Latest AI and machine learning research in radiology for healthcare professionals.
Whole-body MRI (WB-MRI) has evolved over the past 2 decades as a noninvasive imaging technique for detecting distant metastases in prostate cancer. Since the introduction of diffusion-weighted imaging with background body signal suppression by Takahara et al. in 2004, its clinical use has expanded rapidly, particularly in the detection of bone metastases. WB-MRI offers whole-body coverage without ...
Deep learning has achieved remarkable performance in carotid intima-media (CIM) segmentation from ultrasound images, but its clinical applicability remains limited due to data scarcity, annotation variability, and low image quality. While active learning (AL) aims to minimize labeling cost while model learning, the conventional AL approaches do not fully support sparse and noisy clinical labels th...
The purpose of this study is to perform an independent assessment of three state-of-the-art tools for the detection of focal cortical dysplasia (FCD) ...
Tissue motions within body segments, such as the relative movements of muscles, fascia, and bone, remain largely unexplored despite their relevance to...
The development of trustworthy AI models is crucial, particularly for critical medical applications such as brain tumor detection using MRI images. Ho...
Identifying brain hemorrhages from magnetic resonance imaging (MRI) is a critical task for healthcare professionals. The diverse nature of MRI acquisi...
As large language models (LLMs) become increasingly integrated into clinical workflows, advanced prompting strategies offer new opportunities and chal...
OBJECTIVE: Multiparametric magnetic resonance imaging (mpMRI) detects clinically significant prostate cancer (csPCa, Gleason Grade Group ≥ 2) with hig...
PURPOSE: This study aimed to develop a hybrid decision support framework combining deep learning (DL) and machine learning (ML) to automatically class...
BACKGROUND: The changing working conditions in routine radiological reporting require the use of new methods, such as the implementation of artificial...
BACKGROUND: Artificial intelligence-based medical devices (AIMDs) have emerged as transformative technologies in modern health care. However, comprehe...
BACKGROUND: Artificial intelligence (AI) models have been increasingly explored for predicting treatment response to cognitive behavioral therapy (CBT...
Reducing scan times, radiation dose, and enhancing image quality, especially for lower-performance scanners, are critical in low-count/low-dose PET im...
Approximately 30-50% of Papillary thyroid carcinoma (PTC) patients develop cervical lymph nodes (LNs) metastasis, significantly increasing the risk of...
With increasing interest in studying biological systems across spatial scales-from centimeters down to nanometers-histology continues to be the gold s...
Computational super-resolution (SR) methods enable nanoscale imaging from single-frame wide-field or spinning-disk confocal images without hardware mo...
Cardiovascular events, predominantly ischemic, account for approximately 32% of global mortality and are expected to increase approximately 30% by 203...
Contrast enhancement technique is a prevalent problem in aiding interpretation of Magnetic Resonance Imaging (MRI) images. A low contrast MRI image ma...
Cardiovascular disease remains the leading cause of death and disability worldwide. The convergence of big data and artificial intelligence (AI) is re...
Artificial intelligence (AI) is reshaping cardiac electrophysiology by extracting information from electrocardiograms that exceeds human visual interp...