Authors : Ramya R; Dr. Darshan Sohi

Volume/Issue : Volume 10 - 2025, Issue 5 - May

Google Scholar : https://tinyurl.com/yeym534j

DOI : https://doi.org/10.38124/ijisrt/25may2009

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Polycystic Ovary Syndrome (PCOS) is an endocrinal disorder which impacts females aged among 12 and forty five years. it's far the sickness wherein the cyst is fashioned inside the ovary. The oocyte does not get mature at its herbal which and form a fluid_stuffed sacs referred to as cyst. while there are numerous cysts within the ovary, it's miles then known as polycystic ovary. it is able to have an effect on each the ovaries. It causes absent menstrual cycles, weight gain, hirsutism, pigmentation, and decrease in hair quantity. There are some research which say that this is a way of life disease, but the essential motive is not known but. PCOS ends in an obstacle in conceiving. it can be suppressed via a few changes in lifestyle styles inclusive of every day exercise and meals patterns. artificial intelligence (AI) is a science and engineering concern that offers with sensible conduct. it's miles a subfield of computer technology that has progressed human life in a selection of ways. AI is a combination of reasoning, studying, hassle_solving notion, and language understanding. A general advent to the concern of AI creates a brand new revolution in the global and creates a great scope in destiny to describe machines that mimic human nature in association with “cognitive” capabilities of human mind, together with “getting to know” and “trouble_solving.”AI is the method in which the human paintings is completely handled by machines. In numerous domains, AI has recently outperformed people, and there is substantial capability in healthcare. The health_care gadget offers with a huge volume of statistics that is hard to have a look at using popular tactics.AI’s achievement in fitness_care offers improved contamination prevention, detection, prognosis, and treatment. There are many innovations in machines which could take over the manual work. The AI can lessen the percentage of human blunders and affords the satisfactory and fast end result. collectively, human human beings and innovation might also pave the street for higher fitness_care services. The capability for a system to robotically analyze and improve is furnished with the aid of gadget mastering, a branch of AI logically deliberate. Its predominant objective is to create new system gaining knowledge of algorithms that allow customers to get admission to unique datasets and use the data for evaluation and research the unstructured. packages of gadget mastering guide massive alternate, especially in corporations which include fitness care that address data identification, photograph recognition, prediction, and identification. a whole lot essential attention has been paid to PCOS screening. so as to cope with this trouble, the current take a look at changed into created to research a noninvasive way to useful resource in PCOS screening. Our studies demonstrates that the advised algorithm efficiently detects PCOS (suggest place under the curve of zero.978), suggesting that deep getting to know may be a robust technique for PCOS identification. in addition, research findings might also suggest the exceptional capacity of the usage of scleral pictures to diagnose illnesses. A fruitful look at vicinity may additionally emerge from the mixing of AI and characteristics taken from scleral pics. this newsletter specifically is ready PCOS and the role of AI for its prognosis and better effects. The transvaginal ultrasound system is a noninvasive manner of analyzing the human ovary to reveal vital components for PCOS analysis. the key traits that distinguish ovarian pictures are the variety of follicles and their diameters. As a result, PCOS is diagnosed with the aid of manually counting follicles and measuring their diameters. This system is time_eating, labor_extensive, and prone to mistakes. so that you can make this manner clean and mistakes loose the creation of AI is needed.

Keywords : Follicle, Hirsutism, Polycystic Ovary Syndrome, Transvaginal Ultrasound Machines.

References :

1. Norman RJ, Dewailly D, Legro RS, Hickey TE. Polycystic ovary syndrome. Lancet 2007;370:685‑97.
2. Available from: https://en.wikipedia.org/wiki/Artificial_intelligence. [Last retrieved on 2019 Oct 15].
3. Available from: https://www.javatpoint.com/introduction‑to‑artificial‑intelligence. [Last retrieved on 2019 Oct 15].
4. Brown ZA, Louwers YV, Fong SL, Valkenburg O, Birnie E, de Jong FH,et al. The phenotype of polycystic ovary syndrome ameliorates withaging. Fertil Steril 2011;96:1259‑65.
5. Bellver J, Rodríguez‑Tabernero L, Robles A, Muñoz E, Martínez F,Landeras J, et al. Polycystic ovary syndrome throughout a woman’s life.J Assist Reprod Genet 2018;35:25‑39.
6. York G, Kim Y. Ultrasound processing and computing: Review andfuture directions. Annu Rev Biomed Eng 1999;1:559‑88.
7. Tegnoor JR. Automated ovarian classification in digital ultrasound images using SVM. Int J Eng Res Technol 2012;1:1‑17.
8. Catteau‑Jonard S, Bancquart J, Poncelet E, Lefebvre‑Maunoury C,Robin G, Dewailly D. Polycystic ovaries at ultrasound: Normal variant or silent polycystic ovary syndrome? Ultrasound Obstet Gynecol 2012;40:223‑9.
9. Vause TD, Cheung AP, Reproductive Endocrinology and Infertility Committee. Ovulation induction in polycystic ovary syndrome. J Obstet Gynaecol Can 2010;32:495‑502.
10. Mehrotra P, Chakraborty C, Ghoshdastidar B, Ghoshdastidar S, Ghoshdastidar K. Automated Ovarian Follicle Recognition for Polycystic Ovary Syndrome International Conference on Image Information Processing (ICIIP); 2011. p. 1‑4.
11. Availablefrom:https://www.thelancet.com/journals/lancet/article/PIIS0140673607613452/fulltext. [Last accessed on 2021 Oct 06].
12. Chen T, Zhang W, Good S, Zhou KS, Comaniciu D. In Computer Vision, IEEE Automatic Follicle Quantification from 3D Ultrasound Data Using Global/Local Context with Database Guided Segmentation 12th International Conference; 2009. p. 795‑802.
13. Deepika V. Applications of artificial intelligence techniques in polycystic ovarian syndrome diagnosis. J Adv Res Technol Manag Sci 2019;1:59‑63.
14. Jyothi S, Vasavi G. Polycystic ovary syndrome detection using various machine learning methods – A review. J Adv Res Dyn Control Syst 2017;5:334‑9.
15. Soni P, Vashisht S. Image segmentation for detecting polycystic ovarian disease using deep neural networks. Int J Comput Sci Eng Open Access 2019;7:2347‑693.
16. Faghih RT, Styer AK, Brown EN. Automated ovarian follicular monitoring: A novel real‑time approach. Annu Int Conf IEEE Eng Med Biol Soc 2017;2017:632‑5.
17. Jyothi S, Vasavi G. Polycystic ovary syndrome detection using various machine learning methods – A review. J Adv Res Dyn Control Syst 2017;5:234-9. Available from: https://medium.com/cossette‑health/ blockchain‑inhealth‑care‑bbc543fb58e. [Last retrieved on 2018 Dec 28].
18. Available from: https://www.healthline.com/health/ polycystic‑ovary‑disease#diagnosis. [Last retrieved on 2019 Oct 14].
19. Thakre V. PCOcare: PCOS detection and prediction using machine learning algorithms. Biosci Biotechnol Res Commun 2020;13:240‑4.
20. Lv W, Song Y, Fu R, Lin X, Su Y, Jin X, et al. Deep learning algorithm for automated detection of polycystic ovary syndrome using scleralimages. Front Endocrinol (Lausanne) 2021;12:789878