Radiology

Latent diffusion autoencoders: Toward efficient and meaningful unsupervised representation learning in medical imaging - a case study on Alzheimer's disease.

This study presents Latent Diffusion Autoencoder (LDAE), a novel encoder-decoder diffusion-based framework for efficient and meaningful unsupervised learning in medical imaging, focusing on Alzheimer's disease (AD) using brain MRI from the ADNI database as a case study. Unlike conventional diffusion autoencoders operating in image space, LDAE applies the diffusion process in a compressed latent re...

Motion-DSD: AI-assisted dynamic frontal facial simulation of digital diagnostic waxing from 2-dimensional intraoral digital smile design.

PURPOSE: This study proposes the development of "Motion-DSD", an artificial intelligence-assisted workflow for digital smile design (DSD), which enables a dynamic 2-dimensional (2D) simulation of digital diagnostic waxing by transferring an intraoral design onto a frontal facial video, and validates its clinical feasibility. METHODS: A total of 2,000 facial and 190 intraoral images were used to fi...

Are we ready for amyloid-related imaging abnormalities (ARIA) monitoring? A European survey among neuroradiologists and neurologists by the ESNR/EAN Working Group.

OBJECTIVES: Amyloid-lowering immunotherapies can cause amyloid-related imaging abnormalities (ARIA), requiring brain MRI for detection and monitoring....

A multicenter multinational retrospective study of the 1-year natural history of LI-RADS 3 observations in patients with cirrhosis.

OBJECTIVES: To assess the 1-year natural history of liver imaging reporting and data system (LI-RADS) 3 observations on contrast-enhanced MRI in cirrh...

Artificial intelligence in lymphedema: A systematic review of diagnostic and clinical applications.

BACKGROUND: Lymphedema is a chronic, progressive condition characterized by impaired lymphatic drainage and fluid accumulation. Conventional diagnosti...

Advancements in radiation dose reduction for pediatric CT head Imaging: A scoping review of emerging Technologies, Protocols, and optimization strategies.

Computed tomography (CT) is an important imaging modality that provides cross-sectional images, aiding in the detailed visualization of internal struc...

QSAR Prediction of BBB Permeability Based on Machine Learning upon PETBD: A Novel Data Set of PET Tracers.

Assessing small-molecule blood-brain barrier permeability is laborious, yet critical in drug development. Quantitative prediction models are hindered ...

Potential change in healthcare costs of implementing artificial intelligence for detecting cervical spine fractures on CT: an early health technology assessment.

OBJECTIVES: To assess healthcare costs of patients screened for cervical spine (C-spine) fractures using CT, and estimate the change in in-hospital co...

Evaluation of Image-Level Harmonization Methods for Multi-Center MR Neuroimaging.

BACKGROUND: Multi-center imaging studies create large-scale data that are useful for identifying pathological patterns and robust training of deep lea...

The role of focal therapy for localized prostate cancer: From diagnosis to ablation.

INTRODUCTION: Focal therapy (FT) has emerged as an intermediate therapeutic strategy between active surveillance (AS) and radical treatments for the m...

Deep generative models for vessel segmentation in CT angiography of the brain.

Automated vessel segmentation in brain CT angiography (CTA) remains challenging despite the potential benefits of its applications. Expert acquisition...

Adolescents with non-suicidal self-injury exhibit increased pain empathic neural reactivity and personal distress to physical but not affective pain.

BACKGROUND: Non-suicidal self-injury (NSSI) in adolescents represents a critical public health issue. While symptomatic links between NSSI and alterat...

Multimodal Convolutional Neural Network Model for Evaluating Development of Fetal Rabbit Lung Using B-mode and Shear Wave Elastography Ultrasound Images.

OBJECTIVE: To evaluate the performance of convolutional neural network (CNN)-based models for predicting fetal rabbit lung development: unimodal model...

Classification of Salivary Gland Tumors on Ultrasound Using Artificial Intelligence: A Systematic Review and Meta-Analysis.

OBJECTIVE: Accurate classification of salivary gland tumors is critical to guiding appropriate management. This study evaluates the diagnostic perform...

Co-classification network analysis reveals nodal dysfunction and dimension-specific alterations in obstructive sleep apnea-hypopnea syndrome.

BACKGROUND: Obstructive sleep apnea-hypopnea syndrome (OSAHS) is a prevalent sleep disorder linked to brain alterations, but its brain network pattern...

The effect of spatial and intensity level augmentation of structural magnetic resonance images on autism diagnosis model.

In deep learning, the robustness and generalizability of models significantly depend on diverse and heterogeneous training data. Acquiring such an ext...

Deep Learning for Ultrasound-Based Auxiliary Diagnosis of Emergency Ascites.

OBJECTIVE: The Focused Assessment with Sonography for Trauma (FAST) enables rapid detection of free intraperitoneal fluid, facilitating timely managem...

Liver cancer segmentator: Metadata-guided confidence scoring for reliable segmentation of colorectal liver metastases in CT.

BACKGROUND AND OBJECTIVE: This study introduces the liver cancer segmentator (LCS), a deep learning model designed for automatic and robust segmentati...

Corrigendum to "Geometric evaluation of a deep learning method for segmentation of urinary OARs on magnetic resonance imaging for prostate cancer radiotherapy". [Clin. Transl. Radiat. Oncol. 56 (2026) 101091].

[This corrects the article DOI: 10.1016/j.ctro.2025.101091.].

Deep learning image reconstruction improves 40 keV virtual monoenergetic image quality in rectal cancer.

BACKGROUND: Accurate preoperative evaluation of rectal cancer is essential for staging and treatment planning. Low-energy virtual monoenergetic imagin...