Radiology

Combining artificial intelligence analysis with expert mammogram reading: Determining the optimal AI positivity cut-off point for the French population-based breast cancer screening program.

PURPOSE: Artificial intelligence (AI) is increasingly explored as a complement to radiologists in population-based breast cancer screening, yet optimal strategies for its integration remain unclear. The French program relies on systematic double reading, a model challenged by rising workload and radiologist shortages. This study evaluates the performance of an AI algorithm and examines its potenti...

Diagnostic Performance of Artificial Intelligence in Salivary Gland Tumors Using Ultrasound Imaging: A Systematic Review.

BackgroundSalivary gland tumors are heterogeneous, making diagnosis challenging. Artificial intelligence (AI) is a potential adjunct in diagnosis, though its performance in ultrasound-based evaluation of salivary gland tumors is not clear.MethodsPublication databases were searched from inception to August 2025. Eligible studies applied AI techniques to ultrasound for salivary gland tumors and repo...

High-risk carotid plaque detection using a novel radio-morphometric analysis of the carotid bifurcation.

Current surgical eligibility criteria for the treatment of carotid artery disease, primary relying on stenosis and image plaque characteristics, are s...

Radiological Assessment of Liver and Biliary Neoplasms.

Primary liver cancer, encompassing hepatocellular carcinoma and cholangiocarcinoma, represents a growing global health burden with significant morbidi...

A multimodal epilepsy dataset of paired 3-Tesla and 7-Tesla MRI and intracranial EEG.

There is an increasing need to integrate multimodal datasets in epilepsy research, particularly to correlate electrophysiology with imaging in patient...

Cross-modal attentive fusion network for tri-modal lesion growth prediction.

Pre-trained LSTM-RNN models with linear kernels fail to capture irregular, non-linear lesion growth patterns. In this paper presents a new deep learni...

LAM-CATNet: lambda-aware multi-scale cross-attention swin transformer network for mammogram classification.

The fundamental requirement for the initial recognition of a mammogram is the accurate segmentation and classification of breast lesions in mammograms...

Concordance-Based Validation of Electronic Health Records and Modality Log Files to Improve MRI Exam Duration Prediction and Scheduling Performance.

Integrating multi-source healthcare data for predictive modeling requires rigorous data quality validation, yet concordance between data systems is ra...

Differential association of psoas and paraspinal muscle volumes with appendicular lean mass index: AI-derived lumbar computed tomography three-dimensional volumetric indices in older Korean Women: an observational study.

STUDY DESIGN: Retrospective cross-sectional study. PURPOSE: To investigate the relationship between sarcopenia-related markers and artificial intellig...

Can AI applied on MRI reliably predict shunt response in INPH? A comprehensive exploration of deep learning and radiomics approaches using preoperative MRI.

OBJECTIVE: Idiopathic normal pressure hydrocephalus (INPH) is a treatable neurological condition, yet predicting which patients will benefit from a ce...

MicroKAN: Mapping Human Brain Microstructure Using Diffusion MRI and Adaptive Nonlinear Modeling.

Diffusion magnetic resonance imaging (dMRI) provides powerful insights into brain microstructure, but conventional microstructural modeling methods re...

A tissue-informed deep learning-based method for positron range correction in preclinical [Formula: see text]Ga PET imaging.

PURPOSE: Positron range (PR) limits spatial resolution and quantitative accuracy in PET imaging, particularly for high-energy positron-emitting radion...

CT ASSESSMENT OF BODY COMPOSITION AND LUNG EVOLUTION IN CYSTIC FIBROSIS AFTER ELEXACAFTOR-TEZACAFTOR-IVACAFTOR: A COHORT STUDY.

BACKGROUND: Elexacaftor-Tezacaftor-Ivacaftor (ETI) therapy markedly improves pulmonary function in people with cystic fibrosis (pwCF) but induces weig...

Deep learning reconstruction improves appendix visualization on pediatric magnetic resonance imaging (MRI): a single-center experience.

BACKGROUND: Thin-slice MRI may improve appendix visualization in children with suspected appendicitis, but reducing slice thickness decreases signal-t...

Integrated CT-PET radiogenomics and graph-based multi-task learning for preoperative prediction of key glioma molecular markers.

BACKGROUND: The clinical management of glioma is increasingly dependent on the tumor's molecular profile, particularly the mutation status of Isocitra...

A simple morphometric algorithm based on nuclear atypia features of invasive breast carcinoma: relationship with nuclear grade, Ki-67 level, and hormone receptor status.

Background With the widespread availability of whole-slide imaging, many studies have utilized digital images of hematoxylin and eosin (H&E)-stained b...

Diagnostic accuracy of AI-Based models for pulmonary edema detection: A systematic review and Meta-Analysis.

BACKGROUND: Pulmonary edema is a life-threatening condition caused by fluid accumulation in the lungs that impairs gas exchange. Machine learning mode...

A Coarse-to-Fine DoubleUNet Framework with Synergistic Loss for Accurate Fetal Head Circumference Measurement.

OBJECTIVE: Fetal head circumference (HC) measurement is a routine and indispensable examination during pregnancy, closely associated with fetal health...

Proteomic clocks combined with deep learning phenotypes track eye aging and diseases.

Proteomics represents a powerful but underutilized approach for characterizing eye aging. Here, leveraging data from three large-scale, cross-national...

Ultrasound-based multiregional radiomics nomogram for predicting recurrence in HER2-positive breast cancer.

PURPOSE: Accurate prediction of recurrence risk is crucial for personalizing therapy in human epidermal growth factor receptor 2-positive (HER2-positi...