AIMC Topic: X-Rays
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Improving Computer-Aided Thoracic Disease Diagnosis through Comparative Analysis Using Chest X-ray Images Taken at Different Times.
Sensors (Basel, Switzerland)
Feb 24, 2024
Medical professionals in thoracic medicine routinely analyze chest X-ray images, often comparing pairs of images taken at different times to detect lesions or anomalies in patients. This research aims to design a computer-aided diagnosis system that ...
A deep-learning-based framework for identifying and localizing multiple abnormalities and assessing cardiomegaly in chest X-ray.
Nature communications
Feb 14, 2024
Accurate identification and localization of multiple abnormalities are crucial steps in the interpretation of chest X-rays (CXRs); however, the lack of a large CXR dataset with bounding boxes severely constrains accurate localization research based o...
Validating the accuracy of deep learning for the diagnosis of pneumonia on chest x-ray against a robust multimodal reference diagnosis: a post hoc analysis of two prospective studies.
European radiology experimental
Feb 2, 2024
BACKGROUND: Artificial intelligence (AI) seems promising in diagnosing pneumonia on chest x-rays (CXR), but deep learning (DL) algorithms have primarily been compared with radiologists, whose diagnosis can be not completely accurate. Therefore, we ev...
AI-based X-ray fracture analysis of the distal radius: accuracy between representative classification, detection and segmentation deep learning models for clinical practice.
BMJ open
Jan 23, 2024
OBJECTIVES: To aid in selecting the optimal artificial intelligence (AI) solution for clinical application, we directly compared performances of selected representative custom-trained or commercial classification, detection and segmentation models fo...
Artificial Intelligence-enabled Chest X-ray Classifies Osteoporosis and Identifies Mortality Risk.
Journal of medical systems
Jan 13, 2024
A deep learning model was developed to identify osteoporosis from chest X-ray (CXR) features with high accuracy in internal and external validation. It has significant prognostic implications, identifying individuals at higher risk of all-cause morta...
High Throughput Tomography (HiTT) on EMBL beamline P14 on PETRA III.
Journal of synchrotron radiation
Jan 1, 2024
Here, high-throughput tomography (HiTT), a fast and versatile phase-contrast imaging platform for life-science samples on the EMBL beamline P14 at DESY in Hamburg, Germany, is presented. A high-photon-flux undulator beamline is used to perform tomogr...
Ensemble deep-learning networks for automated osteoarthritis grading in knee X-ray images.
Scientific reports
Dec 21, 2023
The Kellgren-Lawrence (KL) grading system is a scoring system for classifying the severity of knee osteoarthritis using X-ray images, and it is the standard X-ray-based grading system for diagnosing knee osteoarthritis. However, KL grading depends on...
Deep learning for report generation on chest X-ray images.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Dec 14, 2023
Medical imaging, specifically chest X-ray image analysis, is a crucial component of early disease detection and screening in healthcare. Deep learning techniques, such as convolutional neural networks (CNNs), have emerged as powerful tools for comput...
Diagnosis and detection of pneumonia using weak-label based on X-ray images: a multi-center study.
BMC medical imaging
Dec 12, 2023
PURPOSE: Development and assessment the deep learning weakly supervised algorithm for the classification and detection pneumonia via X-ray.
Development and validation of open-source deep neural networks for comprehensive chest x-ray reading: a retrospective, multicentre study.
The Lancet. Digital health
Dec 8, 2023
BACKGROUND: Artificial intelligence (AI) systems for automated chest x-ray interpretation hold promise for standardising reporting and reducing delays in health systems with shortages of trained radiologists. Yet, there are few freely accessible AI s...
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