Latest AI and machine learning research in radiology for healthcare professionals.
PURPOSE: Traditional methods of vertebral identification have predominantly relied on relative approaches, depending on discernible landmarks. Artificial Intelligence (AI) has emerged as a transformative force in radiology, aiming to augment the workflow of radiologists and the benefit of patients. This study aims to investigate the real-world application of picture archiving and communication sys...
BACKGROUND AND OBJECTIVE: Our aim was to evaluate whether combining the maximum restriction score derived from restriction spectrum imaging (RSIrsmax) with deep learning (DL) models can enhance patient-level detection of clinically significant prostate cancer (csPCa) in comparison to Prostate Imaging-Reporting and Data System (PI-RADS) or RSIrsmax alone. METHODS: A total of 1892 patients from seve...
While mammography is the standard modality for detecting microcalcifications (MCs), their real-time detection with ultrasound imaging can be invaluabl...
AIMS: Accurate prediction of major adverse cardiovascular events (MACE) is crucial for risk stratification in patients with suspected coronary artery ...
OBJECTIVES: Artificial intelligence (AI) and machine learning (ML) are increasingly integrated into diagnostic imaging. This review examines how AI ad...
BACKGROUND: Brain age gap (BAG)-the difference between predicted and chronological age-captures neurobiological aging, but MRI-only models insufficien...
BACKGROUND: Current reference standards for measuring gastric emptying and motility are not considered optimal due to the time required, ionizing radi...
Radiology is among the most capital-intensive specialities in healthcare, relying on high-cost imaging equipment, complex information technology infra...
Gout, a prevalent and treatable form of crystal-induced arthritis, results from monosodium urate (MSU) crystal deposition in articular and periarticul...
PURPOSE: The tibial slope is a well-known risk factor for anterior cruciate ligament (ACL) injury. As machine learning continues to progress, it has b...
BACKGROUND: Fibroepithelial breast lesions, including fibroadenomas and phyllodes tumors (PTs), can be difficult to classify on needle biopsy. Misclas...
BACKGROUND: The integration of large language models (LLMs) such as ChatGPT into radiology has introduced new possibilities for structured reporting. ...
BACKGROUND: Coronary revascularization decision-making for patients with coronary artery disease (CAD) can be complex and challenging. Artificial inte...
OBJECTIVE: OpenAI, Google, and Microsoft have recently developed popular large language models (LLMs) with incredible clinical applications. LLMs spec...
BACKGROUND: Dental caries is the most prevalent chronic, noncommunicable condition affecting individuals of all ages and socio-economic status. The re...
The scarcity of subspecialist medical expertise poses a considerable challenge for healthcare delivery. This issue is particularly acute in cardiology...
BACKGROUND: We aimed to develop a novel cardiac magnetic resonance (CMR)-based method for quantifying myocardial synchrony and evaluate its diagnostic...
AIMS: Coronary angiography might contain clinically relevant information, beyond its traditional role in delineating coronary artery disease. We sough...
AIMS: Catheter-based coronary intervention is an effective treatment for acute coronary syndrome. However, calcified plaques pose significant challeng...
BACKGROUND AND PURPOSE: Olfactory stimuli are known to have a significant effect on cognitive functions. However, their effect on risky decision-makin...