Latest AI and machine learning research in radiology for healthcare professionals.
Despite remarkable advances in transplant pathology, molecular diagnostics, imaging, and biomarker discovery, uncertainty remains an intrinsic feature of lung allograft evaluation. Traditionally, this uncertainty has been attributed to sampling limitations, overlapping histologic patterns, imperfect diagnostic criteria, insufficiently specific biomarkers, and interobserver variability. While these...
RATIONALE AND OBJECTIVES: To develop and validate an interpretable, multimodal, ultrasound-based machine-learning (ML) model for the non-invasive identification of early renal fibrosis in patients with chronic kidney disease (CKD). MATERIALS AND METHODS: In this prospective, multicenter study, 369 participants (healthy controls, n = 161; mild fibrosis, n = 208) were recruited from 16 institutions....
Retinal detachment (RD) is a vision-threatening condition that requires prompt intervention to preserve sight. A critical factor in treatment urgency ...
Reliable prognostic models of death or liver recurrence following resection of colorectal liver metastases are critical to stratify patients for treat...
OBJECTIVES: This study examines AI's capacity to mitigate noise-related diagnostic errors, evaluates its impact on accuracy, and explores the interpla...
Accurate organ weight determination is essential in forensic autopsy. Postmortem computed tomography (CT) combined with Artificial Intelligence (AI)-b...
Cardiovascular diseases remain as a leading cause of mortality and morbidity worldwide, with coronary artery disease (CAD) and its complications, coll...
Brain tumors are complex and life-threatening conditions that require accurate and efficient diagnostic approaches. However, existing approaches often...
Ribeiro and colleagues offer timely evidence that large language models can make oncology imaging reports more accessible to radiologists and patient ...
BACKGROUND: In Mexico, congenital heart diseases (CHD) are the most common birth defects. Despite their high mortality rate, many CHD are not detected...
BACKGROUND: Type 1 diabetes mellitus (T1DM) in children requires sustained self-management to achieve glycemic targets. Continuous glucose monitoring ...
Cardiac computed tomography angiography (CCTA) has evolved in recent years into a central pillar of non-invasive cardiovascular diagnostics and is now...
PURPOSE: Current hepatocellular carcinoma (HCC) surveillance guidelines rely on manually defined LI-RADS (Liver Imaging Reporting and Data System) fea...
PURPOSE: To evaluate whether deep learning-based respiratory-triggered (DL) 3D magnetic resonance cholangiopancreatography (MRCP) improves acquisition...
PURPOSE: Artificial intelligence (AI) is increasingly proposed as a solution to improve efficiency in radiology and nuclear medicine, particularly in ...
Clinical anatomy has become a prominent setting for educational innovation because it links anatomical science with diagnostic imaging, procedural pra...
Interpreting cardiac magnetic resonance imaging scans takes significant time and specialist expertise. In this News and Perspectives article, JMIR Cor...
BACKGROUND: Artificial intelligence (AI) has the potential to transform chest radiography interpretation by enhancing diagnostic accuracy, identifying...
A malignant disorder known as breast cancer (BC) arises when cells in breast tissue grow out of control, frequently due to genetic, hormonal, or envir...
The extension of transcatheter aortic valve replacement (TAVR) to younger patients with longer life expectancy has driven a shift in focus toward proc...