Radiology

Development of Convolutional Neural Network to Segment Ultrasound Images of Histotripsy Ablation.

OBJECTIVE: Histotripsy is a focused ultrasound therapy that ablates tissue via the action of bubble ...

Deep-learning model for background parenchymal enhancement classification in contrast-enhanced mammography.

Breast background parenchymal enhancement (BPE) is correlated with the risk of breast cancer. BPE le...

Performance Metrics, Algorithms, and Applications of Artificial Intelligence in Vascular and Interventional Neurology: A Review of Basic Elements.

Artificial intelligence (AI) is currently being used as a routine tool for day-to-day activity. Medi...

Artificial intelligence for gastric cancer in endoscopy: From diagnostic reasoning to market.

Recognition of gastric conditions during endoscopy exams, including gastric cancer, usually requires...

Three-dimensional magnetic resonance imaging-based statistical shape analysis and machine learning-based prediction of patellofemoral instability.

This study performed three-dimensional (3D) magnetic resonance imaging (MRI)-based statistical shape...

Investigating the discrimination ability of 3D convolutional neural networks applied to altered brain MRI parametric maps.

Convolutional neural networks (CNNs) are gradually being recognized in the neuroimaging community as...

Role of artificial intelligence in brain tumour imaging.

Artificial intelligence (AI) is a rapidly evolving field with many neuro-oncology applications. In t...

Multimodality quantitative ultrasound envelope statistics imaging based support vector machines for characterizing tissue scatterer distribution patterns: Methods and application in detecting microwave-induced thermal lesions.

Ultrasound envelope statistics imaging, including ultrasound Nakagami imaging, homodyned-K imaging, ...

Deep learning reconstruction computed tomography with low-dose imaging.

A multicentric study of radiomics and artificial intelligence analysis on contrast-enhanced mammography to identify different histotypes of breast cancer.

OBJECTIVE: To evaluate the performance of radiomic analysis on contrast-enhanced mammography images ...

MRI reconstruction with enhanced self-similarity using graph convolutional network.

BACKGROUND: Recent Convolutional Neural Networks (CNNs) perform low-error reconstruction in fast Mag...

A retrieval-augmented chatbot based on GPT-4 provides appropriate differential diagnosis in gastrointestinal radiology: a proof of concept study.

BACKGROUND: We investigated the potential of an imaging-aware GPT-4-based chatbot in providing diagn...

Generative artificial intelligence in ophthalmology.

Generative artificial intelligence (AI) has revolutionized medicine over the past several years. A g...

Low tube voltage and deep-learning reconstruction for reducing radiation and contrast medium doses in thin-slice abdominal CT: a prospective clinical trial.

OBJECTIVES: To investigate the feasibility of low-radiation dose and low iodinated contrast medium (...

Effectiveness of deep learning-based reconstruction for improvement of image quality and liver tumor detectability in the hepatobiliary phase of gadoxetic acid-enhanced magnetic resonance imaging.

PURPOSE: To evaluate the effectiveness of deep learning-based reconstruction (DLR) in improving imag...

Combined radiomics nomogram of different machine learning models for preoperative distinguishing intraspinal schwannomas and meningiomas: a multicenter and comparative study.

AIMS: The objective of our study was to establish and verify a novel combined model based on multipa...

Deep learning-based platform performs high detection sensitivity of intracranial aneurysms in 3D brain TOF-MRA: An external clinical validation study.

PURPOSE: To evaluate the diagnostic efficacy of a developed artificial intelligence (AI) platform in...

Automated Analysis of Split Kidney Function from CT Scans Using Deep Learning and Delta Radiomics.

Differential kidney function assessment is an important part of preoperative evaluation of various ...

New Diagnostic Tools for Pulmonary Embolism Detection.

The presentation of pulmonary embolism (PE) varies from asymptomatic to life-threatening, and manage...

Value of vendor-agnostic deep learning image denoising in brain computed tomography: A multi-scanner study.

To evaluate the effect of a vendor-agnostic deep learning denoising (DLD) algorithm on diagnostic im...

Deep Learning Model for Predicting Proliferative Hepatocellular Carcinoma Using Dynamic Contrast-Enhanced MRI: Implications for Early Recurrence Prediction Following Radical Resection.

RATIONALE AND OBJECTIVES: The proliferative nature of hepatocellular carcinoma (HCC) is closely rela...