Radiology

Machine Learning Radiomics-Based Prediction of Non-sentinel Lymph Node Metastasis in Chinese Breast Cancer Patients with 1-2 Positive Sentinel Lymph Nodes: A Multicenter Study.

RATIONALE AND OBJECTIVES: This study aimed to construct a machine learning radiomics-based model usi...

From technical to understandable: Artificial Intelligence Large Language Models improve the readability of knee radiology reports.

PURPOSE: The purpose of this study was to evaluate the effectiveness of an Artificial Intelligence-L...

US2Mask: Image-to-mask generation learning via a conditional GAN for cardiac ultrasound image segmentation.

Cardiac ultrasound (US) image segmentation is vital for evaluating clinical indices, but it often de...

Checklist for Reproducibility of Deep Learning in Medical Imaging.

The application of deep learning (DL) in medicine introduces transformative tools with the potential...

Evaluating ChatGPT's accuracy in providing screening mammography recommendations among older women: Artificial Intelligence and cancer communication.

Artificial intelligence-based MRI radiomics and radiogenomics in glioma.

The specific genetic subtypes that gliomas exhibit result in variable clinical courses and the need ...

Image quality and metal artifact reduction in total hip arthroplasty CT: deep learning-based algorithm versus virtual monoenergetic imaging and orthopedic metal artifact reduction.

BACKGROUND: To compare image quality, metal artifacts, and diagnostic confidence of conventional com...

Suppressing HIFU interference in ultrasound images using 1D U-Net-based neural networks.

One big challenge with high-intensity focused ultrasound (HIFU) is that the intense acoustic interfe...

Artificial intelligence of imaging and clinical neurological data for predictive, preventive and personalized (P3) medicine for Parkinson Disease: The NeuroArtP3 protocol for a multi-center research study.

BACKGROUND: The burden of Parkinson Disease (PD) represents a key public health issue and it is esse...

Editorial Commentary: Evaluation for Cartilage Lesions on Magnetic Resonance Imaging Continues to Improve: Artificial Intelligence Applications May Result in Higher Sensitivity and Specificity.

Accurate detection of cartilage lesions of the knee is required to offer patient-specific care and c...

Quantitative measurement of the ureter on three-dimensional magnetic resonance urography images using deep learning.

BACKGROUND: Accurate measurement of ureteral diameters plays a pivotal role in diagnosing and monito...

Combination of DCE-MRI and NME-DWI via Deep Neural Network for Predicting Breast Cancer Molecular Subtypes.

BACKGROUND: To explore whether the combination of dynamic contrast-enhanced (DCE) magnetic resonance...

Cross-sectional angle prediction of lipid-rich and calcified tissue on computed tomography angiography images.

PURPOSE: The assessment of vulnerable plaque characteristics and distribution is important to strati...

Deep learning-based label-free imaging of lymphatics and aqueous veins in the eye using optical coherence tomography.

We demonstrate an adaptation of deep learning for label-free imaging of the micro-scale lymphatic ve...

Automatic thoracic aorta calcium quantification using deep learning in non-contrast ECG-gated CT images.

Thoracic aorta calcium (TAC) can be assessed from cardiac computed tomography (CT) studies to improv...

Fully Automated Identification of Lymph Node Metastases and Lymphovascular Invasion in Endometrial Cancer From Multi-Parametric MRI by Deep Learning.

BACKGROUND: Early and accurate identification of lymphatic node metastasis (LNM) and lymphatic vascu...

Deep-learning reconstructed lumbar spine 3D MRI for surgical planning: pedicle screw placement and geometric measurements compared to CT.

PURPOSE: To test equivalency of deep-learning 3D lumbar spine MRI with "CT-like" contrast to CT for ...

A review of ADHD detection studies with machine learning methods using rsfMRI data.

Attention deficit hyperactivity disorder (ADHD) is a common mental health condition that significant...

Detection of urinary tract stones on submillisievert abdominopelvic CT imaging with deep-learning image reconstruction algorithm (DLIR).

PURPOSE: Urolithiasis is a chronic condition that leads to repeated CT scans throughout the patient'...

Radioport: a radiomics-reporting network for interpretable deep learning in BI-RADS classification of mammographic calcification.

Generally, due to a lack of explainability, radiomics based on deep learning has been perceived as a...

Automatic segmentation of hepatocellular carcinoma on dynamic contrast-enhanced MRI based on deep learning.

. Precise hepatocellular carcinoma (HCC) detection is crucial for clinical management. While studies...