Radiology

Prediction of testicular histology in azoospermia patients through deep learning-enabled two-dimensional grayscale ultrasound.

Testicular histology based on testicular biopsy is an important factor for determining appropriate testicular sperm extraction surgery and predicting sperm retrieval outcomes in patients with azoospermia. Therefore, we developed a deep learning (DL) model to establish the associations between testicular grayscale ultrasound images and testicular histology. We retrospectively included two-dimension...

Coronary artery disease detection using deep learning and ultrahigh-resolution photon-counting coronary CT angiography.

PURPOSE: The purpose of this study was to evaluate the diagnostic performance of automated deep learning in the detection of coronary artery disease (CAD) on photon-counting coronary CT angiography (PC-CCTA).

Clinical Impact of Radiologist's Alert System on Patient Care for High-risk Incidental CT Findings: A Machine Learning-Based Risk Factor Analysis.

RATIONALE AND OBJECTIVES: Efficient communication between radiologists and clinicians ordering computed tomography (CT) examinations is crucial for ma...

A novel mean shape based post-processing method for enhancing deep learning lower-limb muscle segmentation accuracy.

This study aims at improving the lower-limb muscle segmentation accuracy of deep learning approaches based on Magnetic Resonance Imaging (MRI) scans, ...

Beamforming-integrated neural networks for ultrasound imaging.

Sparse matrix beamforming (SMB) is a computationally efficient reformulation of delay-and-sum (DAS) beamforming as a single sparse matrix multiplicati...

Automated deep learning segmentation of cardiac inflammatory FDG PET.

BACKGROUND: Fluorodeoxyglucose positron emission tomography (FDG PET) with suppression of myocardial glucose utilization plays a pivotal role in diagn...

Bubble-Inspired Multifunctional Magnetic Microrobots for Integrated Multidimensional Targeted Biosensing.

Microrobots possessing multifunctional integration are desired for therapeutics and biomedicine applications. However, existing microrobots with desir...

Automated Neural Architecture Search for Cardiac Amyloidosis Classification from [18F]-Florbetaben PET Images.

Medical image classification using convolutional neural networks (CNNs) is promising but often requires extensive manual tuning for optimal model defi...

Applications of artificial intelligence in interventional oncology: An up-to-date review of the literature.

Interventional oncology provides image-guided therapies, including transarterial tumor embolization and percutaneous tumor ablation, for malignant tum...

Machine learning based classification of excessive smartphone users via neuronal cue reactivity.

Excessive Smartphone Use (ESU) poses a significant challenge in contemporary society, yet its recognition as a distinct disorder remains ambiguous. Th...

Histopathology-driven prostate cancer identification: A VBIR approach with CLAHE and GLCM insights.

Efficient extraction and analysis of histopathological images are crucial for accurate medical diagnoses, particularly for prostate cancer. This resea...

Machine learning models for discriminating clinically significant from clinically insignificant prostate cancer using bi-parametric magnetic resonance imaging.

PURPOSE: This study aims to demonstrate the performance of machine learning algorithms to distinguish clinically significant prostate cancer (csPCa) f...

The future of multimodal artificial intelligence models for integrating imaging and clinical metadata: a narrative review.

With the ongoing revolution of artificial intelligence (AI) in medicine, the impact of AI in radiology is more pronounced than ever. An increasing num...

Accurate prediction of disease-risk factors from volumetric medical scans by a deep vision model pre-trained with 2D scans.

The application of machine learning to tasks involving volumetric biomedical imaging is constrained by the limited availability of annotated datasets ...

Deep Learning-Based Synthetic Computed Tomography for Low-Field Brain Magnetic Resonance-Guided Radiation Therapy.

PURPOSE: Magnetic resonance (MR)-guided radiation therapy enables online adaptation to address intra- and interfractional changes. To address the need...

Breast tumor segmentation using neural cellular automata and shape guided segmentation in mammography images.

PURPOSE: Using computer-aided design (CAD) systems, this research endeavors to enhance breast cancer segmentation by addressing data insufficiency and...

Deep Learning Approaches for Brain Tumor Detection and Classification Using MRI Images (2020 to 2024): A Systematic Review.

Brain tumor is a type of disease caused by uncontrolled cell proliferation in the brain leading to serious health issues such as memory loss and motor...

Prediction of Lesion-Based Treatment Response after Two Cycles of Lu-177 Prostate Specific Membrane Antigen Treatment in Metastatic Castration-Resistant Prostate Cancer Using Machine Learning.

INTRODUCTION: Lutetium-177 (Lu-177) prostate-specific membrane antigen (PSMA) therapy is a radionuclide treatment that prolongs overall survival in me...

Joint self-supervised and supervised contrastive learning for multimodal MRI data: Towards predicting abnormal neurodevelopment.

The integration of different imaging modalities, such as structural, diffusion tensor, and functional magnetic resonance imaging, with deep learning m...

MMF-NNs: Multi-modal Multi-granularity Fusion Neural Networks for brain networks and its application to epilepsy identification.

Structural and functional brain networks are generated from two scan sequences of magnetic resonance imaging data, which can provide different perspec...