Automated Assessment of Choroidal Mass Dimensions Using Static and Dynamic Ultrasonographic Imaging.

ABSTRACT:

Purpose

The purpose of this study was to develop and validate an artificial intelligence (AI) model for detecting and measuring choroidal mass dimensions on B-scan ophthalmic ultrasound images.

Methods

The study included 1822 still images and 130 cine loops of choroidal masses. For external validation, 180 additional still images were included, along with 374 control images to assess specificity. A two-stage U-Net-based architecture was trained to detect masses and measure dimensions. For cine loops, the algorithm automatically selected the frame with the largest mass cross-sectional area.

Results

In the internal subset, detection accuracy was 94.5% with a false-positive rate of 11.7%. For apical height, the mean absolute error (MAE) was 0.42 ± 0.58 mm (R² = 0.87), wi

SUBMITTER: Emmert N 

PROVIDER: S-EPMC12758429 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature