Recommended Citation
Babbs, Charles F., "One possible solution to the ENDO Challenge for earlier, low cost, non-invasive diagnosis of endometriosis" (2024). Weldon School of Biomedical Engineering Faculty Working Papers. Paper 36.
https://docs.lib.purdue.edu/bmewp/36
Date of this Version
8-25-2024
Keywords
AI, biomarkers, diagnosis, diagnostics, dysmenorrhea, endometriosis, Fallopian tubes, infertility, innovations, implant, laparoscopy, menstrual pain, menstruation, noninvasive, prototype, regurgitation, robotics, symptoms, underrepresented minorities.
Abstract
This short paper outlines a potential solution to the ENDO Challenge posed by the US National Institutes of Health to spur development of painless, noninvasive diagnostic tools for early diagnosis in women suffering needlessly from endometriosis. Endometriosis is a common condition caused by regurgitation of menses backwards through the Fallopian tubes from the uterine cavity into the abdominal cavity during menstruation. A few still viable endometrial cells then implant widely and unpredictably throughout the abdominal cavity and become established on the surfaces of internal organs and the abdominal wall. There they live and remain responsive to cyclic hormones. The resulting patches of endometrial tissue bleed into the abdominal cavity with successive menstrual periods, causing pain. Because the endometrial implants can be scattered widely throughout the peritoneal cavity and can also spread to new locations therein, endoscopic or even open surgical methods rarely discover and eliminate all of them. The more that endometriosis spreads, the more difficult it is to diagnose and treat. Sadly, diagnosis may be delayed many years, with continued spread, due to normalization of intense menstrual pain by patients and their healthcare providers. Simple, noninvasive, low-cost screening methods, applicable to large numbers of women worldwide, are needed. The present proposed solution is a smart phone application that prompts the patient to track and record symptoms day-by-day, and later to compare the detailed patient record both with data for known populations of healthy women versus data for women with recently diagnosed endometriosis. The foundational principle for success is asking the best questions, based upon understanding of the abnormal physiology and anatomy of the endometriosis and published studies. Day to day tracking should reveal more abundant, accurate, and precise data than one-time history taking in a provider’s office. Use of artificial intelligence and modern technology promises identification of key features in the data that allow more accurate classification. Such a tool can be used at little expense from the comfort of the patient’s home, and it may be able to spot suspicious trends much earlier through artificial intelligence.