DEVELOPMENT OF PERSONALIZED, NON-HORMONAL, AI- DRIVEN TREATMENT PATHWAYS FOR HEAVY MENSTRUAL BLEEDING BASED ON MENSTRUAL BLOOD BIOMARKERS AND UTERINE MICROBIOME PROFILING

Abstract

Heavy    menstrual  bleeding  (HMB)  is   a  prevalent  gynecological issue  that disproportionately affects women in  South Asia,  contributing to  high rates  of  anemia and diminished quality  of   life 1  2    .  Conventional therapies,  often  hormone-based  or   surgical, are  not   universally acceptable and fail  to  account for  individual etiological differences. Recent advances in  menstrual blood biomarker   discovery  and   uterine   microbiome  profiling  offer  opportunities   for   precision  medicine  approaches to HMB. This study presents  a comprehensive overview of HMB in the South Asian  context and proposes a novel framework for  personalized,  non-hormonal,  AI-driven treatment  pathways. We review real-time clinical trial  data and current evidence on  key menstrual blood biomarkers (e.g.,  prostaglandin  E2, inflammatory cytokines, fibrinolytic factors) and uterine microbiome patterns (e.g.,  Lactobacillus deficiency, enriched anaerobic taxa)  associated with  HMB 3  4    . An experimental methodology is outlined whereby patient-specific  biomarker  and   microbiome  profiles  inform  an    artificial  intelligence   (AI)  model  to recommend  tailored  interventions  –  for   example, antifibrinolytics for   hyperfibrinolytic  phenotypes or targeted  antibiotics for  microbiome dysbiosis –thereby avoiding blanket hormonal therapy. A proof-of- concept  framework  (Figure 1)  illustrates integration of  clinical  data with  machine learning to  optimize treatment selection in real  time. The clinical  relevance of this  personalized approach is emphasized, aiming to  improve outcomes and patient satisfaction in  low-resource settings. This  manuscript underscores the novelty of combining “omics” data with  AI in HMB management, and discusses the potential benefits and challenges of implementing such personalized, non-hormonal treatment pathways in South Asia.

Keywords: Heavy  menstrual bleeding; Personalized medicine; Menstrual blood biomarkers; Uterine microbiome; Non-hormonal treatment; Artificial intelligence; South Asia