AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Radiography, Thoracic

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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
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
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
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
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)
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
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.

BarlowTwins-CXR: enhancing chest X-ray abnormality localization in heterogeneous data with cross-domain self-supervised learning.

BMC medical informatics and decision making
BACKGROUND: Chest X-ray imaging based abnormality localization, essential in diagnosing various diseases, faces significant clinical challenges due to complex interpretations and the growing workload of radiologists. While recent advances in deep lea...

Labelling with dynamics: A data-efficient learning paradigm for medical image segmentation.

Medical image analysis
The success of deep learning on image classification and recognition tasks has led to new applications in diverse contexts, including the field of medical imaging. However, two properties of deep neural networks (DNNs) may limit their future use in m...

FACNN: fuzzy-based adaptive convolution neural network for classifying COVID-19 in noisy CXR images.

Medical & biological engineering & computing
COVID-19 detection using chest X-rays (CXR) has evolved as a significant method for early diagnosis of the pandemic disease. Clinical trials and methods utilize X-ray images with computer and intelligent algorithms to improve detection and classifica...

Deep Omni-Supervised Learning for Rib Fracture Detection From Chest Radiology Images.

IEEE transactions on medical imaging
Deep learning (DL)-based rib fracture detection has shown promise of playing an important role in preventing mortality and improving patient outcome. Normally, developing DL-based object detection models requires a huge amount of bounding box annotat...