COMPUTER APPLICATION
Artificial intelligence in polycystic ovarian syndrome management: past, present, and future
Integrating artificial intelligence (AI) in the clinical management of polycystic ovary syndrome (PCOS) promises significant improvements in efficiency, interpretability, and generalizability. This review delineates AI-driven interventions in PCOS across diverse clinical contexts, focusing on prediction, diagnosis, classification, and screening of complications.
Executive Impact: AI's Transformative Power in PCOS Management
AI-based analytics are profoundly transforming PCOS management by offering unprecedented accuracy and efficiency in various clinical domains.
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Increase in PCOS Prevalence (Last Decade)
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SVM Model Predictive Accuracy (AUC)
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Metabolomic Diagnostic Precision (Sen & Spec)
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Automated PCOM Classification Accuracy
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Prediction & Prevention
Diagnosis & Distinction
Classification & Evaluation
Screening & Patient Education
AI-Driven PCOS Management Framework
Prediction and Prevention
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Diagnosis and Distinction
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Classification and Evaluation
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Screening complications and Educating Patients
Automated
AI Reduces Labor & Time in PCOS Prediction
AI algorithms automate PCOS prediction, significantly reducing the labor intensity and time required compared to traditional epidemiological research avenues, enabling broader screening and earlier intervention.
| Model Type | Key Features | Accuracy (Acc) | AUC |
|---|---|---|---|
| LSTM (Ahmad R, 2024) | DL methods, Kaggle repository | 96.59% | 96.6% |
| SVM (Fu J, 2024) | Genetics data | 70.1% | 78.1% |
| RF (Tan C, 2024) | Genetics data (GEO) | N/A | 96% |
| Stacking Ensemble (Alam Suha, S, 2023) | Clinical data, symptoms | 95.7% | 0.95 |
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Annual PCOS Incidence (2019)
The annual incidence of PCOS increased significantly from 1.4 million in 1990 to 2.1 million in 2019, underscoring the growing global burden and the need for scalable AI interventions.
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VGG16 Accuracy in PCOM Ultrasound Classification
The VGG16 deep learning model demonstrated exceptional accuracy in classifying Polycystic Ovary Morphology (PCOM) from ultrasound images, significantly aiding PCOS diagnosis and reducing human error.
Case Study: SVM Achieves Perfect AUC in Cuproptosis Cluster Identification
A Support Vector Machine (SVM) model, developed on genetics data, performed satisfactorily in exploring cuproptosis-related molecular clusters of PCOS, achieving an impressive 100% AUC. This highlights AI's capability for precise molecular classification in diagnostics, offering a new avenue for understanding PCOS pathogenesis.
This level of precision in identifying disease-specific molecular signatures can significantly enhance early diagnosis and targeted therapeutic strategies for PCOS patients.
| Model Type | Data Type | Accuracy | AUC |
|---|---|---|---|
| SVM (Elmannai H, 2023) | Clinical data (Kaggle) | 98.87% | N/A |
| CNN (Emanuel RHK, 2023) | Reddit posts (lab results) | 98% | N/A |
| XGBoost (Wang F, 2024) | Clinical data, OVGP1, hormones | N/A | 95.3% (training), 90.7% (testing) |
Subtypes Identified
AI Delineates Distinct PCOS Phenotypes for Personalized Care
AI algorithms successfully identify distinct PCOS subtypes (e.g., metabolic, reproductive, and background) based on clinical features, crucial for guiding more personalized and effective treatment strategies.
| Model Type | Data Type | Key Finding | Accuracy/Outcome |
|---|---|---|---|
| RF (Dapas M, 2020) | Genome-wide, biochemical, genotype | Identified original subtype clusters | Lowest mean misclassification rate (13.2%) |
| SVM (Silva IS, 2022) | Clinical data, risk factors | Stratified patients into phenotypic clusters | Acc = 86.2% |
| OPLS-DA (Fulghesu AM, 2021) | Urine metabolomics | Identified urinary biomarkers of IR | Good fit (R2 = 0.813) |
90.1% AUC
AI's High Predictive Value for Preterm Birth in PCOS
AI models demonstrate strong predictive capabilities for adverse obstetrical outcomes in PCOS, specifically achieving an AUC of 90.1% for preterm birth, enabling proactive patient management and risk mitigation.
| Aspect | English Version | Turkish Version |
|---|---|---|
| Mean Modified DISCERN Score | 27.6 ± 0.87 | 27.2 ± 0.87 |
| Global Quality Score | 100% | 90.9% |
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ChatGPT-4 Accuracy in Answering PCOS Questions
ChatGPT-4 achieved an accuracy of 68.2% in answering PCOS-related questions based on current clinical guidelines, demonstrating its potential for patient education and support, though further refinement is needed.
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Your AI Implementation Roadmap
A strategic, phased approach to integrating AI into your enterprise, ensuring maximum impact and smooth transition.
Phase 01: Discovery & Strategy
Comprehensive analysis of current workflows, data infrastructure, and business objectives to define AI opportunities and a tailored implementation strategy.
Phase 02: Data Foundation & Integration
Establish secure data pipelines, cleanse and standardize data, and integrate AI models with existing systems, prioritizing patient privacy (e.g., Federated Learning).
Phase 03: Model Development & Training
Custom development and rigorous training of AI models using supervised and unsupervised learning techniques to achieve high accuracy in PCOS prediction, diagnosis, and management.
Phase 04: Pilot & Validation
Deploy AI solutions in a controlled pilot environment, validate performance against clinical benchmarks, and gather feedback for iterative refinement.
Phase 05: Scaled Deployment & Monitoring
Full-scale integration of AI across clinical contexts, continuous monitoring for performance and drift, and ongoing optimization to ensure sustained impact and value.
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