Optimized polycystic ovarian disease prognosis and classification using AI based computational approaches on multi-modality data

• Published in: BMC medical informatics and decision making Vol. 24; no. 1; pp. 281 - 22
• Main Authors: Shanmugavadivel, Kogilavani, M S, Murali Dhar, T R, Mahesh, Al-Shehari, Taher, Alsadhan, Nasser A, Yimer, Temesgen Engida
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 01.10.2024; BioMed Central; BMC
• Subjects: Abnormalities; Accuracy; Acne; Adult; Algorithms; Artificial Intelligence; Artificial neural networks; Biochemical tests; Blood tests; Cancer; Cardiovascular disease; Care and treatment; Classification; CNN; Cysts; Datasets; Deep learning; Diabetes; Diabetes mellitus; Diagnosis; Fast food; Feature extraction; Female; Females; Follicles; Genetics; Gynecology; Health aspects; Hirsutism; Hormones; Humans; Infertility; Learning algorithms; Machine learning; Medical prognosis; Menstruation; Methods; Neural networks; Ovarian cancer; Ovaries; Performance evaluation; Polycystic ovary syndrome; Polycystic Ovary Syndrome - diagnosis; Prognosis; Signs and symptoms; Stein-Leventhal syndrome; Support vector machines; Testosterone; Transfer learning; Ultrasonic imaging; Ultrasonography; Ultrasound; Uterine cancer; VGG16; Womens health; India; Deep learning; Polycystic ovary syndrome; CNN; Clinical features; Transfer learning; VGG16; Machine learning; Ultrasound images

Polycystic Ovarian Disease or Polycystic Ovary Syndrome (PCOS) is becoming increasingly communal among women, owing to poor lifestyle choices. According to the research conducted by National Institutes of Health, it has been observe that PCOS, an endocrine condition common in women of childbearing a...