OBJECTIVE: Clinical staging on CT has several biases, and a radiogenomics approach could be proposed. The study aimed to test the performance of a radiogenomics approach in identifying high-risk colon cancer.
OBJECTIVE: The aim of this study was to automatically detect teeth with dens invaginatus (DI) in panoramic radiographs using deep learning algorithms and to compare the success of the algorithms.
PURPOSE: To develop and evaluate radiomics-based models using contrast-enhanced T1-weighted imaging (CE-T1WI) for the non-invasive differentiation of primary central nervous system lymphoma (PCNSL) and solitary brain metastasis (SBM), aiming to impro...
RATIONALE AND OBJECTIVES: To evaluate the impact of AI-generated apparent diffusion coefficient (ADC) maps on diagnostic performance of a 3D U-Net AI model for prostate cancer (PCa) detection and segmentation at biparametric MRI (bpMRI).
PURPOSE: To develop a deep learning model using orbital computed tomography (CT) imaging to accurately distinguish thyroid eye disease (TED) and orbital myositis, two conditions with overlapping clinical presentations.
OBJECTIVE: To investigate the effect of contrast enhancement on the diagnosis of interstitial lung abnormalities (ILA) in automatic quantitative CT measurement in patients with paired pre- and post-contrast scans.
OBJECTIVES: This randomized controlled trial aimed to evaluate the impact of artificial intelligence (AI) assistance on dentists' diagnostic accuracy, confidence, and treatment decisions when detecting periapical radiolucencies (PRs) on panoramic rad...
RATIONALE AND OBJECTIVES: This prospective study evaluated the performance of AI in a diagnostic clinic setting, comparing its effectiveness with radiologists of varying experience.
BACKGROUND: Segmentation of airways and soft tissues on panoramic radiographs is a challenging yet crucial task in dental diagnostics, as these regions can often be confused with fractures or other lesions due to superimposition. This study aimed to ...
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