Radiology

Deep learning-based myocardial infarction detection using echocardiography.

Myocardial infarction (MI) is a life-threatening condition caused by reduced oxygen supply to the heart muscle due to blockage of the coronary arteries. Delayed or missed diagnosis increases the risk of mortality and heart failure, making early detection critical. Echocardiography is a non-invasive imaging technique that uses real-time ultrasound to examine the heart's structure and function, incl...

Preoperative prediction of thymoma risk classification with machine learning-based computed tomography radiomics features.

BACKGROUND: Thymomas are primarily assessed using chest computed tomography (CT); however, this method has inherent limitations such as an overlap between the semantic features of thymomas presenting as localized lesions and other histological subtypes. Therefore, this study aimed to develop a combined radiomics model, based on non-contrast CT, and investigate its potential clinical utility in pre...

Denoising of 4D dynamic PET images using spatiotemporal regularization with integrated temporal restoration (SPRINTER).

PURPOSE: High-quality 4D dynamic PET imaging is often compromised by noise, especially in low-count frames, which limits clinical utility and quantita...

Airway coach project: development of a machine learning-based model using clinical and ultrasound parameters to support videolaryngoscopy strategy.

BACKGROUND: Videolaryngoscopy (VL) is recommended as a first-line technique for tracheal intubation; however, existing airway assessment tools-largely...

Machine learning to improve the prediction of Large for Gestational Age (LGA) neonates: a cohort study.

BACKGROUND: Prediction of Large for Gestational Age (LGA) risk is important as it can enable earlier, more effective interventions, and avoid or mitig...

Differentiating sub-centimeter lung metastases in colorectal cancer by deep learning: a multicenter retrospective study.

BACKGROUND: Early diagnosis of sub-centimeter lung metastases is critical for timely decision-making and improved prognosis in patients with colorecta...

Reconstructing shared visual experiences from human brain activity across individuals.

Reconstructing visual experiences from brain activity promises to strengthen brain-computer interfaces and our fundamental understanding of perception...

Global gender representation in neurointerventional surgery: a natural language processing analysis of journal publications.

BACKGROUND: Despite increased awareness of diversity and inclusion in neurointerventional surgery, the representation of women in neurointerventional ...

Efficacy of a Voice Biomarker for the Prediction of Heart Failure Hospitalisation: Results from the Telemedical Interventional Management in Heart Failure III (TIM-HF3) Voice Study.

Interpretable Multimodal AI to Predict the Presence of Late Gadolinium Enhancement on Cardiac Magnetic Resonance Imaging in Cardiac Sarcoidosis Patients.

Generating Synthetic MR Perfusion Maps From DWI and FLAIR in Acute Ischemic Stroke: Development and External Validation of a Deep Learning Model.

BACKGROUND: Magnetic resonance imaging (MRI) is critical for acute stroke triage, but it is time-consuming and often requires contrast injection for p...

RTsDEN: reverse task attention enabled deep learning model for lung cancer detection using computed tomography.

BACKGROUND: Lung cancer remains the most significant cause of cancer-related death worldwide due to the critical challenges in diagnosis. Despite the ...

Location of AI risk markers and associated mammographic features in screening mammograms obtained years before screen-detected breast cancer.

OBJECTIVES: To examine screening mammograms assigned high-risk scores by two artificial intelligence (AI) models, 2 and 4 years prior to screen-detect...

Neuroimmune mechanisms in PTSD: inflammatory heterogeneity, circuit dysfunction, and biomarker-based subtypes.

Post-traumatic stress disorder (PTSD) is characterized by exaggerated fear response, anxiety, hyperarousal and sleep disturbances. One of the major pa...

Radiomics-enhanced 18F-AV45 PET/MRI for integrative assessment and centiloid estimation of amyloid-β burden in Alzheimer's disease.

OBJECTIVE: Reliable assessment of cerebral amyloid-β (Aβ) deposition is essential for the diagnosis and management of Alzheimer's disease (AD). This s...

An evolutionary neural architecture search for magnetic resonance image reconstructions.

PURPOSE: Deep learning has emerged as a promising approach for magnetic resonance imaging (MRI) reconstruction from undersampled data. However, manual...

Diagnostic performance of commercial AI systems versus participating radiologists for pulmonary nodule detection in routine clinical practice.

PURPOSE: To evaluate the diagnostic performance of two commercial artificial intelligence (AI) systems versus that of radiologists on routine clinical...

A Shepard wide residual network for fast MR image reconstruction on Undersampled k-space data.

Fast Magnetic Resonance Imaging (MRI) reconstruction on undersampled k-space data accelerates MRI scans while maintaining image quality. This enables ...

Wearable sensor technology and machine learning for anterior cruciate ligament injury diagnosis: an exploratory feasibility study.

OBJECTIVES: To evaluate the feasibility of using wearable inertial measurement units (IMUs; small body-worn sensors that capture linear acceleration a...

Evaluation of Baseline and Interim-Therapy PET Features for Prognostication in High-Risk Pediatric Hodgkin Lymphoma: A Retrospective Analysis of the AHOD1331 Trial.

This study aimed to evaluate the prognostic value of conventional and advanced PET metrics for predicting progression-free survival in high-risk pedia...