Radiology

Screening performance and characteristics of breast cancer detected in the Mammography Screening with Artificial Intelligence trial (MASAI): a randomised, controlled, parallel-group, non-inferiority, single-blinded, screening accuracy study.

BACKGROUND: Emerging evidence suggests that artificial intelligence (AI) can increase cancer detection in mammography screening while reducing screen-reading workload, but further understanding of the clinical impact is needed.

Feasibility of remote robot empowered teleultrasound scanning for radioactive patients.

To investigate the feasibility of robot-assisted teleultrasound diagnosis for radioactive patients compared with conventional ultrasound diagnosis. In this prospective study (ChineseClinicalTrials.gov identifier, ChiCTR2200057253), 32 radioactive patients were examined by conventional ultrasound and MGIUS-R3 robot-assisted ultrasound from March 2022 to June 2022. After two scans, patients complete...

Harmonizing flows: Leveraging normalizing flows for unsupervised and source-free MRI harmonization.

Lack of standardization and various intrinsic parameters for magnetic resonance (MR) image acquisition results in heterogeneous images across differen...

Deep-ER: Deep Learning ECCENTRIC Reconstruction for fast high-resolution neurometabolic imaging.

INTRODUCTION: Altered neurometabolism is an important pathological mechanism in many neurological diseases and brain cancer, which can be mapped non-i...

Practical X-ray gastric cancer diagnostic support using refined stochastic data augmentation and hard boundary box training.

Endoscopy is widely used to diagnose gastric cancer and has a high diagnostic performance, but it must be performed by a physician, which limits the n...

Digitalomics: Towards Artificial Intelligence / Machine Learning-Based Precision Cardiovascular Medicine.

Recent advances in traditional "-omics" technologies have provided deeper insights into cardiovascular diseases through comprehensive molecular profil...

The Value of Artificial Intelligence in Prostate-Specific Membrane Antigen Positron Emission Tomography: An Update.

This review aims to provide an up-to-date overview of the utility of artificial intelligence (AI) in evaluating prostate-specific membrane antigen (PS...

Application of artificial intelligence to ultrasound imaging for benign gynecological disorders: systematic review.

OBJECTIVE: Although artificial intelligence (AI) is increasingly being applied to ultrasound imaging in gynecology, efforts to synthesize the availabl...

Developing an interpretable machine learning model for diagnosing gout using clinical and ultrasound features.

OBJECTIVE: To develop a machine learning (ML) model using clinical data and ultrasound features for gout prediction, and apply SHapley Additive exPlan...

AI-based non-invasive imaging technologies for early autism spectrum disorder diagnosis: A short review and future directions.

Autism Spectrum Disorder (ASD) is a neurological condition, with recent statistics from the CDC indicating a rising prevalence of ASD diagnoses among ...

Integrating deep learning algorithms for forecasting evapotranspiration and assessing crop water stress in agricultural water management.

The increasing impacts of climate change on global agriculture necessitate the development of advanced predictive models for efficient water managemen...

Self-supervised parametric map estimation for multiplexed PET with a deep image prior.

Multiplexed positron emission tomography (mPET) imaging allows simultaneous observation of physiological and pathological information from multiple tr...

A survey of obstetric ultrasound uses and priorities for artificial intelligence-assisted obstetric ultrasound in low- and middle-income countries.

Obstetric ultrasound (OBUS) is recommended as part of antenatal care for pregnant individuals worldwide. To better understand current uses of OBUS in ...

BCT-Net: semantic-guided breast cancer segmentation on BUS.

Accurately and swiftly segmenting breast tumors is significant for cancer diagnosis and treatment. Ultrasound imaging stands as one of the widely empl...

JotlasNet: Joint tensor low-rank and attention-based sparse unrolling network for accelerating dynamic MRI.

Joint low-rank and sparse unrolling networks have shown superior performance in dynamic MRI reconstruction. However, existing works mainly utilized ma...

Multimodal Cross Global Learnable Attention Network for MR images denoising with arbitrary modal missing.

Magnetic Resonance Imaging (MRI) generates medical images of multiple sequences, i.e., multimodal, from different contrasts. However, noise will reduc...

UK Biobank MRI data can power the development of generalizable brain clocks: A study of standard ML/DL methodologies and performance analysis on external databases.

In this study, we present a comprehensive pipeline to train and compare a broad spectrum of machine learning and deep learning brain clocks, integrati...

Applying YOLOv6 as an ensemble federated learning framework to classify breast cancer pathology images.

The most common carcinoma-related cause of death among women is breast cancer. Early detection is crucial, and the manual screening method may lead to...

Deep Learning-Based Precontrast CT Parcellation for MRI-Free Brain Amyloid PET Quantification.

PURPOSE: This study aimed to develop a deep learning (DL) model for brain region parcellation using CT data from PET/CT scans to enable accurate amylo...

Ultrasound Predicts Drug-Induced Sleep Endoscopy Findings Using Machine Learning Models.

OBJECTIVES: Ultrasound is a promising low-risk imaging modality that can provide objective airway measurements that may circumvent limitations of drug...