Harnessing Artificial Intelligence for Regeneration of Endometrium in Asherman's Syndrome

Asherman's Syndrome or intrauterine adhesions develop due to acquired endometrium damage, resulting in partial to complete dysfunction of the endometrium within the uterine cavity. The pathophysiology of these adhesions is not clear. Still, the widely accepted mechanism for the development of these adhesions is attributed to three different causes: i. iatrogenic or mechanical, including curettage; ii. pathophysiological conditions, including infection, miscarriage, and Müllerian malformations; and iii. idiopathic.

This review critically evaluates the different therapeutic strategies used to manage or treat Asherman's syndrome and the various issues associated with each treatment.

A thorough literature review was performed for other types of polymers currently used or tested for the regeneration of endometrium both clinically and preclinically, and the issues associated with each of the polymers are also discussed.

Finally, we conclude the manuscript by exploring Artificial Intelligence's role in predicting, classifying, and identifying intrauterine adhesions, including machine learning and deep learning algorithms. We also discuss the role of AI in improving biomaterial properties, enhancing stem cell viability, and refining AI-driven diagnostic and therapeutic strategies for better clinical outcomes.

In alignment with the United Nations Sustainable Development Goal 4 (Quality Education), this review aims to promote advanced interdisciplinary learning by integrating biomedical engineering, materials science, and artificial intelligence to educate and empower future researchers in regenerative medicine.