Radiology

Linking disease activity with optical coherence tomography angiography in neovascular age related macular degeneration using artificial intelligence.

To investigate quantitative associations between AI-assessed disease activity and optical coherence tomography angiography (OCTA)-derived parameters in patients with neovascular age-related macular degeneration (nAMD) undergoing anti-VEGF therapy. OCTA and SD-OCT images obtained from multicenter, randomized study data were evaluated. A deep learning algorithm (RetInSight) was used to detect and qu...

Machine learning model for non-alcoholic steatohepatitis diagnosis based on ultrasound radiomics.

BACKGROUND: Non-Alcoholic Steatohepatitis (NASH) is a crucial stage in the progression of Non-Alcoholic Fatty Liver Disease(NAFLD). The purpose of this study is to explore the clinical value of ultrasound features and radiological analysis in predicting the diagnosis of Non-Alcoholic Steatohepatitis.

Diagnosing Sarcopenia with AI-Aided Ultrasound (DINOSAUR)-A Pilot Study.

Sarcopenia has been recognized as a determining factor in surgical outcomes and is associated with an increased risk of postoperative complications a...

Large-scale pretrained frame generative model enables real-time low-dose DSA imaging: An AI system development and multi-center validation study.

BACKGROUND: Digital subtraction angiography (DSA) devices are commonly used in numerous interventional procedures across various parts of the body, ne...

Artificial intelligence-aided ultrasound imaging in hepatopancreatobiliary surgery: where are we now?

BACKGROUND: Artificial intelligence (AI) models have been applied in various medical imaging modalities and surgical disciplines, however the current ...

An end-to-end deep learning pipeline to derive blood input with partial volume corrections for automated parametric brain PET mapping.

Dynamic 2-[18F] fluoro-2-deoxy-D-glucose positron emission tomography (dFDG-PET) for human brain imaging has considerable clinical potential, yet its ...

Predicting the risk category of thymoma with machine learning-based computed tomography radiomics signatures and their between-imaging phase differences.

The aim of this study was to develop a medical imaging and comprehensive stacked learning-based method for predicting high- and low-risk thymoma. A to...

Deep learning based uterine fibroid detection in ultrasound images.

Uterine fibroids are common benign tumors originating from the uterus's smooth muscle layer, often leading to symptoms such as pelvic pain, and reprod...

Evolution of Research Reporting Standards: Adapting to the Influence of Artificial Intelligence, Statistics Software, and Writing Tools.

Reporting standards are essential to health research as they improve accuracy and transparency. Over time, significant changes have occurred to the re...

An optimized model based on adaptive convolutional neural network and grey wolf algorithm for breast cancer diagnosis.

Medical image classification (IC) is a method for categorizing images according to the appropriate pathological stage. It is a crucial stage in comput...

A prognostic model for thermal ablation of benign thyroid nodules based on interpretable machine learning.

INTRODUCTION: The detection rate of benign thyroid nodules is increasing every year, with some affected patients experiencing symptoms. Ultrasound-gui...

Machine learning models can define clinically relevant bone density subgroups based on patient-specific calibrated computed tomography scans in patients undergoing reverse shoulder arthroplasty.

BACKGROUND: Reduced bone density is recognized as a predictor for potential complications in reverse shoulder arthroplasty (RSA). While humeral and gl...

Altered dynamic large-scale brain networks and combined machine learning in primary angle-closure glaucoma.

Primary angle-closure glaucoma (PACG) is a severe and irreversible blinding eye disease characterized by progressive retinal ganglion cell death. Howe...

Predicting tremor improvement after MRgFUS thalamotomy in essential tremor from preoperative spontaneous brain activity: A machine learning approach.

Magnetic resonance-guided focused ultrasound surgery (MRgFUS) thalamotomy is an emerging technique for medication-refractory essential tremor (ET), bu...

Deep learning improves quality of intracranial vessel wall MRI for better characterization of potentially culprit plaques.

Intracranial vessel wall imaging (VWI), which requires both high spatial resolution and high signal-to-noise ratio (SNR), is an ideal candidate for de...

Predictive Study of Machine Learning-Based Multiparametric MRI Radiomics Nomogram for Perineural Invasion in Rectal Cancer: A Pilot Study.

This study aimed to establish and validate the efficacy of a nomogram model, synthesized through the integration of multi-parametric magnetic resonanc...

An accurate paradigm for denoising degraded ultrasound images based on artificial intelligence systems.

Ultrasound images are susceptible to various forms of quality degradation that negatively impact diagnosis. Common degradations include speckle noise,...

Effect of deep learning reconstruction on the assessment of pancreatic cystic lesions using computed tomography.

This study aimed to compare the image quality and detection performance of pancreatic cystic lesions between computed tomography (CT) images reconstru...

Improved Dementia Prediction in Cerebral Small Vessel Disease Using Deep Learning-Derived Diffusion Scalar Maps From T1.

BACKGROUND: Cerebral small vessel disease is the most common pathology underlying vascular dementia. In small vessel disease, diffusion tensor imaging...

Focal liver lesion diagnosis with deep learning and multistage CT imaging.

Diagnosing liver lesions is crucial for treatment choices and patient outcomes. This study develops an automatic diagnosis system for liver lesions us...