As health data collection becomes more sophisticated, patients are leaving increasingly detailed digital footprints in routinely collected healthcare information, such as the electronic health record. Digital footprints create patterns that can be identified and used to find undiagnosed individuals, accelerating their time to diagnosis and proper treatment. Ultimately, the use of past health data to drive future diagnoses can help a greater number of patients receive:
PROPER CARE AND TREATMENT
REDUCING MEDICATION ERRORS
INCREASING PATIENT SAFETY
OPTIMIZING HEALTH EXPENDITURES
IMPROVING PATIENT QUALITY OF LIFE
In the case of endometriosis, little is known about the patterns that could define a potential diagnosis, making individual factors hard to recognize during the diagnostic journey. Artificial intelligence and machine learning can pick out intricate, subtle data patterns and make sense of these complex digital footprints through advanced, empirically driven analytical methods to create clarity and quantify the risk of endometriosis for an individual patient.
The FEMaLe project will bring the revolution with the application of Augmented Reality (AR) in dealing with endometriosis. One of its core features is to introduce AR in laparoscopic surgery in the hospital setting to improve diagnosis and the surgical removal of mild to moderate endometriosis. We will use artificial intelligence, big data and augmented reality to guide surgeons’ vision, thereby enhance surgical precision by making an organ’s inner anatomy visible, allowing for more effective, accurate and efficient surgical procedures. FEMaLE paves the way for minimal invasive surgery with a potential major impact on surgical healthcare systems worldwide and improved life quality for the patients.