Radiology

Detection of calcified plaques: comparison between coronary CT angiography and thin-slice non-contrast CT with deep learning-aided image registration.

OBJECTIVES: To investigate whether coronary CT angiography (CCTA) misses calcified plaques detected ...

Large language model-assisted radiology reporting in a single-radiologist implementation: a retrospective cohort study interpreted through a UTAUT lens.

BACKGROUND: Radiologist burnout affects approximately 40% of US radiologists. Large language models ...

Cerebral Vasospasm Detection and Delayed Cerebral Ischemia Prediction after Aneurysmal Subarachnoid Hemorrhage: A Scoping Review.

BACKGROUND/OBJECTIVE: Aneurysmal subarachnoid hemorrhage (aSAH) is complicated by angiographic cereb...

Interpretable predictions from whole-body FDG-PET/CT using parameters associated with clinical outcome.

BACKGROUND: Accurate prediction of clinical outcomes is challenging yet important for patient care. ...

A Graph Attention Network-Based Multimodal Auxiliary Intelligent Grading Model for Uterine Prolapse Severity.

INTRODUCTION AND HYPOTHESIS: Uterine prolapse affects women's quality of life. Traditional diagnosis...

Prospective biopsy-controlled validation of an AI model for predicting glioblastoma infiltration: Results from the SupraGlio trial.

BACKGROUND: Glioblastoma recurrence is driven by diffuse microscopic infiltration beyond the contras...

Machine learning models for predicting microvascular invasion in hepatocellular carcinoma with three-dimensional whole-lesion 18F-FDG PET radiomics.

OBJECTIVE: To estimate the performance of machine learning models based on preoperative three-dimens...

Artificial intelligence (AI)-assisted ultrasound in clinical trials: Endpoint automation, decentralized monitoring, and regulatory readiness.

Ultrasound is among the most widely used imaging modalities in clinical trials, and yet its dependen...

CDR-Net: A computerized framework to detect Alzheimer's diseases and mild cognitive impairment.

Alzheimer's disease (AD) and mild cognitive impairment (MCI) are two dementia-related brain illnesse...

GUIDE-US: grade-informed unpaired distillation of encoder knowledge from histopathology to micro-ultrasound.

PURPOSE: Non-invasive grading of prostate cancer (PCa) from micro-ultrasound (micro-US) could expedi...

Effective elimination of respiratory misregistration-induced attenuation correction errors in PET/CT via deep learning trained on data-driven gated PET from strictly respiratory-phase-matched PET/CT cases.

Clinical Machine Learning Model for Predicting Pathological Complete Response in Patients with Esophageal and Gastroesophageal Junction Adenocarcinoma After Trimodality Therapy.

BACKGROUND: Accurate prediction of pathological complete response (pCR) after preoperative chemoradi...

Use of Deep Learning Models in the Diagnosis of Proptosis Through Orbital Magnetic Resonance Imaging.

BACKGROUND Proptosis is a common manifestation of orbital disease; however, current diagnostic tools...

Mammography-based artificial intelligence model for predicting axillary lymph node status after neoadjuvant therapy in breast cancer.

OBJECTIVES: Our objective is to develop a deep learning-based artificial intelligence (AI) model cap...

Improved visualization of perivascular spaces on T2-weighted imaging with deep learning-based denoising and super-resolution reconstruction.

This prospective study evaluated the ability of a deep learning-based denoising followed by super-re...

3D Segmentation of Multi-contrast Cardiac Magnetic Resonances With Topological Correction and Synthetic Data Augmentation.

PURPOSE: Automatic segmentation of cardiac magnetic resonance (CMR) images improves the evaluation o...

[Exploration and clinical application of the "digital and intelligent surgery" diagnosis and treatment workflow for oral and maxillofacial tumors].

Tumors in the oral and maxillofacial region present significant clinical challenges due to anatomica...

Identifying neuroimaging-based biomarkers for obsessive-compulsive disorder: A resting-state fMRI and machine learning study in patients and unaffected first-degree relatives.

BACKGROUND: Identifying whether brain alterations in obsessive-compulsive disorder (OCD) represent t...

Use of Machine Learning to Identify Markers of Risk for Fragile X-Associated Tremor/Ataxia Syndrome: A Preliminary Analysis.

OBJECTIVE: The objective of this study was to examine whether machine learning has the capacity to p...

Construction of Rheumatoid Arthritis-Associated Interstitial Lung Disease diagnostic model and identification of biomarkers based on a multi-omics integration strategy of machine learning.

OBJECTIVE: This study aimed to develop and validate a machine learning model integrating multi-omics...

Evaluation of Brain Age on Magnetic Resonance Imaging Using Machine Learning Techniques in Patients With Hearing Loss.

OBJECTIVES: Hearing impairment is strongly linked to cognitive decline, with individuals having mild...