Clinical Report: AutoPCOS: An Integrated Intelligent Framework for Risk Assessment

Overview

The AutoPCOS framework offers a multimodal approach for PCOS risk assessment and diagnosis, demonstrating strong predictive performance across various data availability scenarios. It integrates clinical, laboratory, and ultrasound data to enhance diagnostic accuracy and accessibility.

Background

Polycystic ovary syndrome (PCOS) is a common endocrine disorder affecting women of reproductive age, often leading to significant health complications if not diagnosed and managed effectively. Traditional diagnostic methods are time-consuming and resource-dependent, highlighting the need for innovative solutions. The integration of artificial intelligence in PCOS diagnosis could streamline the assessment process and improve patient outcomes.

Data Highlights

The AutoPCOS framework utilized a Kaggle dataset to develop four predictive models, achieving precision values of ≥ 0.929 in specific subgroups.

Key Findings

• The AutoPCOS framework categorizes data into clinical, laboratory, and ultrasound modalities for flexible risk assessment.
• Random Forest was the most effective model, outperforming Logistic Regression, Support Vector Machine, Decision Tree, and Gradient Boosting.
• Models showed robust performance in subgroups with BMI < 24 and irregular menstrual cycles.
• The framework provides interpretable risk explanations and personalized recommendations through integration with a knowledge base and Lingshu large language model.
• Future work aims to validate the framework using real-world clinical datasets to enhance generalizability.

Clinical Implications

The AutoPCOS framework presents a practical tool for clinicians to assess PCOS risk more efficiently, particularly in resource-limited settings. Its ability to adapt to varying data availability can improve diagnostic accuracy and patient management strategies.

Conclusion

AutoPCOS represents a significant advancement in the diagnostic approach to PCOS, potentially transforming how healthcare providers assess and manage this complex condition.

References

1. American Society for Reproductive Medicine, ASRM, 2023 -- Recommendations from the 2023 International Evidence-based Guideline for the Assessment and Management of Polycystic Ovary Syndrome
2. The Journal of Clinical Endocrinology & Metabolism, 2023 -- Evaluating Patients: Navigating Diagnostic Obstacles in Polycystic Ovary Syndrome Assessment
3. The Journal of Clinical Endocrinology & Metabolism, 2023 -- The Quest for Identifying Polycystic Ovary Syndrome
4. European Journal of Preventive Cardiology, 2023 -- Nonobese young females with polycystic ovary syndrome are at high risk for long-term cardiovascular disease
5. The Journal of Clinical Endocrinology & Metabolism — Comparing Population Data with Health System and Insurance Records: Notable Underdiagnosis of PCOS
6. Recommendations from the 2023 International Evidence-based Guideline for the Assessment and Management of Polycystic Ovary Syndrome (2023) - practice guidance | American Society for Reproductive Medicine | ASRM