AI Medical Compendium Topic

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

Lung Diseases

Showing 141 to 150 of 150 articles

Clear Filters

Evaluation of a deep learning-based computer-aided detection algorithm on chest radiographs: Case-control study.

Medicine
Along with recent developments in deep learning techniques, computer-aided diagnosis (CAD) has been growing rapidly in the medical imaging field. In this work, we evaluate the deep learning-based CAD algorithm (DCAD) for detecting and localizing 3 ma...

A comparison of general and disease-specific machine learning models for the prediction of unplanned hospital readmissions.

Journal of the American Medical Informatics Association : JAMIA
Unplanned hospital readmissions are a burden to patients and increase healthcare costs. A wide variety of machine learning (ML) models have been suggested to predict unplanned hospital readmissions. These ML models were often specifically trained on ...

Machine Learning/Deep Neuronal Network: Routine Application in Chest Computed Tomography and Workflow Considerations.

Journal of thoracic imaging
The constantly increasing number of computed tomography (CT) examinations poses major challenges for radiologists. In this article, the additional benefits and potential of an artificial intelligence (AI) analysis platform for chest CT examinations i...

Deep Learning for Pulmonary Image Analysis: Classification, Detection, and Segmentation.

Advances in experimental medicine and biology
Image-based computer-aided diagnosis (CAD) algorithms by the use of convolutional neural network (CNN) which do not require the image-feature extractor are powerful compared with conventional feature-based CAD algorithms which require the image-featu...

Generalizable Inter-Institutional Classification of Abnormal Chest Radiographs Using Efficient Convolutional Neural Networks.

Journal of digital imaging
Our objective is to evaluate the effectiveness of efficient convolutional neural networks (CNNs) for abnormality detection in chest radiographs and investigate the generalizability of our models on data from independent sources. We used the National ...

Deep Learning Applications in Chest Radiography and Computed Tomography: Current State of the Art.

Journal of thoracic imaging
Deep learning is a genre of machine learning that allows computational models to learn representations of data with multiple levels of abstraction using numerous processing layers. A distinctive feature of deep learning, compared with conventional ma...

Training and Validating a Deep Convolutional Neural Network for Computer-Aided Detection and Classification of Abnormalities on Frontal Chest Radiographs.

Investigative radiology
OBJECTIVES: Convolutional neural networks (CNNs) are a subtype of artificial neural network that have shown strong performance in computer vision tasks including image classification. To date, there has been limited application of CNNs to chest radio...

Application of semi-supervised deep learning to lung sound analysis.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
The analysis of lung sounds, collected through auscultation, is a fundamental component of pulmonary disease diagnostics for primary care and general patient monitoring for telemedicine. Despite advances in computation and algorithms, the goal of aut...