Often deemed \u201celusive\u201d due to under-documentation and lack of clear diagnostic markers, chronic pelvic pain (CPP) related conditions such as endometriosis, uterine fibroids, and menstrual disorders inflict significant patient burden and contribute to rising healthcare costs. Our work in the Ensari Lab confronts these prevalent yet poorly-understood women\u2019s health challenges through research-based mobile health tools and AI methodologies. We strive to illuminate these “hidden” diseases, accelerate diagnosis, and personalize care, directly addressing health disparities that have too often left vulnerable populations behind.<\/p>\n
Our interdisciplinary team pursues three strategic pillars to fulfill this mission:<\/p>\n
Validating Mobile Health Technologies:<\/strong> Testing whether data from apps, wearables, and AI-based health technologies are reliable, valid, and clinically meaningful for reproductive and pelvic pain conditions.<\/p>\n<\/li>\n AI-Driven Personalized Lifestyle Interventions:<\/strong> Studying exercise and other lifestyle-based strategies to improve daily functioning, quality of life<\/a>, and long-term health for people living with chronic pain conditions.<\/p>\n<\/li>\n Clinical Actionability of Digital Data:<\/strong> Developing ways for patients and clinicians to summarize and interpret symptom, behavior, and physiological data in ways that support better conversations, better decisions, and more agency.<\/p>\n<\/li>\n<\/ul>\n Currently active research studies focus on:
\n1) AI-driven personalized lifestyle interventions for symptom management in CPP Disorders, with a focus on exercise<\/a>
\n2) Investigation of large language models for predictive modeling of infrequently-documented diagnoses and conditions for improving diagnostic performance under real-life clinical settings.
\n3) Integrating genomic markers and genetic data into mHealth and clinical data for improving model performance.<\/p>\n