Radiology

Latest AI and machine learning research in radiology for healthcare professionals.

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Generation of deep learning based virtual non-contrast CT using dual-layer dual-energy CT and its application to planning CT for radiotherapy.

This paper presents a novel approach for generating virtual non-contrast planning computed tomograph...

Leveraging domain knowledge for synthetic ultrasound image generation: a novel approach to rare disease AI detection.

PURPOSE: This study explores the use of deep generative models to create synthetic ultrasound images...

Machine learning-based prognostic modeling in gallbladder cancer using clinical data and pre-treatment [F]-FDG-PET-radiomic features.

OBJECTIVES: This study evaluates the effectiveness of machine learning (ML) models that incorporate ...

Detecting the articular disk in magnetic resonance images of the temporomandibular joint using YOLO series.

The purpose of this study was to construct an artificial intelligence object detection model to dete...

Performance of Convolutional Neural Network Models in Meningioma Segmentation in Magnetic Resonance Imaging: A Systematic Review and Meta-Analysis.

BACKGROUND: Meningioma, the most common primary brain tumor, presents significant challenges in MRI-...

Deep learning radiomics on grayscale ultrasound images assists in diagnosing benign and malignant of BI-RADS 4 lesions.

This study aimed to explore a deep learning radiomics (DLR) model based on grayscale ultrasound imag...

SHIVA-CMB: a deep-learning-based robust cerebral microbleed segmentation tool trained on multi-source T2*GRE- and susceptibility-weighted MRI.

Cerebral microbleeds (CMB) represent a feature of cerebral small vessel disease (cSVD), a prominent ...

A proficient approach for the classification of Alzheimer's disease using a hybridization of machine learning and deep learning.

Alzheimer's disease (AD) is a neurodegenerative disorder. It causes progressive degeneration of the ...

Multi-Energy Evaluation of Image Quality in Spectral CT Pulmonary Angiography Using Different Strength Deep Learning Spectral Reconstructions.

RATIONALE AND OBJECTIVES: To evaluate and compare image quality of different energy levels of virtua...

Role of Artificial Intelligence for Endoscopic Ultrasound.

Endoscopic ultrasound (EUS) is widely used for the diagnosis of biliopancreatic and gastrointestinal...

Overfit detection method for deep neural networks trained to beamform ultrasound images.

Deep neural networks (DNNs) have remarkable potential to reconstruct ultrasound images. However, thi...

Descriptive overview of AI applications in x-ray imaging and radiotherapy.

Artificial intelligence (AI) is transforming medical radiation applications by handling complex data...

Recent Advances and Future Directions in Sonodynamic Therapy for Cancer Treatment.

Deep-tissue solid cancer treatment has a poor prognosis, resulting in a very low 5-year patient surv...

Role of artificial intelligence in magnetic resonance imaging-based detection of temporomandibular joint disorder: a systematic review.

This systematic review aimed to evaluate the application of artificial intelligence (AI) in the iden...

Latent representation learning for classification of the Doppler ultrasound images.

The classification of Doppler ultrasound images plays an important role in the diagnosis of pregnanc...

Diagnosis of intracranial aneurysms by computed tomography angiography using deep learning-based detection and segmentation.

BACKGROUND: Detecting and segmenting intracranial aneurysms (IAs) from angiographic images is a labo...

A wrapper method for finding optimal subset of multimodal Magnetic Resonance Imaging sequences for ischemic stroke lesion segmentation.

Multimodal data, while being information-rich, contains complementary as well as redundant informati...

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