Visualized hysteroscopic artificial intelligence fertility assessment system for endometrial injury: an image-deep-learning study.

ABSTRACT:

Objective

Asherman's syndrome (AS) is a significant cause of subfertility in women from developing countries. Over 80% of AS cases in these regions are linked to dilation and curettage (D&C) procedures following pregnancy. The incidence of AS in patients with infertility and recurrent miscarriage can be as high as 10%, while the pregnancy rate in cases of moderate to severe adhesions can be as low as 34%. We aimed to establish a hysteroscopic artificial intelligence system using image-deep-learning algorithms for fertility assessment.

Methods

This diagnostic study included 555 cases with 4922 hysteroscopic images from a Chinese intrauterine adhesions cohort clinical database (NCT05381376). The study evaluated two image-deep-learning algorithms' effectiveness in predicting pr

SUBMITTER: Li B 

PROVIDER: S-EPMC11921166 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature