Radiology

Commercial AI platforms improve reproducibility but fail to meet clinical precision thresholds for intracranial aneurysm measurement: implications for serial surveillance.

BACKGROUND: Intracranial aneurysm affect 3-7% of the global population, with rupture causing > 80% of non-traumatic subarachnoid hemorrhage and approximately 50% mortality. Clinical management relies on precise measurement of aneurysm neck width and maximum length, where ≥ 1 mm growth signals elevated rupture risk. computed tomography angiography enables non-invasive monitoring but manual measurem...

Performance of machine learning algorithms in diffusion tensor imaging of movement disorders: an exploratory meta-analysis.

BACKGROUND: Machine learning (ML) applied to diffusion tensor imaging (DTI) has emerged as a promising tool for detecting microstructural brain alterations in movement disorders. However, existing studies vary widely in design, sample size, imaging pipelines, and analytic rigor, resulting in high methodological heterogeneity that limits quantitative comparability. OBJECTIVES: This exploratory meta...

Machine learning-facilitated real-time acoustic trapping in time-varying multi-medium environments toward magnetic resonance imaging-guided microbubble manipulation.

Magnetic resonance imaging-guided acoustic trapping is expected to manipulate drug carriers (e.g., microbubbles) within the body, potentially improvin...

Artificial intelligence for fracture detection on computed tomography: a comprehensive systematic review and meta-analysis of diagnostic test accuracy in non-commercial and commercial solutions.

Rising patient volumes, the increasing use of computed tomography (CT) imaging in emergency departments and the resulting prolonged waiting times high...

Real-world use of PACS-integrated automated spine numbering in MRI.

PURPOSE: Traditional methods of vertebral identification have predominantly relied on relative approaches, depending on discernible landmarks. Artific...

Deep Learning Artificial Intelligence and Restriction Spectrum Imaging for Patient-level Detection of Clinically Significant Prostate Cancer on Biparametric Magnetic Resonance Imaging.

BACKGROUND AND OBJECTIVE: Our aim was to evaluate whether combining the maximum restriction score derived from restriction spectrum imaging (RSIrsmax)...

Classification of twinkling artifacts and blood flow for in vivo detection of breast microcalcifications.

While mammography is the standard modality for detecting microcalcifications (MCs), their real-time detection with ultrasound imaging can be invaluabl...

Development and validation of a computed tomography myocardial perfusion imaging radiomic model for major adverse cardiovascular events prediction: a multicenter study.

AIMS: Accurate prediction of major adverse cardiovascular events (MACE) is crucial for risk stratification in patients with suspected coronary artery ...

Malpractice in the machine age: Legal and ethical responses to machine learning in medical imaging.

OBJECTIVES: Artificial intelligence (AI) and machine learning (ML) are increasingly integrated into diagnostic imaging. This review examines how AI ad...

Spatial amyloid-informed multimodal brain age as an early marker of Alzheimer's-related vulnerability and risk stratification.

BACKGROUND: Brain age gap (BAG)-the difference between predicted and chronological age-captures neurobiological aging, but MRI-only models insufficien...

Quantitative Real-Time MRI for the Assessment of Gastric Motility.

BACKGROUND: Current reference standards for measuring gastric emptying and motility are not considered optimal due to the time required, ionizing radi...

Financial Reasoning in Radiology: Interpreting Risk, Value, and Capital Allocation for Resilient and High-Value Imaging Services.

Radiology is among the most capital-intensive specialities in healthcare, relying on high-cost imaging equipment, complex information technology infra...

The Evolving Role of Musculoskeletal Ultrasound in Gout: From Diagnosis to Management-A Narrative Review.

Gout, a prevalent and treatable form of crystal-induced arthritis, results from monosodium urate (MSU) crystal deposition in articular and periarticul...

Machine learning model identifies tibial anatomical variables as potential risk factors for anterior cruciate ligament injury.

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...

AI-FLEET: Phase I-Multimodal Deep Learning Model for Phyllodes Tumor Classification.

BACKGROUND: Fibroepithelial breast lesions, including fibroadenomas and phyllodes tumors (PTs), can be difficult to classify on needle biopsy. Misclas...

Evaluating the role of ChatGPT in structured radiology reporting: A systematic review.

BACKGROUND: The integration of large language models (LLMs) such as ChatGPT into radiology has introduced new possibilities for structured reporting. ...

Perceived Potential and Challenges of Supporting Coronary Artery Disease Treatment Decisions With AI: Qualitative Study.

BACKGROUND: Coronary revascularization decision-making for patients with coronary artery disease (CAD) can be complex and challenging. Artificial inte...

Comparative diagnostic capability of large language models in neurosurgery.

OBJECTIVE: OpenAI, Google, and Microsoft have recently developed popular large language models (LLMs) with incredible clinical applications. LLMs spec...

Emerging trends in the early diagnosis of dental caries: a scoping review of artificial intelligence, digital diagnostics, and teledentistry.

BACKGROUND: Dental caries is the most prevalent chronic, noncommunicable condition affecting individuals of all ages and socio-economic status. The re...

A large language model for complex cardiology care.

The scarcity of subspecialist medical expertise poses a considerable challenge for healthcare delivery. This issue is particularly acute in cardiology...