Radiology

Cost-Sensitive Weighted Contrastive Learning Based on Graph Convolutional Networks for Imbalanced Alzheimer's Disease Staging.

Identifying the progression stages of Alzheimer's disease (AD) can be considered as an imbalanced multi-class classification problem in machine learning. It is challenging due to the class imbalance issue and the heterogeneity of the disease. Recently, graph convolutional networks (GCNs) have been successfully applied in AD classification. However, these works did not handle the class imbalance is...

Magnetic Resonance Electrical Properties Tomography Based on Modified Physics- Informed Neural Network and Multiconstraints.

This paper presents a novel method based on leveraging physics-informed neural networks for magnetic resonance electrical property tomography (MREPT). MREPT is a noninvasive technique that can retrieve the spatial distribution of electrical properties (EPs) of scanned tissues from measured transmit radiofrequency (RF) in magnetic resonance imaging (MRI) systems. The reconstruction of EP values in ...

Machine learning-assisted diagnosis of parotid tumor by using contrast-enhanced CT imaging features.

PURPOSE: This study aims to develop a machine learning diagnostic model for parotid gland tumors based on preoperative contrast-enhanced CT imaging fe...

Mammography classification with multi-view deep learning techniques: Investigating graph and transformer-based architectures.

The potential and promise of deep learning systems to provide an independent assessment and relieve radiologists' burden in screening mammography have...

High-quality expert annotations enhance artificial intelligence model accuracy for osteosarcoma X-ray diagnosis.

Primary malignant bone tumors, such as osteosarcoma, significantly affect the pediatric and young adult populations, necessitating early diagnosis for...

Assessment of multi-modal magnetic resonance imaging for glioma based on a deep learning reconstruction approach with the denoising method.

BACKGROUND: Deep learning reconstruction (DLR) with denoising has been reported as potentially improving the image quality of magnetic resonance imagi...

Preoperative prediction model of lymph node metastasis in the inguinal and femoral region based on radiomics and artificial intelligence.

OBJECTIVE: To predict preoperative inguinal lymph node metastasis in vulvar cancer patients using a machine learning model based on imaging features a...

Learning co-plane attention across MRI sequences for diagnosing twelve types of knee abnormalities.

Multi-sequence magnetic resonance imaging is crucial in accurately identifying knee abnormalities but requires substantial expertise from radiologists...

Impact of acquisition area on deep-learning-based glaucoma detection in different plexuses in OCTA.

Glaucoma is a group of neurodegenerative diseases that can lead to irreversible blindness. Yet, the progression can be slowed down if diagnosed and tr...

Magnetic Resonance-Guided Cancer Therapy Radiomics and Machine Learning Models for Response Prediction.

Magnetic resonance imaging (MRI) is known for its accurate soft tissue delineation of tumors and normal tissues. This development has significantly im...

Automated classification of mandibular canal in relation to third molar using CBCT images.

BACKGROUND: Dental radiology has significantly benefited from cone-beam computed tomography (CBCT) because of its compact size and low radiation expos...

Three contrasts in 3 min: Rapid, high-resolution, and bone-selective UTE MRI for craniofacial imaging with automated deep-learning skull segmentation.

PURPOSE: Ultrashort echo time (UTE) MRI can be a radiation-free alternative to CT for craniofacial imaging of pediatric patients. However, unlike CT, ...

Artificial intelligence-based extraction of quantitative ultra-widefield fluorescein angiography parameters in retinal vein occlusion.

OBJECTIVE: To examine the association between quantitative vascular parameters extracted from intravenous fluorescein angiography (IVFA) and baseline ...

Deep unfolding network with spatial alignment for multi-modal MRI reconstruction.

Multi-modal Magnetic Resonance Imaging (MRI) offers complementary diagnostic information, but some modalities are limited by the long scanning time. T...

Impact of artificial intelligence assisted compressed sensing technique on scan time and image quality in musculoskeletal MRI - A systematic review.

INTRODUCTION: Magnetic Resonance Imaging (MRI) has revolutionized the diagnosis and treatment of musculoskeletal disorders. Parallel imaging (PI) and ...

Can large language models be new supportive tools in coronary computed tomography angiography reporting?

The advent of large language models (LLMs) marks a transformative leap in natural language processing, offering unprecedented potential in radiology, ...

Deep learning for the harmonization of structural MRI scans: a survey.

Medical imaging datasets for research are frequently collected from multiple imaging centers using different scanners, protocols, and settings. These ...

Can deep learning classify cerebral ultrasound images for the detection of brain injury in very preterm infants?

OBJECTIVES: Cerebral ultrasound (CUS) is the main imaging screening tool in preterm infants. The aim of this work is to develop deep learning (DL) mod...

Assessing GPT-4 multimodal performance in radiological image analysis.

OBJECTIVES: This study aims to assess the performance of a multimodal artificial intelligence (AI) model capable of analyzing both images and textual ...

Valorization of tomato processing by-products: Predictive modeling and optimization for ultrasound-assisted lycopene extraction.

Lycopene is a carotenoid highly valuable to the food, pharmaceutical, dye, and cosmetic industries, present in ripe tomatoes and other fruits with a d...