ReviewTissue engineering and regenerative medicine2026
Harnessing Artificial Intelligence for Regeneration of Endometrium in Asherman's Syndrome.
Review in Tissue engineering and regenerative medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
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Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAsherman'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.
objectiveThis review critically evaluates the different therapeutic strategies used to manage or treat Asherman's syndrome and the various issues associated with each treatment.
methodsA 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.
resultsFinally, 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.
conclusionIn 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.