Radiology

Multimodal deep learning model for multiclass classification of renal tumors.

Accurate classification of renal masses before treatment is crucial for therapeutic decision-making and patient outcome. This study developed and validated Multi-Phase Attention Network (MPANet), a multimodal deep learning model integrating multiphase contrast-enhanced CT and clinical information, which can utilize both complete-phase and missing-phase CT data for multiclass classification of four...

Classification of spinal tuberculous infection, pyogenic infection and spinal metastasis from magnetic resonance imaging using machine learning.

BACKGROUND: Differentiating between spinal tuberculosis, pyogenic (bacterial) spondylitis and spinal metastasis remains a major diagnostic challenge because their radiological features often overlap. Delayed or incorrect diagnosis may lead to inappropriate treatment, permanent disability or death. OBJECTIVE: To develop and evaluate deep learning models for automated classification of spinal tuberc...

Pelvic MRI-based machine learning models for age estimation and age threshold classification in living children and young adults.

OBJECTIVES: Accurate bone age assessment is crucial in forensic medicine and pediatrics. This study aimed to systematically characterize the MRI devel...

A multi-modal deep learning framework for enhanced breast cancer diagnosis using mammograms and clinical data.

The fatality due to breast cancer is one of the most universal causes among women worldwide. The limited interpretation is due to tissue overlapping, ...

AI-Powered Gradient Echo Plural Contrast Imaging (AI-GEPCI)-A Comprehensive Neurological Protocol From a Single MRI Scan.

BACKGROUND: MRI is essential for diagnosing and monitoring neurological diseases. Conventional protocols require multiple sequences to obtain compleme...

A multicenter-validated interpretable transformer model for pituitary microadenoma detection on non-contrast multiparametric MRI.

BACKGROUND: Detecting pituitary microadenomas using non-contrast multi-parametric magnetic resonance imaging (MRI) is challenging yet essential for cl...

From intelligent models to clinical tools: the evolving landscape of AI in medical imaging.

Integration of Artificial Intelligence (AI), particularly deep learning, into medical imaging represents a profound shift in diagnostic medicine, movi...

Breast ultrasound images for segmentation and classification using multi-task U-Net.

Breast ultrasound imaging is widely used for the early detection of breast cancer due to its accessibility and effectiveness, particularly in dense br...

Hybrid Vi+ECNN framework for advanced ADHD diagnostic accuracy in medical imaging.

Attention-Deficit/Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder that imposes significant personal and societal burdens. Tra...

Segmentation of spinal rootlets across MRI contrasts with RootletSeg.

Segmentation of spinal nerve rootlets is relevant for spinal level estimation, lesion classification, neuromodulation therapy, and group-level analyse...

Evaluation of new vectorcardiography algorithms for identifying left ventricular hypertrophy and impaired systolic function.

BACKGROUND: Timely diagnosis of impaired systolic function and left ventricular hypertrophy (LVH) remains a clinical challenge. Routine electrocardiog...

A Brainstem Radiomics Framework to Distinguish Progressive Supranuclear Palsy from Parkinson's Disease.

BACKGROUND: Differentiating progressive supranuclear palsy (PSP) from Parkinson's disease (PD) can be clinically challenging. In the neuroimaging fiel...

Carbon dots in laboratory medicine: synthesis, properties, and applications.

Carbon dots (CDs), zero-dimensional carbon-based nanomaterials, have significantly advanced laboratory medicine due to their high photoluminescence qu...

Imaging of Small Cell Lung Cancer: An Updated Overview of Current and Emerging Applications.

Small cell lung cancer (SCLC) is an aggressive pulmonary neuroendocrine carcinoma characterized by rapid progression and early metastasis. Despite rec...

Template-Based Label Propagation for Mouse Brain MRI Skull Stripping.

Accurate skull stripping is an essential preprocessing step in mouse brain magnetic resonance imaging, particularly for reliable atlas registration an...

Diagnostic Classification of Autism Spectrum Disorder in the Frequency Domain Using Resting-State fMRI.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder with problems in social interactions, verbal and non-verbal communication, repetitive ...

Megahertz-rate down-sampled optical coherence tomography based on high-speed k-linear swept laser and deep learning.

Megahertz-rate optical coherence tomography (MHz-OCT) is an optical imaging technology that has attracted considerable attention in clinical practice....

Vision Transformer-Based Segmentation of Abdominal Subcutaneous and Visceral Fat on MRI.

The purpose of this study is to validate a deep learning-based vision transformer for automated quantification and segmentation of abdominal adipose t...

Comparative Clinical Evaluation of "Memory-Efficient" Synthetic 3D Generative Adversarial Networks (GAN) Head-to-Head to State of Art: Results on Computed Tomography of the Chest.

Generative adversarial networks (GANs) are increasingly used to generate synthetic medical images, addressing the critical shortage of annotated data ...

Balancing Speed and Sensitivity: Echo-Planar Accelerated MRI for ARIA-H Screening in Anti-Aβ Therapeutics.

The recent advent of anti-amyloid-β monoclonal antibodies has introduced new demands for MRI-based screening of amyloid-related imaging abnormalities,...