This study investigated the effects of feature augmentation, which uses generated images with specific imaging features, on the performance of isocitrate dehydrogenase (IDH) mutation prediction models in gliomas. A total of 598 patients were included from our institution (310 training, 152 internal test) and the Cancer Genome Atlas (136 external test). Score-based diffusion models were used to generate T2-weighted, FLAIR, and contrast-enhanced T1-weighted image triplets.
View Article and Find Full Text PDFBackground: The glymphatic system, essential for brain waste clearance, has been implicated in neurodegenerative diseases, including amyotrophic lateral sclerosis (ALS). Emerging imaging markers, such as the analysis along the perivascular space (ALPS) index and choroid plexus volume (CPV), may provide insights into glymphatic function, but their relevance to ALS remains unclear.
Objective: To assess glymphatic dysfunction in ALS patients using the ALPS index and CPV.
Korean J Radiol
November 2024
Generative artificial intelligence (AI) has been applied to images for image quality enhancement, domain transfer, and augmentation of training data for AI modeling in various medical fields. Image-generative AI can produce large amounts of unannotated imaging data, which facilitates multiple downstream deep-learning tasks. However, their evaluation methods and clinical utility have not been thoroughly reviewed.
View Article and Find Full Text PDFObjective: We investigated the prevalence of pneumonia in novel coronavirus disease 2019 (COVID-19) patients using chest radiographs to identify the characteristics of those with initially negative chest radiographs, who were positive for pneumonia on follow-up.
Materials And Methods: Retrospective cohort data of 236 COVID-19 patients were reviewed. Chest radiography was performed on admission, with serial radiographs obtained until discharge.