Radiology

Artificial intelligence for acute appendicitis diagnosis: A systematic review of current evidence, challenges, and future directions.

BACKGROUND: Acute appendicitis remains one of the most common surgical emergencies, yet its diagnosis continues to pose challenges due to overlapping clinical presentations and variable imaging findings. Artificial intelligence (AI) has recently emerged as a promising tool to enhance diagnostic accuracy, assist clinical decision-making, and potentially reduce negative appendectomy rates. METHODS: ...

Scalable and Robust Artificial Intelligence for Spine Alignment Assessment: Multicenter Study Enabled by Real-Time Data Transformation.

BACKGROUND: Artificial intelligence (AI) has shown promise for automating spinal alignment assessment in adolescent idiopathic scoliosis (AIS). However, AI models typically exhibit reduced accuracy and robustness when deployed across multiple medical centers due to variability in imaging protocols and data characteristics, potentially compromising clinical diagnosis and treatment decisions. OBJECT...

Average glandular dose prediction for breast model with patient-specific fibroglandular distribution in mammography and digital breast tomosynthesis: a machine-learning algorithms comparison.

Objective To investigate Machine Learning (ML) methodologies for predicting glandular dose conversion coefficients for breast models with patient-spec...

Pilot study: external validation of commercial veterinary radiology artificial intelligence services shows deficiencies in interpretation of general practice-sourced canine abdominal radiographs.

OBJECTIVE: To evaluate the diagnostic performance of commercial veterinary radiology AI platforms on general practice canine abdominal radiographs wit...

Artificial intelligence in medicine: a scoping review of the risk of deskilling and loss of expertise among physicians.

BACKGROUND: Artificial intelligence (AI) systems are increasingly deployed in clinical practice, particularly in radiology, pathology, endoscopy, and ...

Reliability of Rectus Femoris Ultrasound Measurements and Relationship With Truncal Muscle Mass in Healthy Individuals Using Concurrent CT Measurements as the Reference Standard: A Pilot Study.

In chronic diseases, accelerated muscle mass loss is associated with poor clinical outcomes. Computed tomography (CT) is considered a reference standa...

Cross-sectional evaluation of cardiovascular biological age using point-of-care ultrasound.

AIMS: Biological age is increasingly recognized as a superior predictor of morbidity, mortality, compared with chronological age. Artificial intellige...

Interactive Deep Learning for Myocardial Scar Segmentation Using Cardiovascular Magnetic Resonance.

BACKGROUND: Following myocardial infarction, late gadolinium-enhancement (LGE) assessed by cardiovascular magnetic resonance (CMR) provides a reliable...

Comparing large language models and human experts in interpreting MRI reports for personalized patient education.

RATIONALE AND OBJECTIVES: Knee osteoarthritis (OA) is a prevalent global condition. While MRI guides clinical decisions, its technical complexity hind...

AI-based triage and decision support in mammography and digital tomosynthesis for breast cancer screening: a paired, noninferiority trial.

Artificial intelligence (AI) systems have been demonstrated to improve the accuracy of screening mammograms. Here this prospective, paired, noninferio...

Real-world unified denoising for multi-organ fast MRI: a large-scale prospective validation.

Lengthy acquisition time remains a key bottleneck for the widespread use of MRI in clinics. While accelerated MRI can reduce scan duration, it often i...

Wavelet-Transformed Frequency Linked Attention with Selective Hierarchy for Abdominal Organ Segmentation.

The clinical use of computed tomography (CT) scans has been steadily rising due to their ability to provide detailed three-dimensional (3D) representa...

A Deep Learning Framework for Enhanced Ovarian Adnexal Mass Classification Using Routinely Acquired Ultrasound Images.

Accurate classification of ovarian masses is crucial for clinical decision-making. B-mode ultrasound is widely used for imaging adnexal masses, yet co...

Prediction of coronary artery disease using retinal optical coherence tomography angiography imaging and electronic health records: a multimodal machine learning approach.

Improved BG-PVS Quantification in Infant Brain MRI Using Anatomy-Informed Pseudo-Labels for Joint BG and PVS Segmentation.

BACKGROUND: Reliable quantification of perivascular spaces (PVS) in the basal ganglia (BG) is of growing interest for understanding the glymphatic sys...

5-Hydroxymethylcytosine profiles in circulating cell-free DNA serve as potential biomarkers for diagnosis and classification of adenomyosis.

BACKGROUND: Adenomyosis diagnosis and classification relies primarily on magnetic resonance imaging or pathological examination. Liquid biopsy has bee...

Percutaneous coronary intervention in high bleeding risk patients: a review of contemporary management strategies.

INTRODUCTION: High bleeding risk (HBR) affects over one-third of patients undergoing percutaneous coronary intervention (PCI) and is associated with e...

BWS-Net: An Optimal Deep Learning Architecture for the Anterior Bladder Wall Segmentation using Ultrasound Imaging.

Urodynamic tests are used to assess bladder function by measuring detrusor pressure, which requires invasive catheterization. Ultrasound bladder vibro...

Interpretable Clinical-Radiomics Model for Prediction of Blood Stasis and Left Atrial Appendage Thrombus.

BACKGROUND: The left atrial (LA) morphological profile, anatomically contiguous with the left atrial appendage (LAA), exhibits hemodynamic properties ...

Visualization of the Trochlear Nerve Using Deep Learning-enhanced 3D T2-weighted MR Imaging at 3T.

PURPOSE: Cranial nerve imaging with 3T MRI commonly uses 3D fast imaging employing steady-state acquisition (3D-FIESTA); however, this sequence has li...