Feasibility of generating sagittal radiographs from coronal views using GAN-based deep learning framework in adolescent idiopathic scoliosis

Feasibility of generating sagittal radiographs from coronal views using GAN-based deep learning framework in adolescent idiopathic scoliosis

Abstract Background Minimizing radiation exposure is crucial in monitoring adolescent idiopathic scoliosis (AIS). Generative adversarial networks (GANs) have emerged as valuable tools being able to generate high-quality synthetic images. This study explores the use of GANs to generate synthetic sagi...

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Bibliographic Details
Main Authors: Tito Bassani, Andrea Cina, Fabio Galbusera, Andrea Cazzato, Maria Elena Pellegrino, Domenico Albano, Luca Maria Sconfienza
Format: Article
Language:English
Published: SpringerOpen 2025-01-01
Series:European Radiology Experimental
Subjects:
Adolescent
Artificial intelligence
Deep learning
Lordosis
Scoliosis
Online Access:https://doi.org/10.1186/s41747-025-00553-6
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https://doi.org/10.1186/s41747-025-00553-6

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