Radiology

Multimodal GPT model for assisting thyroid nodule diagnosis and management.

Although using artificial intelligence (AI) to analyze ultrasound images is a promising approach to assessing thyroid nodule risks, traditional AI models lack transparency and interpretability. We developed a multimodal generative pre-trained transformer for thyroid nodules (ThyGPT), aiming to provide a transparent and interpretable AI copilot model for thyroid nodule risk assessment and managemen...

Prediction of anthropogenic I in the South China Sea based on machine learning.

With the rapid increase in the number of nuclear power plants along the China coast and the potential for releases of radioactive substances to marine ecosystems, it is important to investigate and predict the dispersion of radionuclides in the seas and assess their radiological risks. Due to iodine's high solubility in water, and the high fission yield and long half life of I, it has been widely ...

Automatic ultrasound image alignment for diagnosis of pediatric distal forearm fractures.

PURPOSE: The study aims to develop an automatic method to align ultrasound images of the distal forearm for diagnosing pediatric fractures. This appro...

A systematic review on deep learning-enabled coronary CT angiography for plaque and stenosis quantification and cardiac risk prediction.

BACKGROUND: Coronary artery disease (CAD) is a major worldwide health concern, contributing significantly to the global burden of cardiovascular disea...

Development and Evaluation of Automated Artificial Intelligence-Based Brain Tumor Response Assessment in Patients with Glioblastoma.

This project aimed to develop and evaluate an automated, AI-based, volumetric brain tumor MRI response assessment algorithm on a large cohort of patie...

Visualizing functional network connectivity differences using an explainable machine-learning method.

. Functional network connectivity (FNC) estimated from resting-state functional magnetic resonance imaging showed great information about the neural m...

Unsupervised Domain Adaptation for Low-Dose CT Reconstruction via Bayesian Uncertainty Alignment.

Low-dose computed tomography (LDCT) image reconstruction techniques can reduce patient radiation exposure while maintaining acceptable imaging quality...

Spatiotemporal Implicit Neural Representation for Unsupervised Dynamic MRI Reconstruction.

Supervised Deep-Learning (DL)-based reconstruction algorithms have shown state-of-the-art results for highly-undersampled dynamic Magnetic Resonance I...

High-Resolution Maps of Left Atrial Displacements and Strains Estimated With 3D Cine MRI Using Online Learning Neural Networks.

The functional analysis of the left atrium (LA) is important for evaluating cardiac health and understanding diseases like atrial fibrillation. Cine M...

Deep learning model for predicting the RAS oncogene status in colorectal cancer liver metastases.

BACKGROUND: To develop a deep learning radiomics (DLR) model based on contrast-enhanced computed tomography (CECT) to assess the rat sarcoma (RAS) onc...

Electrode Arrays for Detecting and Modulating Deep Brain Neural Information in Primates: A Review.

Primates possess a more developed central nervous system and a higher level of intelligence than rodents. Detecting and modulating deep brain activity...

The Use of Maximum-Intensity Projections and Deep Learning Adds Value to the Fully Automatic Segmentation of Lesions Avid for [F]FDG and [Ga]Ga-PSMA in PET/CT.

This study investigated the added value of using maximum-intensity projection (MIP) images for fully automatic segmentation of lesions using deep lear...

Characterization of Effective Half-Life for Instant Single-Time-Point Dosimetry Using Machine Learning.

Single-time-point (STP) image-based dosimetry offers a more convenient approach for clinical practice in radiopharmaceutical therapy (RPT) compared wi...

Artifact estimation network for MR images: effectiveness of batch normalization and dropout layers.

BACKGROUND: Magnetic resonance imaging (MRI) is an essential tool for medical diagnosis. However, artifacts may degrade images obtained through MRI, e...

Performance of radiomics analysis in ultrasound imaging for differentiating benign from malignant adnexal masses: A systematic review and meta-analysis.

INTRODUCTION: We present the state of the art of ultrasound-based machine learning (ML) radiomics models in the context of ovarian masses and analyze ...

Artificial Intelligence in Prostate Cancer Diagnosis on Magnetic Resonance Imaging: Time for a New PARADIGM.

Artificial intelligence (AI) may provide a solution for improving access to expert, timely, and accurate magnetic resonance imaging (MRI) interpretati...

Self-supervised learning for label-free segmentation in cardiac ultrasound.

Segmentation and measurement of cardiac chambers from ultrasound is critical, but laborious and poorly reproducible. Neural networks can assist, but s...

Effective reduction of unnecessary biopsies through a deep-learning-assisted aggressive prostate cancer detector.

Despite being one of the most prevalent cancers, prostate cancer (PCa) shows a significantly high survival rate, provided there is timely detection an...

Deep learning-based classification of coronary arteries and left ventricle using multimodal data for autonomous protocol selection or adjustment in angiography.

Optimal selection of X-ray imaging parameters is crucial in coronary angiography and structural cardiac procedures to ensure optimal image quality and...

Performance of AI Approaches for COVID-19 Diagnosis Using Chest CT Scans: The Impact of Architecture and Dataset.

AI is emerging as a promising tool for diagnosing COVID-19 based on chest CT scans. The aim of this study was the comparison of AI models for COVID-19...