AIMC Topic: Radiography, Thoracic
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AMFP-net: Adaptive multi-scale feature pyramid network for diagnosis of pneumoconiosis from chest X-ray images.
Artificial intelligence in medicine
Jun 18, 2024
Early detection of pneumoconiosis by routine health screening of workers in the mining industry is critical for preventing the progression of this incurable disease. Automated pneumoconiosis classification in chest X-ray images is challenging due to ...
Artificial intelligence-based radiographic extent analysis to predict tuberculosis treatment outcomes: a multicenter cohort study.
Scientific reports
Jun 7, 2024
Predicting outcomes in pulmonary tuberculosis is challenging despite effective treatments. This study aimed to identify factors influencing treatment success and culture conversion, focusing on artificial intelligence (AI)-based chest X-ray analysis ...
Robust Stochastic Neural Ensemble Learning With Noisy Labels for Thoracic Disease Classification.
IEEE transactions on medical imaging
Jun 3, 2024
Chest radiography is the most common radiology examination for thoracic disease diagnosis, such as pneumonia. A tremendous number of chest X-rays prompt data-driven deep learning models in constructing computer-aided diagnosis systems for thoracic di...
The utility of artificial intelligence in identifying radiological evidence of lung cancer and pulmonary tuberculosis in a high-burden tuberculosis setting.
South African medical journal = Suid-Afrikaanse tydskrif vir geneeskunde
May 31, 2024
BACKGROUND: Artificial intelligence (AI), using deep learning (DL) systems, can be utilised to detect radiological changes of various pulmonary diseases. Settings with a high burden of tuberculosis (TB) and people living with HIV can potentially bene...
Standalone deep learning versus experts for diagnosis lung cancer on chest computed tomography: a systematic review.
European radiology
May 22, 2024
PURPOSE: To compare the diagnostic performance of standalone deep learning (DL) algorithms and human experts in lung cancer detection on chest computed tomography (CT) scans.
COVID‑19 detection from chest X-ray images using transfer learning.
Scientific reports
May 21, 2024
COVID-19 is a kind of coronavirus that appeared in China in the Province of Wuhan in December 2019. The most significant influence of this virus is its very highly contagious characteristic which may lead to death. The standard diagnosis of COVID-19 ...
DLA-Net: dual lesion attention network for classification of pneumoconiosis using chest X-ray images.
Scientific reports
May 21, 2024
Accurate and early detection of pneumoconiosis using chest X-rays (CXR) is important for preventing the progression of this incurable disease. It is also a challenging task due to large variations in appearance, size and location of lesions in the lu...
Impact of intelligent virtual and AI-based automated collimation functionalities on the efficiency of radiographic acquisitions.
Radiography (London, England : 1995)
May 18, 2024
INTRODUCTION: Intelligent virtual and AI-based collimation functionalities have the potential to enable an efficient workflow for radiographers, but the specific impact on clinical routines is still unknown. This study analyzes primarily the influenc...
Performance of AI to exclude normal chest radiographs to reduce radiologists' workload.
European radiology
May 17, 2024
INTRODUCTION: This study investigates the performance of a commercially available artificial intelligence (AI) system to identify normal chest radiographs and its potential to reduce radiologist workload.
Enhancing multi-class lung disease classification in chest x-ray images: A hybrid manta-ray foraging volcano eruption algorithm boosted multilayer perceptron neural network approach.
Network (Bristol, England)
May 16, 2024
One of the most used diagnostic imaging techniques for identifying a variety of lung and bone-related conditions is the chest X-ray. Recent developments in deep learning have demonstrated several successful cases of illness diagnosis from chest X-ray...
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