Marzieh Neykhonji, Abdulridha Mohammed Al-Asady, Amir Avan, Majid Khazaei and Seyed Mahdi Hassanian* Pages 1 - 11 ( 11 )
Introduction: Endometriosis, a prevalent women's health condition, is associated with persistent pelvic pain and infertility. Despite ongoing research, its precise disease mechanism remains elusive, impeding the discovery of a definitive cure. However, the progression of this disease is driven by three central factors, namely estrogen, progesterone, and inflammatory processes. The current work summarizes an evaluation of hormonal drug therapy in endometriosis, highlighting pathogenesis, clinical studies, and the anticipated role of AI in improving diagnostic accuracy and therapeutic results.
Methods: Current information related to endometriosis and the application of AI in its diagnosis and treatment were evaluated through an in-depth literature search in the PubMed database and Google Scholar search engine.
Results: The current treatment modalities for this disease encompass drug therapy and surgery. In line with key contributing factors, the first-line pharmaceutical treatment revolves around progestin therapy, which involves administration either alone or in combination with a small amount of estrogen. Each medication is linked to certain drawbacks, encompassing bone loss associated with progesterone-only therapy, considerable cost implications, and heightened risks of bleeding, spotting, and drug intolerance when utilizing combined progesterone-estrogen therapy.
Conclusion: Many clinical studies on endometriosis are currently investigating the overall impact of the therapeutic approach involving progesterone-estrogen therapy with respect to the treatment of pelvic pain, health-related quality of life, cost-effectiveness, and tolerability. The rise of artificial intelligence and its advanced data processing capabilities present a promising opportunity to revolutionize endometriosis diagnosis and treatment by offering novel approaches.
Endometriosis, infertility, pelvic pain, progesterone, estrogen, inflammation, artificial intelligence.