Researchers have developed a potential machine learning model that could personalize treatments for female pelvic floor disorders, specifically urinary incontinence. Based on an analysis of over 600 patients, this model utilizes data such as demographics, initial symptoms, and information gathered from the Leva® Pelvic Health System within the first 14 days of use.
The researchers employed various machine learning techniques and found the Random Forest model to be the most effective, accurately predicting treatment outcomes 78% of the time. This model’s accuracy was further validated using an independent dataset. This breakthrough allows clinicians to identify patients who might benefit from additional support or treatment adjustments, ultimately aiming to improve treatment success rates.
The development of this model was a collaborative effort between researchers from Massachusetts General Hospital, Massachusetts Institute of Technology, and Axena Health, a medical device company specializing in female pelvic health. The team believes this discovery will significantly impact how clinicians approach treatment for urinary incontinence, enabling them to create personalized plans based on data-driven predictions.
The Leva Pelvic Health System, used in this research, is a non-invasive, medication-free treatment for urinary incontinence and chronic fecal incontinence. It combines a vaginal motion sensor with software to provide real-time feedback and track progress, empowering women to strengthen their pelvic floor muscles at home. The system is available by prescription, ensuring physician oversight and guidance throughout treatment.
Jon Napitupulu is Director of Media Relations at The Clinical Trial Vanguard. Jon, a computer data scientist, focuses on the latest clinical trial industry news and trends.
