AIMC Topic: Radiography, Thoracic
Clear Filters Showing 121 to 130 of 591 articles
Deep learning prediction of survival in patients with heart failure using chest radiographs.
The international journal of cardiovascular imaging
Jul 5, 2024
Heart failure (HF) is associated with high rates of morbidity and mortality. The value of deep learning survival prediction models using chest radiographs in patients with heart failure is currently unclear. The aim of our study is to develop and val...
Deep learning pneumoconiosis staging and diagnosis system based on multi-stage joint approach.
BMC medical imaging
Jul 2, 2024
BACKGROUND: Pneumoconiosis has a significant impact on the quality of patient survival due to its difficult staging diagnosis and poor prognosis. This study aimed to develop a computer-aided diagnostic system for the screening and staging of pneumoco...
Comparison of lung ultrasound assisted by artificial intelligence to radiology examination in pneumothorax.
Journal of clinical ultrasound : JCU
Jun 29, 2024
BACKGROUND: Lung ultrasound can evaluate for pneumothorax but the accuracy of diagnosis depends on experience among physicians. This study aimed to investigate the sensitivity and specificity of intelligent lung ultrasound in comparison with chest x-...
The limits of fair medical imaging AI in real-world generalization.
Nature medicine
Jun 28, 2024
As artificial intelligence (AI) rapidly approaches human-level performance in medical imaging, it is crucial that it does not exacerbate or propagate healthcare disparities. Previous research established AI's capacity to infer demographic data from c...
A deep learning-based algorithm for pulmonary tuberculosis detection in chest radiography.
Scientific reports
Jun 28, 2024
In tuberculosis (TB), chest radiography (CXR) patterns are highly variable, mimicking pneumonia and many other diseases. This study aims to evaluate the efficacy of Google teachable machine, a deep neural network-based image classification tool, to d...
ISLE: An Intelligent Streaming Framework for High-Throughput AI Inference in Medical Imaging.
Journal of imaging informatics in medicine
Jun 27, 2024
As the adoption of artificial intelligence (AI) systems in radiology grows, the increase in demand for greater bandwidth and computational resources can lead to greater infrastructural costs for healthcare providers and AI vendors. To that end, we de...
A retrospective study of deep learning generalization across two centers and multiple models of X-ray devices using COVID-19 chest-X rays.
Scientific reports
Jun 25, 2024
Generalization of deep learning (DL) algorithms is critical for the secure implementation of computer-aided diagnosis systems in clinical practice. However, broad generalization remains to be a challenge in machine learning. This research aims to ide...
Enhancing semantic segmentation in chest X-ray images through image preprocessing: ps-KDE for pixel-wise substitution by kernel density estimation.
PloS one
Jun 24, 2024
BACKGROUND: In medical imaging, the integration of deep-learning-based semantic segmentation algorithms with preprocessing techniques can reduce the need for human annotation and advance disease classification. Among established preprocessing techniq...
Development of a new prognostic model to predict pneumonia outcome using artificial intelligence-based chest radiograph results.
Scientific reports
Jun 22, 2024
This study aimed to develop a new simple and effective prognostic model using artificial intelligence (AI)-based chest radiograph (CXR) results to predict the outcomes of pneumonia. Patients aged > 18 years, admitted the treatment of pneumonia betwee...
Prospective Evaluation of Artificial Intelligence Triage of Incidental Pulmonary Emboli on Contrast-Enhanced CT Examinations of the Chest or Abdomen.
AJR. American journal of roentgenology
Jun 20, 2024
Artificial intelligence (AI) algorithms improved detection of incidental pulmonary embolism (IPE) on contrast-enhanced CT (CECT) examinations in retrospective studies; however, prospective validation studies are lacking. The purpose of this study w...
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