Earlier PCOS insight. Better fertility care.
FertiCare AI applies machine learning to routine clinical, hormonal and ultrasound data to flag likely polycystic ovary syndrome early, so women get answers and fertility support sooner.
The problem
A common hormonal disorder that too often goes undiagnosed.
Polycystic ovary syndrome affects women of reproductive age and is a leading cause of infertility and menstrual irregularity. Many women live with it for years before a diagnosis, losing valuable time for treatment and family planning.
of women worldwide are affected by PCOS.
women face infertility or menstrual irregularity linked to PCOS, as reported in our research.
accuracy reached by the hybrid machine-learning model highlighted in the paper.
Sources: figures as reported in the founding research paper (2025).
How it works
From routine data to an early, explainable signal.
FertiCare AI works with information clinics already collect. No new test is required to get started.
Collect
Clinical history, cycle data, hormone results and ultrasound findings are entered or imported.
Model
Machine-learning models trained on PCOS cases score the likelihood of PCOS.
Explain
The clinician sees which indicators drove the score, not just a number.
Support
Women flagged early are guided to diagnosis, lifestyle support and fertility care.
Product
Designed with clinicians, for women.
FertiCare AI is a decision-support tool. Diagnosis and treatment remain with qualified doctors.
Hybrid ML engine
Combines several algorithms, in line with the hybrid approach that performed best in our research.
Proven algorithm family
Built on methods reviewed in the paper: CNN, SVM, KNN, Random Forest, Logistic Regression, Decision Tree and Naive Bayes.
Clear explanations
Highlights the indicators behind each result so clinicians can discuss them with the patient.
Privacy by design
Patient data is handled with confidentiality as a core requirement from day one.
Who it's for
Built for the people on the front line of women's health.
Gynaecology clinics
Screen patients consistently and prioritise those needing a full work-up.
Fertility centres
Identify PCOS earlier in the fertility journey to plan treatment sooner.
Primary care & women's health programmes
Bring structured PCOS screening to general practice.
The research
Built on a review of machine learning for PCOS detection.
- Undiagnosed PCOS can lead to infertility, so accurate and timely screening matters.
- Surveys machine-learning methods used to detect PCOS, including CNN, SVM, KNN, Random Forest, Logistic Regression, Decision Tree and Naive Bayes.
- A hybrid model achieved 90.01% accuracy for early PCOS detection.
- Supports ML as a practical aid for fertility support in women of reproductive age (18–44).
Xavier Suresh, Ganeshan, Babar, Shaqadan, & Sabarna (2025). Fertility Support of PCOS with Machine Learning. In Business Resilience and Business Innovation for Sustainability. Springer.
Team
Medical insight meets data science.
Founded in 2025 by a student team from two faculties.
Faculty of Allied Medical Sciences
Clinical indicators, women's health workflow and validation.
Faculty of Engineering & Information Technology
Machine-learning models, data pipeline and the software platform.
FAQ
Questions we hear often.
Does FertiCare AI diagnose PCOS?
No. It provides a likelihood score to support the clinician. Diagnosis follows established clinical criteria.
What data does it need?
Data clinics already gather: cycle history, hormone results, ultrasound findings and basic metabolic measures.
Why machine learning?
PCOS presents differently from woman to woman. ML can weigh many indicators together and spot patterns earlier.
Can our clinic join a pilot?
Yes. We are looking for clinics and fertility centres to validate FertiCare AI in practice.
Help women get answers sooner.
We are partnering with gynaecology clinics and fertility centres for pilot studies.