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Polycystic Ovary Syndrome

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Early Predictors of Gestational Diabetes Mellitus in IVF-Conceived Pregnancies.

Endocrine practice : official journal of the American College of Endocrinology and the American Association of Clinical Endocrinologists
OBJECTIVE: Gestational diabetes mellitus (GDM) is associated with adverse maternal and fetal outcomes. This study aimed to identify early and reliable GDM predictors that would enable implementation of preventive and management measures.

Deep Learning Algorithm for Automated Detection of Polycystic Ovary Syndrome Using Scleral Images.

Frontiers in endocrinology
The high prevalence of polycystic ovary syndrome (PCOS) among reproductive-aged women has attracted more and more attention. As a common disorder that is likely to threaten women's health physically and mentally, the detection of PCOS is a growing pu...

Prediction of PCOS and Mental Health Using Fuzzy Inference and SVM.

Frontiers in public health
Polycystic ovarian syndrome (PCOS) is a hormonal disorder found in women of reproductive age. There are different methods used for the detection of PCOS, but these methods limitedly support the integration of PCOS and mental health issues. To address...

Machine Learning to Identify Metabolic Subtypes of Obesity: A Multi-Center Study.

Frontiers in endocrinology
BACKGROUND AND OBJECTIVE: Clinical characteristics of obesity are heterogenous, but current classification for diagnosis is simply based on BMI or metabolic healthiness. The purpose of this study was to use machine learning to explore a more precise ...

Raman spectroscopy of follicular fluid and plasma with machine-learning algorithms for polycystic ovary syndrome screening.

Molecular and cellular endocrinology
Polycystic ovary syndrome (PCOS) is the main cause of anovulatory infertility and affects women throughout their lives. The specific diagnostic method is still under investigation. In the present study, we aimed to identify the metabolic tracks of th...

Polycystic ovary syndrome: clinical and laboratory variables related to new phenotypes using machine-learning models.

Journal of endocrinological investigation
PURPOSE: Polycystic Ovary Syndrome (PCOS) is the most frequent endocrinopathy in women of reproductive age. Machine learning (ML) is the area of artificial intelligence with a focus on predictive computing algorithms. We aimed to define the most rele...

Artificial intelligence deep learning model assessment of leukocyte counts and proliferation in endometrium from women with and without polycystic ovary syndrome.

F&S science
OBJECTIVE: To study whether artificial intelligence (AI) technology can be used to discern quantitative differences in endometrial immune cells between cycle phases and between samples from women with polycystic ovary syndrome (PCOS) and non-PCOS con...

Application of machine learning and artificial intelligence in the diagnosis and classification of polycystic ovarian syndrome: a systematic review.

Frontiers in endocrinology
INTRODUCTION: Polycystic Ovarian Syndrome (PCOS) is the most common endocrinopathy in women of reproductive age and remains widely underdiagnosed leading to significant morbidity. Artificial intelligence (AI) and machine learning (ML) hold promise in...

Fuzzy machine learning logic utilization on hormonal imbalance dataset.

Computers in biology and medicine
In this research work, a novel fuzzy data transformation technique has been proposed and applied to the hormonal imbalance dataset. Hormonal imbalance is ubiquitously found principally in females of reproductive age which ultimately leads to numerous...

Nutritional management recommendation systems in polycystic ovary syndrome: a systematic review.

BMC women's health
BACKGROUND: People with polycystic ovary syndrome suffer from many symptoms and are at risk of developing diseases such as hypertension and diabetes in the future. Therefore, the importance of self-care doubles. It is mainly to modify the lifestyle, ...