Early prediction of endometriosis with Artificial Intelligence

What is endometriosis and why is it difficult to diagnose?

La endometriosis It is a chronic gynecological disease that affects approximately 10% of women of reproductive age worldwide. It is characterized by the abnormal growth of endometrial tissue outside the uteruswhich causes symptoms such as severe pelvic pain, Chronic inflamation y fertility problems.

One of the main medical challenges is its late diagnosisIn many cases, the symptoms are confused with other gynecological conditions such as polycystic ovary syndrome (PCOS) and primary dysmenorrhea, which delays detection between 7 and 10 yearsThis delay prevents access to timely treatments and compromises the reproductive health of the patients.

Innovation in diagnostics with artificial intelligence

To address this problem, the Institute for the Study of the Biology of Human Reproduction (INEBIR), in collaboration with the Software Systems Engineering and Science Group of the University of Seville, has developed a artificial intelligence (AI) model for early detection of endometriosis.

This project, led by doctors José Navarro-Pando y Ana Teresa MarcosIt was published in January 2025 in the prestigious scientific journal Expert Systems with Applications. Check the article A novel machine learning-based proposal for early prediction of endometriosis disease here.

The challenges in diagnosing endometriosis

The diagnosis of endometriosis It presents several difficulties:

1. Nonspecific and overlapping symptoms

It is often confused with other gynecological conditions, making early identification difficult.

2. Limited detection methods

Transvaginal ultrasound is a common tool, but laparoscopy remains the most reliable method, although it is expensive, invasive, and inaccessible.

3. Absence of specific biomarkers

One has not yet been identified reliable biological marker that allows the non-invasive diagnosis.

Thanks to the use of artificial intelligence and machine learning algorithm It is possible to analyze large volumes of clinical and genetic data, detect predictive patterns and facilitate a early and accurate diagnosis.

How does the AI-based prediction model work?

The model developed by INEBIR and Sevilla University uses supervised learning algorithms to detect Key indicators of endometriosis.

This system was trained with a database of 5.143 diagnosed women, ensuring a high level of precision and customization.

Phases of the predictive process

1. Collection and analysis of clinical and genetic data

• Detailed medical history of the patient.
• Associated hormonal and reproductive factors.
• Detecting genetic variants in exome genetic studies.

2. Processing by machine learning algorithm

• Application of supervised algorithms to detect hidden correlations between clinical and genetic data.
• Evaluation of clinical risk factors for endometriosis.
• Detecting genetic variants that help predict predisposition to the disease.
• Generating highly accurate predictions.

3. Risk categorization and support for specialists

• Classification of patients according to their probability of developing the disease.
• Provision of real-time information to facilitate early intervention.

This approach reduces diagnosis time, personalize the treatments, minimizes the need for invasive procedures y Improves quality of life of the patients.

Impact of early diagnosis on female fertility

One of the most worrying effects of the endometriosis is its impact on the fertilityMany women discover the disease when they try unsuccessfully to conceive.

Benefits of early diagnosis

• Symptom relief before the disease progresses.
• Fertility preservation, improving the success of treatments Assisted reproduction.
• Optimization of medical strategies, avoiding unnecessary surgeries and reducing costs in the long term.

El INEBIR AI model It is an essential tool for gynecologists and fertility specialistswhich can significantly increase success rates in patients seeking to conceive.

Artificial intelligence in reproductive medicine

The use of Artificial Intelligence is revolutionizing the gynecology and reproductive medicineSome of its most innovative applications include:

1. Predicting success in fertility treatments

Optimization of protocols ovarian stimulation and improvement in the selection of embryos.

2. Embryonic quality analysis in fertilization vitro (IVF)

AI-based embryo classification to increase implantation rates.

3. Personalized hormone therapies

Dose adjustment in treatments of fertility according to the patient's genetic profile.

These advances guarantee a more accurate and effective medical care, reducing the uncertainty and improving the clinical results.

AI and endometriosis: a paradigm shift in medicine

The development of predictive models with Artificial Intelligence represents a milestone in the early detection of gynecological diseases.

Thanks to machine learning algorithm , It is possible to analyze large volumes of clinical and genetic datawhich allows for improvements diagnostic methods.

INEBIR has made significant progress in integrating AI tools in medical practice, offering a accurate and accessible prediction system for thousands of women around the world.

See the scientific article: Download the study at Expert Systems with Applicationns. 

Do you have symptoms of endometriosis? Consult with specialists

if you experience painful periods, chronic pelvic pain, irregular menstrual bleeding o fertility problemsYou could be facing a case of undiagnosed endometriosis.

En INEBIR, we have a team of experts in reproductive health Ready to help you. Agenda a consultation with our specialists.