Radiology

Determination of spectroscopy marker of atherosclerotic carotid stenosis using FTIR-ATR combined with machine learning and chemometrics analyses.

Atherosclerotic carotid stenosis (ACS) is a recognized risk factor for ischemic stroke. Currently, the gold diagnostic standard is Doppler ultrasound, the results of which do not provide certainty whether a given person should be qualified for surgery or not, because in some patients, carotid artery stenosis, for example at the level of 70 %, does not cause ischemic stroke in others yes. Therefore...

Using Natural Language Processing (GPT-4) for Computed Tomography Image Analysis of Cerebral Hemorrhages in Radiology: Retrospective Analysis.

BACKGROUND: Cerebral hemorrhage is a critical medical condition that necessitates a rapid and precise diagnosis for timely medical intervention, including emergency operation. Computed tomography (CT) is essential for identifying cerebral hemorrhage, but its effectiveness is limited by the availability of experienced radiologists, especially in resource-constrained regions or when shorthanded duri...

Cross-site Validation of AI Segmentation and Harmonization in Breast MRI.

This work aims to perform a cross-site validation of automated segmentation for breast cancers in MRI and to compare the performance to radiologists. ...

Automatic Segmentation of Ultrasound-Guided Quadratus Lumborum Blocks Based on Artificial Intelligence.

Ultrasound-guided quadratus lumborum block (QLB) technology has become a widely used perioperative analgesia method during abdominal and pelvic surger...

A Large Language Model to Detect Negated Expressions in Radiology Reports.

Natural language processing (NLP) is crucial to extract information accurately from unstructured text to provide insights for clinical decision-making...

Raman fiber-optic probe for rapid diagnosis of gastric and esophageal tumors with machine learning analysis or similarity assessments: a comparative study.

Gastric and esophageal cancers, the predominant forms of upper gastrointestinal malignancies, contribute significantly to global cancer mortality. Rou...

MRI-based deep learning for differentiating between bipolar and major depressive disorders.

Mood disorders, particularly bipolar disorder (BD) and major depressive disorder (MDD), manifest changes in brain structure that can be detected using...

Radiomics prediction models of left atrial appendage hypercoagulability based on machine learning algorithms: an exploration about cardiac computed tomography angiography imaging.

Transesophageal echocardiography (TEE) is the standard method for diagnosing left atrial appendage (LAA) hypercoagulability in patients with atrial fi...

Generative AI and large language models in nuclear medicine: current status and future prospects.

This review explores the potential applications of Large Language Models (LLMs) in nuclear medicine, especially nuclear medicine examinations such as ...

Development and validation of a machine learning model to predict myocardial blood flow and clinical outcomes from patients' electrocardiograms.

We develop a machine learning (ML) model using electrocardiography (ECG) to predict myocardial blood flow reserve (MFR) and assess its prognostic valu...

Pretreatment CT-based machine learning radiomics model predicts response in unresectable hepatocellular carcinoma treated with lenvatinib plus PD-1 inhibitors and interventional therapy.

Magnetically Actuated Soft Microrobot with Environmental Adaptative Multimodal Locomotion Towards Targeted Delivery.

The development of environmentally adaptive solutions for magnetically actuated microrobots to enable targeted delivery in complex and confined fluid ...

3-1-3 Weight averaging technique-based performance evaluation of deep neural networks for Alzheimer's disease detection using structural MRI.

Alzheimer's disease (AD) is a progressive neurological disorder. It is identified by the gradual shrinkage of the brain and the loss of brain cells. T...

Uncertainty Estimation for Dual View X-ray Mammographic Image Registration Using Deep Ensembles.

Techniques are developed for generating uncertainty estimates for convolutional neural network (CNN)-based methods for registering the locations of le...

A Robust Deep Learning Method with Uncertainty Estimation for the Pathological Classification of Renal Cell Carcinoma Based on CT Images.

This study developed and validated a deep learning-based diagnostic model with uncertainty estimation to aid radiologists in the preoperative differen...

Machine Learning-Enabled Fuhrman Grade in Clear-cell Renal Carcinoma Prediction Using Two-dimensional Ultrasound Images.

OBJECTIVE: Accurate assessment of Fuhrman grade is crucial for optimal clinical management and personalized treatment strategies in patients with clea...

Deep learning enables accurate brain tissue microstructure analysis based on clinically feasible diffusion magnetic resonance imaging.

Diffusion magnetic resonance imaging (dMRI) allows non-invasive assessment of brain tissue microstructure. Current model-based tissue microstructure r...

Dynamic MRI interpolation in temporal direction using an unsupervised generative model.

PURPOSE: Cardiac cine magnetic resonance imaging (MRI) is an important tool in assessing dynamic heart function. However, this technique requires long...

Exploration of fractional flow reservation score based on artificial intelligence post-processing for coronary artery lesions in patients with diabetes and coronary heart disease.

In order to evaluate the relationship between coronary heart disease (CHD) and fractional flow reservation (FFR) in patients with different levels of ...

Comparative evaluation of machine learning models in predicting overall survival for nasopharyngeal carcinoma using F-FDG PET-CT parameters.

PURPOSE: The objective of this study is to assess the prognostic efficacy of F-fluorodeoxyglucose (F-FDG) positron emission tomography/computed tomogr...