A Comprehensive Review on Computational Screening and Digital Management for PMOS, PCOD, and Menopause

Abstract

This survey paper presents a systematic review of the global digital health literature focused on computational screening methodologies for Polyendocrine Metabolic Ovarian Syndrome (PMOS), Polycystic Ovarian Disease (PCOD), and transitional climacteric phases such as menopause. While contemporary mobile health informatics applications offer fundamental data-logging interfaces, they consistently exhibit severe architectural limitations regarding multi-stage integration, predictive diagnostic intelligence, and cross-demographic localization. This paper synthesizes current research across machine learning-driven risk classification models, longitudinal disease trajectories, and systemic human-computer interaction bottlenecks. The analysis reveals a stark structural fragmentation within the industry, where metabolic disorders during reproductive years and biological changes during aging phases are treated as entirely isolated clinical pathways. By mapping these systemic technical gaps, this survey defines a foundational taxonomy matrix to guide the development of next-generation, patient-centric preventive care models.

Introduction

This survey paper examines the growing need for advanced digital health platforms to support women with chronic reproductive and gynecological conditions such as Polycystic Ovarian Disease, Polycystic Ovary Syndrome, premature ovarian insufficiency, and early menopause. These conditions significantly affect hormonal, physical, and mental health, yet delayed diagnosis remains common due to social stigma, limited awareness, and inadequate healthcare infrastructure.

Current mobile health (mHealth) applications primarily function as simple tracking calendars and fail to provide continuous monitoring, predictive analysis, personalized recommendations, or long-term health record management. The paper therefore evaluates the key technological and clinical components needed for a comprehensive women's health platform.

Key Findings

1. Clinical Importance

• Early menopause and related reproductive disorders are associated with serious long-term risks, including osteoporosis, cardiovascular disease, cognitive decline, and metabolic disorders.
• Existing digital health tools generally provide static tracking features and do not adapt to an individual's changing hormonal profile.
• Personalized, long-term symptom monitoring combined with patient education is essential for early intervention and improved health outcomes.

2. Machine Learning in Women's Health

• Machine learning models have shown promise in automated disease screening.
• One reviewed system used TF-IDF feature extraction and a Multinomial Naive Bayes classifier, achieving approximately 87% classification accuracy for symptom-based screening.
• However, most existing models lack:
  • Long-term patient data storage
  • User authentication systems
  • Historical health tracking
  • Multilingual support
• These limitations reduce their effectiveness for lifelong reproductive health monitoring.

3. Longitudinal Symptom Tracking

Research identifies four major symptom categories associated with menopause and hormonal transitions:

1. Somatic symptoms (physical and musculoskeletal issues)
2. Vasomotor symptoms (hot flashes and night sweats)
3. Psychological symptoms (mood changes, depression, cognitive difficulties)
4. Sexual and reproductive symptoms

Large-scale studies involving over 450,000 individuals demonstrate that severe menopausal symptoms can serve as early indicators of future conditions such as:

• Cardiovascular disease
• Type 2 diabetes
• Osteoporosis
• Metabolic syndrome

The findings emphasize the need for software that continuously tracks symptoms across different life stages rather than treating reproductive health phases separately.

4. Usability Challenges

• Approximately 80% of health applications provide only manual logging features.
• Only around 10% incorporate predictive artificial intelligence.
• Most applications:
  • Lack support for early menopause and mental health monitoring.
  • Provide English-only interfaces.
  • Have poor usability scores, with some scoring as low as 51.8 on the System Usability Scale (SUS).
• Users frequently report issues related to poor personalization and low confidence in data accuracy.

Major Research Gaps Identified

The review highlights four significant shortcomings in existing women's health technologies:

1. Fragmented disease coverage – Most applications focus on a single condition and fail to address interconnected reproductive disorders.
2. Lack of life-course tracking – Systems do not adequately connect reproductive health data across different stages of life.
3. Limited predictive intelligence – Current tools rely heavily on manual symptom recording instead of AI-driven risk prediction.
4. Accessibility barriers – Low usability and English-only interfaces exclude many users, particularly in low- and middle-income regions.

Future Directions

The paper recommends developing next-generation women's health platforms that:

• Maintain persistent, long-term health records.
• Integrate machine learning for predictive risk assessment.
• Use natural language processing (NLP) to allow conversational symptom reporting.
• Implement strong encryption and privacy protections.
• Follow established usability principles to improve user experience.
• Support multiple regional languages, including Hindi and Tamil, to increase accessibility.

Conclusion

This survey paper has systematically mapped the structural, algorithmic, and interface design limitations across contemporary digital health architectures. The existing literature confirms that while statistical models can capture isolated reproductive conditions, practical software applications remain limited by fragmented features, low usability metrics, and an absence of proactive prediction capabilities. Moving forward, the development of next-generation women\'s health informatics requires a structural shift toward integrated, multi-language systems that link conversational symptom entry to persistent backend databases. Resolving these technical gaps is essential to replace reactive medical models with scalable, preventative digital healthcare.

References

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Copyright

Copyright © 2026 Dr. Ravindra S, Saima Arfain, Syeda Afreen, Trupthi D R, Sudha D. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.