Screening of adolescent idiopathic scoliosis using generative adversarial network (GAN) inversion method in chest radiographs.

ABSTRACT:

Objective

Conventional computer-aided diagnosis using convolutional neural networks (CNN) has limitations in detecting sensitive changes and determining accurate decision boundaries in spectral and structural diseases such as scoliosis. We devised a new method to detect and diagnose adolescent idiopathic scoliosis in chest X-rays (CXRs) employing the latent space's discriminative ability in the generative adversarial network (GAN) and a simple multi-layer perceptron (MLP) to screen adolescent idiopathic scoliosis CXRs.

Materials and methods

Our model was trained and validated in a two-step manner. First, we trained a GAN using CXRs with various scoliosis severities and utilized the trained network as a feature extractor using the GAN inversion method. Second, we classified e

SUBMITTER: Lee JS 

PROVIDER: S-EPMC10202263 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature