PURPOSE: To evaluate the diagnostic accuracy of machine learning (ML) in detecting vertebral fractures, considering varying fracture classifications, patient populations, and imaging approaches.
PURPOSE: The differences between the Alberta Stroke Program Early CT Score (ASPECTS) obtained by experts and artificial intelligence (AI) software require elucidation. We aimed to characterize the discrepancies between the ASPECTS obtained by AI and ...
BACKGROUND: Brain metastases (BMs) represents a severe neurological complication stemming from cancers originating from various sources. It is a highly challenging clinical task to accurately distinguish the pathological subtypes of brain metastatic ...
OBJECTIVE: To develop and externally validate a binary classification model for lumbar vertebral body fractures based on CT images using deep learning methods.
PURPOSE: To evaluate the impact of deep learning-based reconstruction (DLRecon) on bone assessment in zero echo-time (ZTE) MRI of the knee at 1.5 Tesla.
PURPOSE: To investigate the feasibility of deep learning (DL) based on conventional MRI to differentiate tuberculous spondylitis (TS) from brucellar spondylitis (BS).