AI Medical Compendium Journal:
Journal of applied clinical medical physics

Showing 91 to 100 of 159 articles

Feasibility study of deep learning-based markerless real-time lung tumor tracking with orthogonal X-ray projection images.

Journal of applied clinical medical physics
PURPOSE: The feasibility of a deep learning-based markerless real-time tumor tracking (RTTT) method was retrospectively studied with orthogonal kV X-ray images and clinical tracking records acquired during lung cancer treatment.

Fully automatic tumor segmentation of breast ultrasound images with deep learning.

Journal of applied clinical medical physics
BACKGROUND: Breast ultrasound (BUS) imaging is one of the most prevalent approaches for the detection of breast cancers. Tumor segmentation of BUS images can facilitate doctors in localizing tumors and is a necessary step for computer-aided diagnosis...

Respiratory motion prediction based on deep artificial neural networks in CyberKnife system: A comparative study.

Journal of applied clinical medical physics
BACKGROUND: In external beam radiotherapy, a prediction model is required to compensate for the temporal system latency that affects the accuracy of radiation dose delivery. This study focused on a thorough comparison of seven deep artificial neural ...

Image quality improvement in low-dose chest CT with deep learning image reconstruction.

Journal of applied clinical medical physics
OBJECTIVES: To investigate the clinical utility of deep learning image reconstruction (DLIR) for improving image quality in low-dose chest CT in comparison with 40% adaptive statistical iterative reconstruction-Veo (ASiR-V40%) algorithm.

Super-resolution of brain tumor MRI images based on deep learning.

Journal of applied clinical medical physics
INTRODUCTION: To explore and evaluate the performance of MRI-based brain tumor super-resolution generative adversarial network (MRBT-SR-GAN) for improving the MRI image resolution in brain tumors.

Applying a CT texture analysis model trained with deep-learning reconstruction images to iterative reconstruction images in pulmonary nodule diagnosis.

Journal of applied clinical medical physics
OBJECTIVE: To investigate the feasibility and accuracy of applying a computed tomography (CT) texture analysis model trained with deep-learning reconstruction images to iterative reconstruction images for classifying pulmonary nodules.

Semi-supervised classification of fundus images combined with CNN and GCN.

Journal of applied clinical medical physics
PURPOSE: Diabetic retinopathy (DR) is one of the most serious complications of diabetes, which is a kind of fundus lesion with specific changes. Early diagnosis of DR can effectively reduce the visual damage caused by DR. Due to the variety and diffe...

Artificial intelligence (AI) versus expert: A comparison of left ventricular outflow tract velocity time integral (LVOT-VTI) assessment between ICU doctors and an AI tool.

Journal of applied clinical medical physics
PURPOSE: The application of point of care ultrasound (PoCUS) in medical education is a relatively new course. There are still great differences in the existence, quantity, provision, and depth of bedside ultrasound education. The left ventricular out...

Deep learning for emergency ascites diagnosis using ultrasonography images.

Journal of applied clinical medical physics
PURPOSE: The detection of abdominal free fluid or hemoperitoneum can provide critical information for clinical diagnosis and treatment, particularly in emergencies. This study investigates the use of deep learning (DL) for identifying peritoneal free...