Deep generative model of the distal tibial classic metaphyseal lesion in infants: assessment of synthetic images.

ABSTRACT:

Background

The classic metaphyseal lesion (CML) is a distinctive fracture highly specific to infant abuse. To increase the size and diversity of the training CML database for automated deep-learning detection of this fracture, we developed a mask conditional diffusion model (MaC-DM) to generate synthetic images with and without CMLs.

Purpose

To objectively and subjectively assess the synthetic radiographic images with and without CMLs generated by MaC-DM.

Materials and methods

For retrospective testing, we randomly chose 100 real images (50 normals and 50 with CMLs; 39 infants, male = 22, female = 17; mean age = 4.1 months; SD = 3.1 months) from an existing distal tibia dataset (177 normal, 73 with CMLs), and generated 100 synthetic distal tibia images via MaC-DM (50

SUBMITTER: Wu S 

PROVIDER: S-EPMC11335364 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature