Radiology

A Generative Shape Compositional Framework to Synthesize Populations of Virtual Chimeras.

Generating virtual organ populations that capture sufficient variability while remaining plausible is essential to conduct in silico trials (ISTs) of medical devices. However, not all anatomical shapes of interest are always available for each individual in a population. The imaging examinations and modalities used can vary between subjects depending on their individualized clinical pathways. Diff...

Complex-Valued Convolutional Gated Recurrent Neural Network for Ultrasound Beamforming.

Ultrasound detection is a potent tool for the clinical diagnosis of various diseases due to its real-time, convenient, and noninvasive qualities. Yet, existing ultrasound beamforming and related methods face a big challenge to improve both the quality and speed of imaging for the required clinical applications. The most notable characteristic of ultrasound signal data is its spatial and temporal f...

GBCHV an advanced deep learning anatomy aware model for accurate classification of gallbladder cancer utilizing ultrasound images.

This study introduces a novel deep learning approach aimed at accurately classifying Gallbladder Cancer (GBC) into benign, malignant, and normal categ...

AutoDPS: An unsupervised diffusion model based method for multiple degradation removal in MRI.

BACKGROUND AND OBJECTIVE: Diffusion models have demonstrated their ability in image generation and solving inverse problems like restoration. Unlike m...

Living Microalgae-Based Magnetic Microrobots for Calcium Overload and Photodynamic Synergetic Cancer Therapy.

The combination of Ca overload and reactive oxygen species (ROS) production for cancer therapy offers a superior solution to the lack of specificity i...

T1-weighted MRI-based brain tumor classification using hybrid deep learning models.

Health is fundamental to human well-being, with brain health particularly critical for cognitive functions. Magnetic resonance imaging (MRI) serves as...

Prediction of contrast-associated acute kidney injury with machine-learning in patients undergoing contrast-enhanced computed tomography in emergency department.

Radiocontrast media is a major cause of nephrotoxic acute kidney injury(AKI). Contrast-enhanced CT(CE-CT) is commonly performed in emergency departmen...

Predicting the efficacy of neoadjuvant chemotherapy in breast cancer patients based on ultrasound longitudinal temporal depth network fusion model.

OBJECTIVE: The aim of this study was to develop and validate a deep learning radiomics (DLR) model based on longitudinal ultrasound data and clinical ...

Effectiveness of AI for Enhancing Computed Tomography Image Quality and Radiation Protection in Radiology: Systematic Review and Meta-Analysis.

BACKGROUND: Artificial intelligence (AI) presents a promising approach to balancing high image quality with reduced radiation exposure in computed tom...

Machine learning-driven ultrasound radiomics for assessing axillary lymph node burden in breast cancer.

OBJECTIVE: This study explores the value of combining intratumoral and peritumoral radiomics features from ultrasound imaging with clinical characteri...

Data-efficient generalization of AI transformers for noise reduction in ultra-fast lung PET scans.

PURPOSE: Respiratory motion during PET acquisition may produce lesion blurring. Ultra-fast 20-second breath-hold (U2BH) PET reduces respiratory motion...

Evaluation of MRI anatomy in machine learning predictive models to assess hydrogel spacer benefit for prostate cancer patients.

INTRODUCTION: Hydrogel spacers (HS) are designed to minimise the radiation doses to the rectum in prostate cancer radiation therapy (RT) by creating a...

Evaluation of AI-based nerve segmentation on ultrasound: relevance of standard metrics in the clinical setting.

BACKGROUND: In artificial intelligence for ultrasound-guided regional anaesthesia, accurate nerve identification is essential. The technology communit...

Post-Mortem imaging biobanks: Building data for reproducibility, standardization, and AI integration.

In recent years, post-mortem imaging has advanced with techniques such as Post-Mortem Computed Tomography (PMCT) and Post-Mortem Magnetic Resonance im...

Artificial intelligence in medical imaging: From task-specific models to large-scale foundation models.

Artificial intelligence (AI), particularly deep learning, has demonstrated remarkable performance in medical imaging across a variety of modalities, i...

Deep Learning for Lumbar Disc Herniation Diagnosis and Treatment Decision-Making Using Magnetic Resonance Imagings: A Retrospective Study.

BACKGROUND: Lumbar disc herniation (LDH) is a common cause of back and leg pain. Diagnosis relies on clinical history, physical exam, and imaging, wit...

The radiologist as an independent "third party" to the patient and clinicians in the era of generative AI.

Radiologists are crucial in the diagnostic workflow. They must maintain an independent perspective, being a "third party" to the patients and referral...

Deep-learning tool for early identification of non-traumatic intracranial hemorrhage etiology and application in clinical diagnostics based on computed tomography (CT) scans.

BACKGROUND: To develop an artificial intelligence system that can accurately identify acute non-traumatic intracranial hemorrhage (ICH) etiology (aneu...

AI in Breast Cancer Imaging: An Update and Future Trends.

Breast cancer is one of the most common types of cancer affecting women worldwide. Artificial intelligence (AI) is transforming breast cancer imaging ...

Impact of an artificial intelligence decision support system among radiologists with different levels of experience in breast ultrasound: A prospective study in a tertiary center.

PURPOSE: To assess the impact of an artificial intelligence decision support system (Koios DS) on the diagnostic performance of radiologists with diff...