OBJECTIVE: The aim of this study was to develop a hybrid diagnostic framework integrating radiomic and deep features for accurate and reproducible detection and classification of wrist fractures using X-ray images.
Manual interpretation of CT images for bone metastasis (BM) detection in primary cancer remains challenging. We present an automated Bone Lesion Detection System (BLDS) developed using CT scans from 2518 patients (9177 BMs; 12,824 non-BM lesions) acr...
Journal of computer assisted tomography
May 13, 2025
OBJECTIVE: The aim of the study was to evaluate the image quality of coronary computed tomography (CT) angiography (CCTA) in obese patients by using deep learning image reconstruction (DLIR) in comparison with adaptive statistical iterative reconstru...
Journal of computer assisted tomography
May 13, 2025
OBJECTIVE: This study aimed to compare the performance of deep learning image reconstruction (DLIR) with that of standard filtered back projection (FBP) and adaptive statistical iterative reconstruction V (ASiR-V) for measurement of the vascular diam...
Journal of computer assisted tomography
May 13, 2025
Artificial intelligence (AI) is the most revolutionizing development in the health care industry in the current decade, with diagnostic imaging having the greatest share in such development. Machine learning and deep learning (DL) are subclasses of A...
Journal of computer assisted tomography
May 13, 2025
OBJECTIVE: To quantitatively evaluate computed tomography (CT) parameters of coronavirus disease 2019 (COVID-19) pneumonia an artificial intelligence (AI)-based software in different clinical severity groups during the disease course.
Journal of computer assisted tomography
May 13, 2025
OBJECTIVE: To investigate the performance of Dual-AI Deep Learning Platform in detecting unreported pulmonary nodules that are 6 mm or greater, comprising computer-vision (CV) algorithm to detect pulmonary nodules, with positive results filtered by n...
Interdisciplinary cardiovascular and thoracic surgery
May 6, 2025
OBJECTIVES: This study aimed to develop an automated method for pulmonary artery and vein segmentation in both left and right lungs from computed tomography (CT) images using artificial intelligence (AI). The segmentations were evaluated using PulmoS...
BACKGROUND: The growing demand for follow-up imaging highlights the need for tools supporting the assessment of pulmonary nodules over time. We evaluated the performance of an artificial intelligence (AI)-based system for automated nodule matching.
Background Chest pain is a leading cause of outpatient and emergency department visits; advancements in artificial intelligence (AI) could improve coronary CT angiography (CCTA) workflows for these patients. Purpose To evaluate the performance of an ...
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