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Unveiling the Role of Artificial Intelligence (AI) in Polycystic Ovary Syndrome (PCOS) Diagnosis: A Comprehensive Review

  • Reproductive Endocrinology: Review
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Abstract

Polycystic Ovary Syndrome (PCOS) is one of the most widespread endocrine and metabolic disorders affecting women of reproductive age. Major symptoms include hyperandrogenism, polycystic ovary, irregular menstruation cycle, excessive hair growth, etc., which sometimes may lead to more severe complications like infertility, pregnancy complications and other co-morbidities such as diabetes, hypertension, sleep apnea, etc. Early detection and effective management of PCOS are essential to enhance patients' quality of life and reduce the chances of associated health complications. Artificial intelligence (AI) techniques have recently emerged as a popular methodology in the healthcare industry for diagnosing and managing complex diseases such as PCOS. AI utilizes machine learning algorithms to analyze ultrasound images and anthropometric and biochemical test result data to diagnose PCOS quickly and accurately. AI can assist in integrating different data sources, such as patient histories, lab findings, and medical records, to present a clear and complete picture of an individual's health. This information can help the physician make more informed and efficient diagnostic decisions. This review article provides a comprehensive analysis of the evolving role of AI in various aspects of the management of PCOS, with a major focus on AI-based diagnosis tools.

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Data Availability

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Abbreviations

Ada Boost:

Adaptive Boosting

AI:

Artificial intelligence

AITAH:

Artificial Intelligence for Targeted Analysis of Histology

AMH:

Anti- mullerian hormone

ANN:

Artificial neural network

BMI:

Body Mass Index

CART:

Classification and Regression Trees

CAT boost:

Categorical Boosting

CD:

Cluster of differentiation

CGSVM:

Coarse Gaussian SVM

CNN:

Convolution Neural Network

CSVM:

Cubic SVM

DL:

Deep learning

DT:

Decision Tree

ER:

Endometrial receptivity

FGSVM:

Fine Gaussian SVM

FN:

False Negative

FP:

False Positive

FSH:

Follicle stimulating hormone

GIST:

Global Image Structure tensor

GLCM:

Grey Level Co-Occurrence Matrix

GLP-1:

Glucagon like peptide-1

IDE:

Integrated development environment

IFFOA-ANN:

Improved Fruit Fly Optimization- artificial neural network

IVF:

in vitro Fertilization

KNN:

K-Nearest Neighbor

LF:

Leavy flight

LH:

Luteinizing hormone

LR:

Logistic Regression

LSVM:

Linear SVM

MGSVM:

Medium Gaussian SVM

ML:

Machine learning

NB:

Naïve bayes

OTSU:

Operational Test Support Unit

PCA:

Principal component analysis

PCOS:

Polycystic Ovary Syndrome

QSVM:

Quadratic SVM

REF:

Recursive feature elimination

RF:

Random forest

SIFT:

Scale-Invariant Feature Transform method

SVM:

Support Vector Machine

TB:

Tongue body

TC:

Tongue coating

TN:

True Negative

TOPSIS:

TEchnique for Order of Preference by Similarity to Ideal Solution

TP:

True Positive

UCI ML:

University of California Irvine machine learning repository

USG:

Ultrasonography

WHO:

World Health Organization

WSI:

Whole slide images

XGBoot:

Extreme Gradient Booting

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  1. Pulkit Verma and Pratibha Maan contributed equally to this work.

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    Pulkit Verma, Pratibha Maan, Rohit Gautam & Taruna Arora

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Verma, P., Maan, P., Gautam, R. et al. Unveiling the Role of Artificial Intelligence (AI) in Polycystic Ovary Syndrome (PCOS) Diagnosis: A Comprehensive Review. Reprod. Sci. (2024). https://doi.org/10.1007/s43032-024-01615-7

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