BACKGROUND:: Tuberculosis disease is the disease that causes significant morbidity and mortality worldwide. Thus, early detection of the disease is crucial for proper treatment and controlling the spread of Tuberculosis disease. Chest X-ray imaging i...
AIM: Spine fractures are a frequent and relevant diagnosis, but systematic documentation is time-consuming and sometimes overlooked. A deep learning pipeline for opportunistic fracture detection in computed tomography (CT) spine images of varying fie...
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
39970821
The morphological characteristics of pulmonary nodule, also known as the attributes, are crucial for classification of benign and malignant nodules. In clinical, radiologists usually conduct a comprehensive analysis of correlations between different ...
RATIONALE AND OBJECTIVES: To evaluate the value of artificial intelligence (AI) assisted diagnostic system in reconstructing axial lumbar disc CT images and diagnosing lumbar disc herniation.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
39947085
Recently, deep learning models have demonstrated impressive performance in Automatic Joint Lesion Detection (AJLD), yet balancing accuracy and efficiency remains a significant challenge. This paper focuses on achieving end-to-end lesion detection whi...
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
39947083
Coronary artery disease (CAD) continues to be a leading global cause of cardiovascular related mortality. The scoring of coronary artery calcium (CAC) using computer tomography (CT) images is a diagnostic instrument for evaluating the risk of asympto...
PURPOSE: Accurate differentiation of benign renal lesions from renal cell carcinoma (RCC) is crucial for optimized management, particularly for small renal lesions (≤4 cm in diameter). This study aimed to integrate clinical data, radiomic features, a...
Radiologists are prone to missing some colorectal cancers (CRCs) on routine abdominopelvic CT examinations that are in fact detectable on the images. The purpose of this study was to develop an artificial intelligence (AI) model to detect CRC on ro...
International journal of computer assisted radiology and surgery
39932621
PURPOSE: Deep-learning-based supervised CT segmentation relies on fully and densely labeled data, the labeling process of which is time-consuming. In this study, our proposed method aims to improve segmentation performance on CT volumes with limited ...
Purpose To construct and evaluate the performance of a machine learning model for bone segmentation using whole-body CT images. Materials and Methods In this retrospective study, whole-body CT scans (from June 2010 to January 2018) from 90 patients (...