases. A fruitful study area may emerge from the integration of AI and characteristics taken from scleral pictures. This article mainly is about PCOS and the role of AI for its diagnosis and better results. The transvaginal ultrasound machine is a noninvasive means of examining the human ovary to show important aspects for PCOS diagnosis. The key characteristics that distinguish ovarian pictures are the number of follicles and their diameters. As a result, PCOS is diagnosed by manually counting follicles and measuring their diameters. This procedure is time-consuming, labor-intensive, and prone to errors. So to make this process easy and error free the introduction of AI is needed.