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Radiographic Image Interpretation, Computer-Assisted

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A comprehensive segmentation of chest X-ray improves deep learning-based WHO radiologically confirmed pneumonia diagnosis in children.

European radiology
OBJECTIVES: To investigate a comprehensive segmentation of chest X-ray (CXR) in promoting deep learning-based World Health Organization's (WHO) radiologically confirmed pneumonia diagnosis in children.

Utilizing Deep Learning and Computed Tomography to Determine Pulmonary Nodule Activity in Patients With Nontuberculous Mycobacterial-Lung Disease.

Journal of thoracic imaging
PURPOSE: To develop and evaluate a deep convolutional neural network (DCNN) model for the classification of acute and chronic lung nodules from nontuberculous mycobacterial-lung disease (NTM-LD) on computed tomography (CT).

Super-resolution deep learning reconstruction at coronary computed tomography angiography to evaluate the coronary arteries and in-stent lumen: an initial experience.

BMC medical imaging
A super-resolution deep learning reconstruction (SR-DLR) algorithm trained using data acquired on the ultrahigh spatial resolution computed tomography (UHRCT) has the potential to provide better image quality of coronary arteries on the whole-heart, ...

Improving the depiction of small intracranial vessels in head computed tomography angiography: a comparative analysis of deep learning reconstruction and hybrid iterative reconstruction.

Radiological physics and technology
This study aimed to evaluate the ability of deep learning reconstruction (DLR) compared to that of hybrid iterative reconstruction (IR) to depict small vessels on computed tomography (CT). DLR and two types of hybrid IRs were used for image reconstru...

[Physical Properties of Small Focal Spot Imaging with Deep Learning Reconstruction in Chest-abdominal Plain CT].

Nihon Hoshasen Gijutsu Gakkai zasshi
PURPOSE: The aim of this study was to compare the physical properties of small focal spot imaging with deep learning reconstruction (DLR) and small or large focal spot imaging with hybrid iterative reconstruction (IR) in chest-abdominal plain compute...

Automatic detection and classification of nasopalatine duct cyst and periapical cyst on panoramic radiographs using deep convolutional neural networks.

Oral surgery, oral medicine, oral pathology and oral radiology
OBJECTIVE: The aim of this study was to evaluate a deep convolutional neural network (DCNN) method for the detection and classification of nasopalatine duct cysts (NPDC) and periapical cysts (PAC) on panoramic radiographs.

Evaluation of deep learning for detecting intraosseous jaw lesions in cone beam computed tomography volumes.

Oral surgery, oral medicine, oral pathology and oral radiology
OBJECTIVE: The study aim was to develop and assess the performance of a deep learning (DL) algorithm in the detection of radiolucent intraosseous jaw lesions in cone beam computed tomography (CBCT) volumes.

Computer-aided diagnosis of chest X-ray for COVID-19 diagnosis in external validation study by radiologists with and without deep learning system.

Scientific reports
To evaluate the diagnostic performance of our deep learning (DL) model of COVID-19 and investigate whether the diagnostic performance of radiologists was improved by referring to our model. Our datasets contained chest X-rays (CXRs) for the following...

Deep Learning Image Reconstruction Algorithm for CCTA: Image Quality Assessment and Clinical Application.

Journal of computer assisted tomography
OBJECTIVE: The increasing number of coronary computed tomography angiography (CCTA) requests raised concerns about dose exposure. New dose reduction strategies based on artificial intelligence have been proposed to overcome limitations of iterative r...