Artificial CT images can enhance variation of case images in diagnostic radiology skills training.

ABSTRACT:

Objectives

We sought to investigate if artificial medical images can blend with original ones and whether they adhere to the variable anatomical constraints provided.

Methods

Artificial images were generated with a generative model trained on publicly available standard and low-dose chest CT images (805 scans; 39,803 2D images), of which 17% contained evidence of pathological formations (lung nodules). The test set (90 scans; 5121 2D images) was used to assess if artificial images (512 × 512 primary and control image sets) blended in with original images, using both quantitative metrics and expert opinion. We further assessed if pathology characteristics in the artificial images can be manipulated.

Results

Primary and control artificial images attained an average obje

SUBMITTER: Hofmeijer EIS 

PROVIDER: S-EPMC10630276 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature