Radiology

Clinically aligned whole-body MRI segmentation of skeletal metastases via Supervised Anatomical Pretraining.

In oncology practice, response assessment of metastatic disease requires reliable and reproducible quantification of measurable metastatic burden. Manual identification, segmentation, and volumetry of all lesions is labor-intensive and variable, limiting routine clinical adoption. An automated approach is therefore needed. Segmenting metastatic bone disease (MBD) on whole-body MRI (WB-MRI) is chal...

One-stop strabismus digital diagnosis via AI-integrated skin-like and wearable "Eyelectronics".

Strabismus, affecting ~4% of children, impairs vision and psychosocial health. However, clinical diagnosis requires multiple instruments and stepwise examinations of ocular alignment, extraocular muscle function, and deviation angle. It is limited by low diagnostic objectivity, poor pediatric compliance, and high cost. Here, we propose a strategy for one-stop strabismus digital diagnosis via artif...

Functional neurobehaviour of the human fetus: a comprehensive framework for prenatal assessment using 4D ultrasound and AI.

OBJECTIVES: To evaluate current evidence on prenatal neurobehavioural assessment using four-dimensional ultrasound (4D-US), structured scoring systems...

Development and validation of a neural network survival prediction model for ischemic heart disease.

BACKGROUND: Current risk prediction models for ischemic heart disease in clinical use are relatively simple and use a limited collection of well-known...

Lipoprotein(a) Selectively Associates with Vulnerable Coronary Plaque Phenotypes in Comparison with Other Established Risk Markers.

AIMS: Lipoprotein(a) [Lp(a)] is an inherited cardiovascular risk factor. However, its association with coronary plaque characteristics beyond traditio...

Deep learning-based plaque characterization in hybrid IVUS-OCT images is superior to single-modality deep learning analysis and human experts: head-to-head comparison against histology.

AIMS: Hybrid intravascular ultrasound-optical coherence tomography (IVUS-OCT) can enable more accurate plaque characterization than single-modality in...

18F-FDG PET radiomics model for predicting TARE response in patients with colorectal cancer liver metastases.

PURPOSE: Predicting treatment response in patients with colorectal cancer liver metastases (CRCLM) who have undergone transarterial radioembolization ...

Evaluation of supervised machine learning models in predicting temporomandibular joint disc displacement on 3T magnetic resonance imaging.

OBJECTIVE: To evaluate supervised machine learning (ML) models for classifying temporomandibular joint (TMJ) disc displacement on MRI using morphometr...

Generative deep learning synthesizes high signal-to-noise ratio sensitivity maps for PET from low count direct normalization data.

An accurate and precise normalization procedure is essential to correct for variations in detector efficiency in reconstructed positron emission t...

An Efficient 3D Latent Diffusion Model for T1-contrast Enhanced MRI Generation.

Gadolinium-based contrast agents (GBCAs) are commonly employed with T1-weighted (T1w) MRI to enhance lesion visualization but are restricted in patien...

Sub-connection Learning for fMRI-based Brain Functional Network.

Brain functional network analysis models the brain as a graph of regions of interest (ROIs) and quantifies the correlations across different regions d...

Radiomics analysis of early pregnancy ultrasound images to predict viability at the end of first trimester.

To determine whether there are radiomic ultrasound features of early pregnancy when viability is unknown, which in combination with clinical features,...

A comprehensive multi-task deep learning model for kidney cancer: histological subtyping, clinical staging, and anatomical complexity grading.

OBJECTIVES: To develop and validate a multi-task deep learning (MTDL) model using multiphase contrast-enhanced CT (CECT) for simultaneously assessing ...

Semi-supervised learning in prostate MRI tumor detection approaches fully supervised performance on external validation.

OBJECTIVE: To evaluate the diagnostic performance of semi-supervised learning models for aggressive prostate cancer detection on MRI compared to fully...

Automatic detection and measurement system for aortic aneurysms using deep learning-based artificial intelligence.

This study evaluated the performance of a deep learning-based artificial intelligence (DLAI) system for detecting fusiform aortic aneurysms and measur...

Mechanoresponsive Biomaterials: Principles, Mechanisms, and Applications.

Mechanoresponsive biomaterials are a revolutionary class of materials designed to respond dynamically to mechanical stimuli, providing tissue engineer...

The Role of Lower-Limb Muscle Volumes in Vertical Jump Performance in NCAA Division I Athletes.

INTRODUCTION/PURPOSE: This study examined the relationship between lower extremity muscle volumes and countermovement jump (CMJ) performance in 207 (4...

Human-AI interaction and collaboration in radiology: from conceptual frameworks to responsible implementation.

Artificial intelligence (AI) is entering routine radiology practice, but most studies evaluate algorithms in isolation rather than their interaction w...

A comprehensive review of emerging protein modification methods: modified properties and potential applications.

This review discusses recent advances in protein modification technologies that aim to enhance key functional properties, including solubility, emulsi...

Deep learning outperformed radiomics based on MRI in the differentiation of sinonasal small round cell and non-small round cell malignant tumors.

OBJECTIVE: Differentiating sinonasal small round cell malignant tumors (SRCMTs) from non-SRCMTs is challenging due to overlapping MRI features. This s...