Latest AI and machine learning research in radiology for healthcare professionals.
Intracranial hemorrhage (ICH) is an emergency clinical condition that requires rapid diagnosis. In patients with suspected ICH, head computed tomography (CT) is the first-line imaging modality because it provides fast results. In recent years, interest has increased in 3D deep learning approaches for automated scan-level ICH detection. However, real-world deployability is limited by high computati...
BACKGROUND: Artificial intelligence (AI) has emerged as a promising tool for prostate cancer (PCa) risk stratification and outcome prediction. However, current studies often lack multicenter external validation, have limited sample sizes, present significant intermodel variability, and face overfitting concerns. OBJECTIVE: This study aimed to comprehensively evaluate the diagnostic performance of ...
The long-term use of opioids for analgesia is associated with serious adverse effects such as addiction and respiratory depression, as well as a poten...
Foundation models are recognized for their broad generalizability across diverse tasks, which has driven their widespread adoption. In medicine, howev...
RATIONALE AND OBJECTIVES: The 70-gene signature (MammaPrint) guides risk assessment and treatment in the hormone receptor-positive/human epidermal gro...
OBJECTIVE: To develop and validate a nomogram integrating artificial intelligence (AI)-extracted ultrasound features with clinic pathologic data for n...
Pediatric fracture detection in plain radiographs presents distinct clinical challenges due to the presence of growth plates, incomplete ossification,...
Quantifying muscle health at scale has been limited by the difficulty of segmenting individual muscles on MRI. We developed an automated 3D deep-learn...
BACKGROUND: Multiple sclerosis (MS) is a chronic neurological disease affecting both white and gray matter of the central nervous system. Despite the ...
Cell types represent groups of cells with shared anatomical and functional properties. Traditional brain cell type atlases rely on single-cell sequenc...
Image-based machine learning tools are powerful resources for analyzing medical images, with deep learning-based semantic segmentation commonly utiliz...
BACKGROUND: Facial assessment is central to treatment planning and outcome evaluation. Traditional approaches, including visual inspection, manual ant...
Radiomics applied to two-dimensional breast ultrasound has emerged as a potential noninvasive approach for differentiating benign from malignant breas...
Deep learning models for medical image analysis often fail in clinical deployment due to domain shift from varied acquisition hardware and protocols. ...
Breast density is a key factor in mammographic screening, as high-density tissue increases cancer risk and can obscure lesions, reducing diagnostic se...
Patients increasingly use large language models (LLMs) to interpret radiology reports, yet the reliability of radiologist oversight in detecting error...
Deep learning (DL) is increasingly applied to automate brain tumor classification from magnetic resonance imaging (MRI), yet meaningful clinical deplo...
Fetal congenital heart disease (FCHD) remains a leading cause of infant mortality globally, yet the clinical deployment of deep learning models for au...
Microvascular invasion (MVI) represents a critical prognostic determinant in hepatocellular carcinoma (HCC), yet preoperative prediction remains chall...
BACKGROUND: Intraoperative ultrasound plays a central role in hepatobiliary surgery for the detection of liver lesions, but image interpretation is ch...