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Revolution in endometriosis diagnosis: AI brings new hope!
An R&D team at FAU Erlangen-Nuremberg is using AI to investigate new approaches to diagnosing and treating endometriosis.
Revolution in endometriosis diagnosis: AI brings new hope!
Endometriosis is a largely unknown but widespread disease that is estimated to affect every tenth to fifteenth woman of childbearing age in Germany. Despite the extensive symptoms, such as severe pelvic pain during menstruation, infertility and functional disorders during bowel movements and urination, the diagnosis is often not made. It usually takes an average of eight years for the disease to be recognized. Many patients only find out about their disease during an endoscopic procedure. This enormous number of unreported cases and the varying symptoms represent a major challenge for the medical care landscape. A new initiative from the Friedrich-Alexander University Erlangen-Nuremberg (FAU) aims to improve diagnostic options through the use of artificial intelligence (AI).
The research project called EndoKI receives funding of three million euros from the Bavarian State Ministry for Health, Care and Prevention. The aim is to improve non-invasive diagnostics and effective treatment of endometriosis patients. It will be carried out over a period of three years and combines various imaging techniques such as ultrasound and MRI with AI methods to develop a detailed 3D model for patients. A multidisciplinary team of five scientists from FAU and Erlangen University Hospital, supported by partners from the University of Würzburg and the Technical University of Munich, is driving this project forward.
Challenges in endometriosis diagnosis
The symptoms of endometriosis are often ignored or misinterpreted, leading to significant delays in diagnosis, which can range from four to eleven years. To change this, researchers in the EU-funded FEMaLe project have developed further innovative approaches. This uses AI to analyze patient data such as clinical records, symptoms and genetic information. This technology makes it possible to detect subtle patterns and early indicators of endometriosis at an early stage.
As part of the FEMaLe project, two key systems were developed: a clinical decision support system for healthcare professionals and a digital companion app for patients. This app enables users to track their symptoms and treatments over time. Anonymized data from the app flows into the AI engine, which helps improve diagnostic suggestions. The goal is to speed up diagnostics and thus reduce the number of necessary operations.
Integration of AI into treatment
In addition to improving diagnosis, integrating AI could also promote innovative approaches to endometriosis therapy. The development of the Endo app is in this context. It is based on guidelines from an independent EU expert group that takes into account principles such as legality, ethics and robustness. This is to ensure that the use of this technology complies with both legal requirements and ethical principles.
The possible applications for AI are promising. By analyzing users' symptom diaries, the AI can recognize patterns and uncover connections between symptoms and factors such as diet or exercise. It could also help avoid unnecessary invasive procedures and enable faster diagnosis. The AI will continually learn and improve based on user feedback and a growing database of symptoms and clinical results.
In the long term, a pseudonymized database with MRI data sets and histopathological information will be created. A qualitative ethnographic sub-study will also be carried out to capture the perspectives of gynecologists, patients and researchers. Recommendations for diagnostics and therapy should be developed, including for organizations such as the WHO. A conference on the topic of endometriosis is planned for 2028 at FAU and the University Hospital in order to pass on the knowledge gained and promote exchange among experts.
Overall, it shows that the combination of AI and interdisciplinary research opens up promising ways to sustainably improve the care of endometriosis patients. [FAU] reports on the significant steps towards more effective diagnostics and therapy. But projects like [CORDIS] and [Endometriose.App] also illustrate the need to increase the quality of life of affected women and to understand, measure and treat their pain.