Hybrid artificial intelligence echogenic components-based diagnosis of adnexal masses on ultrasound.

ABSTRACT:

Background

Adnexal masses are heterogeneous and have varied sonographic presentations, making them difficult to diagnose correctly.

Purpose

Our study aimed to develop an innovative hybrid artificial intelligence/computer-aided diagnosis (AI/CADx)-based pipeline to distinguish between benign and malignant adnexal masses on ultrasound imaging based upon automatic segmentation and echogenic-based classification.

Methods

The retrospective study was conducted on a consecutive dataset of patients with an adnexal mass. There was one image per mass. Mass borders were segmented from the background via a supervised U-net algorithm. Masses were spatially subdivided automatically into their hypo- and hyper-echogenic components by a physics-driven unsupervised clustering algorithm

SUBMITTER: Yoeli-Bik R 

PROVIDER: S-EPMC12264317 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature