Prediction of uterine cavity conception environment using two-dimensional transvaginal ultrasound imaging semantic feature-based machine learning: a case-control study.

ABSTRACT:

Background

Independently investigating the association between pregnancy outcomes and the uterine cavity conception environment (UCCE) is challenging. Therefore, this study aimed to employ a range of machine learning algorithms to systematically analyze the semantic features derived from two-dimensional transvaginal ultrasound (TVUS) images that characterize the UCCE.

Methods

This case-control study was conducted at the Women and Children's Hospital of Chongqing Medical University. Preoperative TVUS findings in patients diagnosed with infertility who subsequently achieved clinical pregnancy within 1 year following hysteroscopic intervention alone were classified as abnormal UCCE (case group), whereas TVUS findings in the uterine cavity of women who successfully achieved clin

SUBMITTER: Zhang J 

PROVIDER: S-EPMC12619196 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature