AIMC Topic: Radiography, Thoracic
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The use of artificial intelligence to aid the diagnosis of lung cancer - A retrospective-cohort study.
Radiography (London, England : 1995)
Jan 30, 2025
INTRODUCTION: AI software in the form of deep learning-based automatic detection (DLAD) algorithms for chest X-ray (CXR) interpretation have shown success in early detection of lung cancer (LC), however, there remains uncertainty related to clinical ...
Clinical Validation of an AI System for Pneumoconiosis Detection Using Chest X-rays.
Journal of occupational and environmental medicine
Jan 28, 2025
OBJECTIVE: The aims of the study were to develop and evaluate "eTóraxLaboral," an intelligent platform for detecting signs of pneumoconiosis in chest radiographs and to assess its predictive capacity.
A commercial AI tool untrained for COVID-19 demonstrates slight improvement in the interpretation of COVID-19 pneumonia x-rays, especially among inexperienced readers.
Radiologia
Jan 24, 2025
INTRODUCTION: Our objective is to evaluate how useful an artificial intelligence (AI) tool is to chest radiograph readers with various levels of expertise for the diagnosis of COVID-19 pneumonia when the tool has been trained on a non-COVID-19 pneumo...
DenseSeg: joint learning for semantic segmentation and landmark detection using dense image-to-shape representation.
International journal of computer assisted radiology and surgery
Jan 23, 2025
PURPOSE: Semantic segmentation and landmark detection are fundamental tasks of medical image processing, facilitating further analysis of anatomical objects. Although deep learning-based pixel-wise classification has set a new-state-of-the-art for se...
Interpretable COVID-19 chest X-ray detection based on handcrafted feature analysis and sequential neural network.
Computers in biology and medicine
Jan 22, 2025
Deep learning methods have significantly improved medical image analysis, particularly in detecting COVID-19 chest X-rays. Nonetheless, these methodologies frequently inhibit some drawbacks, such as limited interpretability, extensive computational r...
ATEDU-NET: An Attention-Embedded Deep Unet for multi-disease diagnosis in chest X-ray images, breast ultrasound, and retina fundus.
Computers in biology and medicine
Jan 21, 2025
In image segmentation for medical image analysis, effective upsampling is crucial for recovering spatial information lost during downsampling. This challenge becomes more pronounced when dealing with diverse medical image modalities, which can signif...
An effective COVID-19 classification in X-ray images using a new deep learning framework.
Journal of X-ray science and technology
Jan 19, 2025
BackgroundThe global concern regarding the diagnosis of lung-related diseases has intensified due to the rapid transmission of coronavirus disease 2019 (COVID-19). Artificial Intelligence (AI) based methods are emerging technologies that help to iden...
CXR-LLaVA: a multimodal large language model for interpreting chest X-ray images.
European radiology
Jan 15, 2025
OBJECTIVE: This study aimed to develop an open-source multimodal large language model (CXR-LLaVA) for interpreting chest X-ray images (CXRs), leveraging recent advances in large language models (LLMs) to potentially replicate the image interpretation...
Multimodal deep learning for predicting in-hospital mortality in heart failure patients using longitudinal chest X-rays and electronic health records.
The international journal of cardiovascular imaging
Jan 9, 2025
Amid an aging global population, heart failure has become a leading cause of hospitalization among older people. Its high prevalence and mortality rates underscore the importance of accurate mortality prediction for swift disease progression assessme...
Male
Middle Aged
Risk Assessment
Risk Factors
Aged
Aged, 80 and over
Predictive Value of Tests
Feasibility Studies
Heart Failure
Radiography, Thoracic
Deep Learning
Time Factors
Hospital Mortality
Humans
Decision Support Techniques
Female
Reproducibility of Results
Prognosis
Electronic Health Records
Repeatability of AI-based, automatic measurement of vertebral and cardiovascular imaging biomarkers in low-dose chest CT: the ImaLife cohort.
European radiology
Jan 8, 2025
OBJECTIVE: To evaluate the repeatability of AI-based automatic measurement of vertebral and cardiovascular markers on low-dose chest CT.
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