Radiology

Performance across different versions of an artificial intelligence model for screen-reading of mammograms.

OBJECTIVES: Studies have reported promising results regarding artificial intelligence (AI) as a tool for improved mammographic screening interpretive performance. We analyzed AI malignancy risk scores from two versions of the same commercial AI model. MATERIALS AND METHODS: This retrospective cohort study used data from 117,709 screening examinations performed in BreastScreen Norway 2009-2018. The...

From Recognition to Action: Integrating Deep Learning and Robotic Control in Transthoracic Echocardiography.

Population aging has driven a rise in heart failure cases, increasing the clinical burden on cardiac diagnostics. As a first-line imaging method, transthoracic echocardiography (TTE) faces limitations due to operator dependence, patient variability, and workflow inefficiencies. Meanwhile, advances in artificial intelligence (AI) and robotic ultrasound systems offer new potential pathways toward au...

Current treatment options for perinatal depression.

PURPOSE OF REVIEW: Perinatal depression (PND) affects up to one in five patients and is the leading cause of maternal mortality, yet remains underdiag...

Neural networks for faster laser ultrasound tomography in tissue phantoms.

Speed of sound (SoS) mapping provides quantitative and localised information about a material's acoustic properties, allowing identification of spatia...

Based on the umami-transformer machine learning model: Screening of umami peptides from ultrasound-assisted enzymatic hydrolysis of shiitake mushrooms and study on their flavor mechanism.

This study aimed to optimize ultrasound-assisted enzymatic hydrolysis for umami peptide preparation from shiitake mushrooms, develop a reliable predic...

Weight-bearing computed tomography volumetric analysis for diagnosis of syndesmotic instability: A new standard of care?

Assessing the severity and clinical impact of syndesmotic injury, especially when subtle, remains among the most diagnostically challenging conditions...

CT-free attenuation and scatter correction of [11C]CFT brain PET using a Bi-directional matching network.

Quantitative PET imaging requires accurate attenuation and scatter correction (ASC), but the standard CT-based method introduces additional radiation ...

Diagnostic Accuracy of Histopathologic Features Suggestive of Dermatophytosis: A Retrospective Cohort Selection Study With Machine Learning Evaluation.

BACKGROUND: Histopathologic features have been proposed as clues to potential dermatophyte infection. The inter-observer reproducibility and diagnosti...

Non-invasive Prediction of CYP11B2-Defined Subtypes in Primary Aldosteronism Using 18F-Pentixafor PET/CT and Machine Learning.

PURPOSE: This study aims to develop and validate an interpretable machine learning model that integrates clinical data, radiomics, and deep learning (...

Shap-interpretable predictive modeling of microvascular invasion and early recurrence in hepatocellular carcinoma using MRI habitat imaging combined with clinical features.

OBJECTIVE: To develop and validate an integrated model combining Gd-EOB-DTPA-enhanced MRI habitat imaging with clinical features for preoperative pred...

C2HFusion: Clinical context-driven hierarchical fusion of multimodal data for personalized and quantitative prognostic assessment in pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignancy. Accurate prognostic modeling enables reliable risk stratification to identi...

Exploring cognitive workload recognition using CogRepLKNet with EEG-fMRI.

Accurate multimodal Cognitive Workload Recognition (CWR) remains challenging due to the difficulty of modeling cross-modal relationships between Elect...

Artificial intelligence in breast cancer screening: A systematic review and meta-analysis of integration strategies.

OBJECTIVE: To compare AI-augmented and conventional double reading in organised breast-cancer screening with respect to cancer-detection rate (CDR), r...

Subregional Radiomics Analysis on Multiparametric MRI for Evaluating Lymphovascular Invasion and Survival in Gastric Cancer: A Multicenter Study.

BACKGROUND: Accurate preoperative assessment of lymphovascular invasion (LVI) remains challenging due to the high heterogeneity of gastric cancer (GC)...

External validation of an explainable electrocardiogram-only deep learning algorithm for the prediction of response after cardiac resynchronization therapy.

BACKGROUND: Cardiac resynchronization therapy (CRT) can improve clinical outcomes in patients with dyssynchronous heart failure, but many patients sel...

Utilizing artificial intelligence for the diagnosis of ocular surface squamous neoplasia with ultrasound biomicroscopy images.

PURPOSE: This study aims to develop an artificial intelligence (AI) model to assist ophthalmologists in distinguishing ocular surface squamous neoplas...

UTMorph: A hybrid CNN-transformer network for weakly-supervised multimodal image registration in biopsy puncture.

Accurate registration of preoperative magnetic resonance imaging (MRI) and intraoperative ultrasound (US) images is essential to enhance the precision...

Unlocking 2D/3D+T myocardial mechanics from cine MRI: a mechanically regularized space-time finite element correlation framework.

Accurate and biomechanically consistent quantification of cardiac motion remains a major challenge in cine MRI analysis. While classical feature-track...

An Ensemble Learning Artificial Intelligence Model for Alzheimer's Disease Detection Using OCT.

PURPOSE: There has been significant progress in detecting Alzheimer's disease (AD) using retinal imaging. We developed an ensemble learning-based deep...

Automating prostate biopsy guidance: A robust CNN approach for non-rigid 3D/3D MR-TRUS image registration.

Fusing transrectal ultrasound (TRUS) and magnetic resonance (MR) images has significantly improved the accuracy of prostate cancer detection during ta...