Radiology

Revolutionizing early Alzheimer's disease and mild cognitive impairment diagnosis: a deep learning MRI meta-analysis.

BACKGROUND:  The early diagnosis of Alzheimer's disease (AD) and mild cognitive impairment (MCI) remains a significant challenge in neurology, with conventional methods often limited by subjectivity and variability in interpretation. Integrating deep learning with artificial intelligence (AI) in magnetic resonance imaging (MRI) analysis emerges as a transformative approach, offering the potential ...

A Shape-Consistent Deep-Learning Segmentation Architecture for Low-Quality and High-Interference Myocardial Contrast Echocardiography.

OBJECTIVE: Myocardial contrast echocardiography (MCE) plays a crucial role in diagnosing ischemia, infarction, masses and other cardiac conditions. In the realm of MCE image analysis, accurate and consistent myocardial segmentation results are essential for enabling automated analysis of various heart diseases. However, current manual diagnostic methods in MCE suffer from poor repeatability and li...

Predicting ultrasound wave stimulated bone growth in bioinspired scaffolds using machine learning.

For conditions like osteoporosis, changes in bone pore geometry even when porosity is constant have been shown to correlate to increased fracture risk...

Automatic localization of anatomical landmarks in head cine fluoroscopy images via deep learning.

BACKGROUND: Fluoroscopy guided interventions (FGIs) pose a risk of prolonged radiation exposure; personalized patient dosimetry is necessary to improv...

Comparison of the Accuracy of a Deep Learning Method for Lesion Detection in PET/CT and PET/MRI Images.

PURPOSE: Develop a universal lesion recognition algorithm for PET/CT and PET/MRI, validate it, and explore factors affecting performance.

Intraoral Ultrasound Imaging Using a Rotational Transducer with Periodontal Feature Identification by Machine Learning.

Innovative intraoral ultrasound devices with smart artificial intelligence-based identification for dento-anatomy could provide crucial information fo...

Self-normalization for a 1 mmresolution clinical PET system using deep learning.

This work proposes, for the first time, an image-based end-to-end self-normalization framework for positron emission tomography (PET) using conditiona...

Segmentation of ovarian cyst in ultrasound images using AdaResU-net with optimization algorithm and deep learning model.

Ovarian cysts pose significant health risks including torsion, infertility, and cancer, necessitating rapid and accurate diagnosis. Ultrasonography is...

Automatic detection and visualization of temporomandibular joint effusion with deep neural network.

This study investigated the usefulness of deep learning-based automatic detection of temporomandibular joint (TMJ) effusion using magnetic resonance i...

Peritumoral edema enhances MRI-based deep learning radiomic model for axillary lymph node metastasis burden prediction in breast cancer.

To investigate whether peritumoral edema (PE) could enhance deep learning radiomic (DLR) model in predicting axillary lymph node metastasis (ALNM) bur...

The Usefulness of Low-Kiloelectron Volt Virtual Monochromatic Contrast-Enhanced Computed Tomography with Deep Learning Image Reconstruction Technique in Improving the Delineation of Pancreatic Ductal Adenocarcinoma.

To evaluate the usefulness of low-keV multiphasic computed tomography (CT) with deep learning image reconstruction (DLIR) in improving the delineation...

Optimizing Acute Stroke Segmentation on MRI Using Deep Learning: Self-Configuring Neural Networks Provide High Performance Using Only DWI Sequences.

Segmentation of infarcts is clinically important in ischemic stroke management and prognostication. It is unclear what role the combination of DWI, AD...

Artificial intelligence for treatment delivery: image-guided radiotherapy.

Radiation therapy (RT) is a highly digitized field relying heavily on computational methods and, as such, has a high affinity for the automation poten...

Automatic deep learning segmentation of the hippocampus on high-resolution diffusion magnetic resonance imaging and its application to the healthy lifespan.

Diffusion tensor imaging (DTI) can provide unique contrast and insight into microstructural changes with age or disease of the hippocampus, although i...

Deep learning-aided respiratory motion compensation in PET/CT: addressing motion induced resolution loss, attenuation correction artifacts and PET-CT misalignment.

PURPOSE: Respiratory motion (RM) significantly impacts image quality in thoracoabdominal PET/CT imaging. This study introduces a unified data-driven r...

Practical Evaluation of ChatGPT Performance for Radiology Report Generation.

RATIONALE AND OBJECTIVES: The process of generating radiology reports is often time-consuming and labor-intensive, prone to incompleteness, heterogene...

How do large language models answer breast cancer quiz questions? A comparative study of GPT-3.5, GPT-4 and Google Gemini.

Applications of large language models (LLMs) in the healthcare field have shown promising results in processing and summarizing multidisciplinary info...

Advancing breast ultrasound diagnostics through hybrid deep learning models.

Today, doctors rely heavily on medical imaging to identify abnormalities. Proper classification of these abnormalities enables them to take informed a...

End-to-end reproducible AI pipelines in radiology using the cloud.

Artificial intelligence (AI) algorithms hold the potential to revolutionize radiology. However, a significant portion of the published literature lack...

Deep Learning-Assisted Automatic Diagnosis of Anterior Cruciate Ligament Tear in Knee Magnetic Resonance Images.

Anterior cruciate ligament (ACL) tears are prevalent knee injures, particularly among active individuals. Accurate and timely diagnosis is essential f...