Radiology

Artificial Intelligence-Driven Angiographic Quantification of Fractional Flow Reserve: Proof-of-Concept Validation Study.

BACKGROUND: Angiography-based fractional flow reserve (FFR) techniques offer wire-free alternatives but often require manual segmentation and 3-dimensional reconstruction. Although an artificial intelligence-driven approach automates these steps, validation remains limited. This study investigated the diagnostic performance of an artificial intelligence-driven angiography-based FFR (medipixel FFR ...

Discordance Between Textual Reasoning and Visual Interpretation in Large Language Models for Low Back Pain: Cross-Sectional Quantitative Evaluation and Exploratory Multimodal Stress Test.

BACKGROUND: Large language models (LLMs) are rapidly evolving from text-based agents to multimodal systems capable of interpreting medical images. While their textual reasoning has improved, the safety implications of this shift remain underexplored, specifically regarding the alignment between visual interpretation and textual advice in low back pain (LBP) management. OBJECTIVE: This study aims t...

A Multiparametric MRI‑Based Deep Learning Model for Binary Triage of Lung Versus Non‑lung Brain Metastases: A Multicenter Proof‑of‑Concept Study.

Identifying the origin of brain metastases (BM) is essential for personalized treatment, particularly in patients with carcinoma of unknown primary or...

Explainable 3D deep learning from full-head MRI suggests scalp and skull involvement in Alzheimer's disease.

BackgroundEmerging evidence suggests that extracranial tissues and immune-glymphatic interactions may contribute to neurodegenerative processes in Alz...

Intravoxel incoherent motion imaging with improved image quality by deep learning reconstruction for prediction of lymph-vascular space invasion in patients with cervical cancer.

PURPOSE: To evaluate the performance of intravoxel incoherent motion (IVIM) imaging with deep learning reconstruction (DLR) for prediction of lymph-va...

A stacking model for AI-assisted diagnosis of suspected pituitary microadenomas on non-contrast T1COR MRI: a multicenter reader study on bridging the experience gap.

OBJECTIVE: This study sought to quantify, through a multi-reader study, whether AI assistance improves diagnostic accuracy across experience levels, r...

Automated deep learning-based segmentation and volumetric analysis of meningiomas.

INTRODUCTION: Meningiomas are the most common primary intracranial tumors and are frequently monitored over extended periods. Volumetric assessment ty...

Performance of generative artificial intelligence in oral and maxillofacial radiology based on the board-certification examination of Japan.

OBJECTIVE: To evaluate the performance and potential utility of generative artificial intelligence (AI) in oral and maxillofacial radiology using the ...

Two-stage deep learning framework for magnetic resonance imaging-based detection of temporomandibular joint osteoarthritis.

OBJECTIVES: Cone-beam computed tomography (CBCT) is the reference standard for detecting osseous changes in temporomandibular joint osteoarthritis (TM...

Diagnostic Accuracy of Medical Imaging-Based Artificial Intelligence for Osteonecrosis of the Femoral Head: Systematic Review and Meta-Analysis.

BACKGROUND: Osteonecrosis of the femoral head (ONFH) is a common cause of hip disability in clinical practice. Early and accurate diagnosis can delay ...

3D interactive radiology reports: referring physicians' acceptance of artificial intelligence varies across practice settings.

INTRODUCTION: A 3D interactive report is a state-of-the-art artificial intelligence (AI) tool that integrates a patient's imaging history into an intu...

Breast tumor detection in ultrasound images using adaptive hierarchical pruning of non-tumor regions.

BACKGROUND: The development of deep learning techniques has greatly improved tumor detection and analysis in breast ultrasound images. Despite their i...

HARU-Net: Hybrid Attention Residual U-Net for Edge-Preserving Denoising in Cone-Beam Computed Tomography.

OBJECTIVES: Cone-beam computed tomography (CBCT) is widely used in dental and maxillofacial imaging, but low-dose acquisition introduces strong, spati...

Coronary Artery Calcification: Contemporary Diagnosis from Invasive Imaging to Percutaneous Treatment.

Coronary artery calcification (CAC) represents a significant challenge in contemporary interventional cardiology, substantially affecting percutaneous...

Outcomes of Single-View Asymmetries Recalled From Screening Mammography and Exploratory Analysis of AI Detection.

PURPOSE: To evaluate the diagnostic outcomes of single-view asymmetries (SVAs) recalled from screening mammography and to explore the association betw...

Intelligent Organ Recognition Technology Combined With an Artificial Intelligence Iterative Reconstruction Algorithm in Contrast-enhanced CT of the Chest, Abdomen, and Pelvis.

RATIONALE AND OBJECTIVES: To evaluate the application value of intelligent organ recognition technology combined with the artificial intelligence iter...

Deep learning based hybrid system for automatic detection and classification of nasopalatine canal in cone beam computed tomography images.

PURPOSE: The objective of this study was to develop and evaluate a deep learning-based hybrid system for the automatic detection and classification of...

Interpretable Machine Learning Using Multimodal Ultrasound and MRI Features for Predicting Axillary Lymph Node Metastasis in Breast Cancer: A Dual-Center Retrospective Study.

The objective of the study is to develop and externally evaluate interpretable machine learning models integrating routinely reported ultrasound and M...

A Grok API-Based DICOM File-Attachment Structured Reporting Workflow for Silicone Breast Implant MRI: a Single-Center Proof-of-Concept Agreement Study.

This study aims to evaluate a Grok application programming interface (API)-based file-attachment structured reporting workflow for silicone breast imp...

Utility of artificial intelligence in predicting clinical outcomes of angiographic procedures: a comprehensive scoping review.

Artificial intelligence (AI) is emerging as a transformative tool in cardiovascular imaging, particularly in coronary angiography. With the growing in...