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Radiography, Thoracic

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Automatic classification and grading of canine tracheal collapse on thoracic radiographs by using deep learning.

Veterinary radiology & ultrasound : the official journal of the American College of Veterinary Radiology and the International Veterinary Radiology Association
Tracheal collapse is a chronic and progressively worsening disease; the severity of clinical symptoms experienced by affected individuals depends on the degree of airway collapse. Cutting-edge automated tools are necessary to modernize disease screen...

Large-Scale Study on AI's Impact on Identifying Chest Radiographs with No Actionable Disease in Outpatient Imaging.

Academic radiology
RATIONALE AND OBJECTIVES: Given the high volume of chest radiographs, radiologists frequently encounter heavy workloads. In outpatient imaging, a substantial portion of chest radiographs show no actionable findings. Automatically identifying these ca...

Better performance of deep learning pulmonary nodule detection using chest radiography with pixel level labels in reference to computed tomography: data quality matters.

Scientific reports
Labeling errors can significantly impact the performance of deep learning models used for screening chest radiographs. The deep learning model for detecting pulmonary nodules is particularly vulnerable to such errors, mainly because normal chest radi...

Deep Learning-Based Localization and Detection of Malpositioned Nasogastric Tubes on Portable Supine Chest X-Rays in Intensive Care and Emergency Medicine: A Multi-center Retrospective Study.

Journal of imaging informatics in medicine
Malposition of a nasogastric tube (NGT) can lead to severe complications. We aimed to develop a computer-aided detection (CAD) system to localize NGTs and detect NGT malposition on portable chest X-rays (CXRs). A total of 7378 portable CXRs were retr...

A deep learning-based model to estimate pulmonary function from chest x-rays: multi-institutional model development and validation study in Japan.

The Lancet. Digital health
BACKGROUND: Chest x-ray is a basic, cost-effective, and widely available imaging method that is used for static assessments of organic diseases and anatomical abnormalities, but its ability to estimate dynamic measurements such as pulmonary function ...

Optimization of vision transformer-based detection of lung diseases from chest X-ray images.

BMC medical informatics and decision making
BACKGROUND: Recent advances in Vision Transformer (ViT)-based deep learning have significantly improved the accuracy of lung disease prediction from chest X-ray images. However, limited research exists on comparing the effectiveness of different opti...

Deep learning prediction of survival in patients with heart failure using chest radiographs.

The international journal of cardiovascular imaging
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
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
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-...