Radiology

Applying machine-learning and deep-learning to predict depression from brain MRI and identify depression-related brain biology.

The accuracy of grey-matter predictors of depression has remained limited. In this study, brain-based predictors of major depressive disorder (MDD) were trained using machine-learning (Best Linear Unbiased Predictors [BLUP]) and deep-learning (ResNet3D) techniques applied to high-dimensional (voxel-wise) grey-matter structure extracted from T1-weighted structural MRI. The training sample comprised...

Ultrasound-Responsive Serotonylation and Hallmark Pathway Gene Signatures Reveal Tumor Microenvironment Vulnerabilities and Prognostic Subtypes in Colorectal Cancer.

BACKGROUND: Focused ultrasound, low-intensity focused ultrasound, and microbubble-enhanced sonoporation are examples of ultrasound-based cancer therapies that have shown promise as biophysical modalities for enhancing drug penetration, immunogenic cell death, and targeted delivery of radiopharmaceuticals in solid tumors. The molecular factors controlling ultrasonic therapy receptivity, however, ar...

Geometric deep learning-based coronary wall shear stress estimation from real-world patients.

BACKGROUND: Coronary wall shear stress (WSS) derived from computational fluid dynamics (CFD) provides mechanistic insight and prognostic information, ...

Minimally Invasive Resection of a Compound Odontoma Assisted by an Autonomous Oral Robot: A Technical Innovation.

This report describes the first clinical application of an autonomous oral robot for minimally invasive resection of a compound odontoma in a 16-year-...

EASDnet: Empowering human-centered evidence-based medicine through an evidence and attention-based spatial disparity network for discriminative colorectal cancer histopathological screening and attribution.

PURPOSE: Accurate preoperative staging of colorectal cancer is critical to guide treatment decisions, including eligibility for R0 resection, to reduc...

Screening for Breast Cancer: Imaging Updates.

This article provides a comprehensive overview of breast cancer screening guidelines and the evolving role of established and emerging imaging technol...

Real-Time MRI With Deep Learning for Efficient Evaluation of Neuromuscular Breathing Impairment.

Efficient detection of breathing impairment is critical for treatment and prognosis in neuromuscular disorders. However, standard pulmonary function t...

Leveraging AI solutions for sustainable practice in pediatric radiology: a practical guide and an educational tool.

Pediatric imaging presents distinct and urgent sustainability challenges, in part driven by its unique subspecialty demands: safeguarding the lifetime...

Predicting Hypertension Persistence in Coarctation of the Aorta: A Feasibility Study.

Hypertension (HTN), despite contemporary endovascular repair, is a common and challenging complication of coarctation of the aorta (CoA), and its mech...

Dual Low-b-value-driven U-shaped fusion GAN for synthesizing high-b-value prostate DWI.

High-b-value (b = 2000 s/mm²) diffusion-weighted imaging (DWI) is vital for prostate disease detection and characterization due to superior tumor-to-b...

Regional brain age is decreased in children with sickle cell anemia.

DeepBrainNet, a machine learning tool, uses magnetic resonance imaging (MRI) to predict an individual's brain age, allowing calculation of the brain a...

Predicting Plaque Vulnerability Using Machine Learning-Enabled Nanoagents Sensitized Molecular High-Resolution Magnetic Resonance Imaging Data.

Molecular imaging based on paramagnetic nanoagents has emerged as an intriguing strategy to sensitize the local magnetic properties of pivotal patholo...

Natriuretic peptides and cardiac troponins in hypertension and heart failure: A contemporary review of the evidence.

Hypertension is the second leading cause of heart failure (HF), yet strategies for identifying hypertensive individuals at increased HF risk remain li...

Simulated evaluation of large language model stepwise diagnostic reasoning with real-world chest pain encounters and Bayesian networks.

BACKGROUND: Real-world evaluation of large language models (LLMs) as clinical diagnostic aids is limited by the reliance on static vignettes and retro...

Regional brain age is decreased in children with sickle cell anemia.

DeepBrainNet, a machine learning tool, uses magnetic resonance imaging (MRI) to predict an individual's brain age, allowing calculation of the brain a...

Environmental PET-microplastic exposure and risk of non-alcoholic fatty liver disease: An integrated computational toxicology and multi-omics study.

The escalating severity of global microplastic pollution has triggered significant public health concerns. Polyethylene terephthalate (PET), a ubiquit...

A systematic review on dysconnectivity in face and emotion processing networks in schizophrenia.

Schizophrenia is a complex psychiatric disorder that affects approximately 20 million people worldwide. Patients show face-processing deficits that si...

Automatic segmentation of coronary plaques in coronary CT angiography using neural networks.

Rapid and accurate detection of coronary plaques on CCTA is critical for timely CAD diagnosis but is limited by reader workload and interobserver vari...

TASNet: A tri-modal attentive scale robust adaptive fusion framework for progressive mild cognitive impairment classification.

BACKGROUND: Accurate prediction of progressive Mild Cognitive Impairment (pMCI) versus stable MCI (sMCI) is crucial for early Alzheimer's disease (AD)...

An optimized hierarchical attention assisted deep learning model for brain tissue classification.

BACKGROUND & OBJECTIVE: Precise differentiation of brain tissue from Magnetic Resonance Imaging is a vital constraint in various medical applications....