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Updated: Jun 4, 2025

Author Spotlight: Investigating the Mechanisms and Inducing Models of Polycystic Ovary Syndrome
Published on: July 5, 2024
Comparative Efficacy of Ayurveda Treatment Regimen and Letrozole along with Yoga Module in the Management of
Anubha Chandla1, Nalneesh Sharma2, Kavita Vyas1
1Regional Ayurveda Research Institute, Mandi, India.
Introduction:
Infertility is a significant health problem with psycho-social burden for couples. Ayurveda considers a holistic approach, including complex multimodal regimens in managing infertility. Increasing evidence has shown that Yoga as an adjuvant to infertility treatment increases pregnancy rates. Therefore, this research will investigate the comparative efficacy of Ayurveda regimen and letrozole, along with integration of Yoga in both groups in female infertility.
Methods:
The primary outcome, ovulation rate, will be measured through serial transvaginal sonography (TVS) for six consecutive menstrual cycles. The secondary outcome, conception rate, will be measured by definitive signs of pregnancy. Participants' quality of life will be assessed using the Ferti Quality of Life (FertiQOL) tool. The study will randomize 160 women aged 21-40 years diagnosed with anovulatory or unexplained infertility into two groups using permuted block randomization. Participants in group I will receive internal oleation with Phala Ghrita and mild purgation with Gandharvahasta (GH) oil for three consecutive cycles. Standard care group II participants will receive letrozole tablet once daily from day 3 to day 7 of the menstrual cycle for three cycles. The yoga instructor will take yoga sessions for participants of both groups for 6 months. This study will also use machine learning (ML) models to compare two regimens to predict improvements in outcome variables. The goal was to develop an online platform that aids personalized treatment planning and enhances clinical decision-making.
Discussion:
This will be the first study to generate evidence on the efficacy of integrated treatment regimens in female infertility, where predictive ML models will be developed.
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