Radiology

Modeling inter-slice dependencies with temporal graph learning for Alzheimer's disease.

Accurate and early diagnosis of Alzheimer's disease (AD) remains a major clinical challenge, particularly in distinguishing mild cognitive impairment (MCI) from cognitively normal (CN) aging. Conventional approaches that rely solely on pre-trained 2D models often fail to capture the full spatial context of three-dimensional MRI volumes, as well as the temporal dependencies that exist across consec...

Transparent reporting is central to reproducible radiological AI research: A call to action.

BACKGROUND: Artificial intelligence (AI) is increasingly embedded in radiology research and practice, yet concerns about the reproducibility of AI studies remain a key barrier to regulatory acceptance and clinical translation. Transparent reporting across the analytic pipeline is essential for independent verification, evidence synthesis, and safe implementation. PURPOSE: To examine major barriers...

Stone metrics: is stone volume the new king?

PURPOSE OF REVIEW: Stone volume represents the most accurate measure of urolithiasis burden. While this may be obvious to all, this stone metric has n...

Applications of Generative Artificial Intelligence for Strabismus Surgery Video-Based Education.

OBJECTIVE: To develop educational artificial intelligence (AI)-generated videos for patients undergoing strabismus surgery and assess patient percepti...

Multiple instance learning approach for automated gallbladder cancer detection using ultrasound imaging: multi-center validation of a deep learning model with the public dataset contribution.

BACKGROUND: Gallbladder cancer (GBC) diagnosis is challenging due to overlapping imaging features. We developed and validated a multiple instance lear...

A multi-modal deep learning network for the classification of paramagnetic rim and remyelinated lesions in multiple sclerosis.

OBJECTIVES: Robust automated classification of paramagnetic rim lesions (PRLs) and remyelinated lesions based on iron and myelin content in people wit...

[Exploring the therapeutic targets and molecular mechanisms of pimecrolimus in the treatment of oral lichen planus based on network pharmacology, machine learning, and molecular docking].

Objective: To systematically investigate the potential targets and therapeutic mechanisms of pimecrolimus in the treatment of oral lichen planus (OLP)...

Comparing artificial intelligence and healthcare professional performance in surgical and interventional video analysis: a systematic review and meta-analysis.

This systematic review and meta-analysis examines the design of studies comparing the performance of artificial intelligence (AI) with that of healthc...

AI-based quality control was associated with improved fetal ultrasound image quality in low-resource settings: a real-world multicenter study from West China.

BACKGROUND: Despite rapid advances in medical artificial intelligence (AI), robust evidence for real-world clinical application-particularly in low-re...

LCNet: lightweight segmentation network for blood vessel segmentation in retinal imaging.

Precise retinal vessel segmentation techniques are crucial for computer-aided clinical diagnosis. Recent advancements in deep learning have considerab...

Exploring feature importance in machine learning for neuroimaging traits in Alzheimer's disease across a multiethnic cohort.

BackgroundAlzheimer's disease (AD) affects 55 million people worldwide, projected to reach 139 million by 2050; yet, most machine learning (ML)-based ...

Enhancing Early Detection of Contralateral Breast Cancer in Breast Cancer Survivors Using AI-Assisted Mammography.

BACKGROUND: Women with a history of breast cancer face an elevated risk of developing contralateral breast cancer (CBC). Although annual mammographic ...

Development trajectory and trends of ultrasound biomicroscopy in glaucoma research: a comprehensive 20-year bibliometric analysis.

PURPOSE: This study conducts a comprehensive bibliometric analysis regarding the application of ultrasound biomicroscopy in glaucoma research over the...

Artificial Intelligence and Machine Learning Applications in Fibromuscular Dysplasia: Transforming Diagnosis, Risk Stratification, and Clinical Decision-Making.

Fibromuscular dysplasia (FMD) is a non-atherosclerotic vascular disorder with heterogeneous presentations, making diagnosis and management highly depe...

The effect of neoadjuvant therapy on radiological, surgical, and pathological results in nonmetastatic breast cancer: A retrospective observational study.

The aim of the study was to evaluate the concordance between radiological imaging modalities and pathological findings and to test whether neoadjuvant...

Radiomics-based ultrasOund Model for differentiating Uterine Sarcomas from leiomyomas (ROMUS): a retrospective pilot Multicenter Italian Trials in Ovarian Cancer (MITO) study.

OBJECTIVE: To develop machine-learning models that incorporate clinical information and radiomics features extracted from ultrasound images to disting...

Reimagining Thomas Lewis's perspective: using artificial intelligence tools to predict cardiovascular risk from computed tomography.

Artificial intelligence (AI) is reshaping cardiovascular imaging, transforming it from a set of diagnostic tests into powerful tools of precision medi...

3D Spatiotemporal cardiac reconstruction for predicting MACE in acute myocardial infarction.

Artificial intelligence has made significant strides in predicting major adverse cardiovascular events (MACE) in patients with acute myocardial infarc...

Percutaneous nephrostomy guidance by a convolutional-neural-network-based optical coherence tomography endoscope.

Percutaneous nephrostomy is widely used in kidney access surgeries. Despite its prevalence in urological interventions, it presents two operational ch...

Deep Learning-Derived Body Composition Analysis Predicts Long-Term Mortality After Transcatheter Aortic Valve Replacement.

OBJECTIVE: To examine the association between body composition metrics derived from preprocedural computed tomography (CT) angiography and all-cause m...