Radiology

Latest AI and machine learning research in radiology for healthcare professionals.

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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...

Jan 2025 39888598
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 indi...

Jan 2025 39919468
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 advanc...

Jan 2025 39889430
Self-supervised parametric map estimation for multiplexed PET with a deep image prior.

Multiplexed positron emission tomography (mPET) imaging allows simultaneous observation of physiolog...

Jan 2025 39774095
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 worldw...

Jan 2025 39890863
BCT-Net: semantic-guided breast cancer segmentation on BUS.

Accurately and swiftly segmenting breast tumors is significant for cancer diagnosis and treatment. U...

Jan 2025 39883373
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 reconstr...

Jan 2025 39889975
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, f...

Jan 2025 39904265
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 ...

Jan 2025 39876079
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 me...

Jan 2025 39876795
Segmentation of coronary artery and calcification using prior knowledge based deep learning framework.

BACKGROUND: Computed tomography angiography (CTA) is used to screen for coronary artery calcificatio...

Jan 2025 39878608
Triaging mammography with artificial intelligence: an implementation study.

PURPOSE: Many breast centers are unable to provide immediate results at the time of screening mammog...

Jan 2025 39881074
Use of ChatGPT Large Language Models to Extract Details of Recommendations for Additional Imaging From Free-Text Impressions of Radiology Reports.

Automated extraction of actionable details of recommendations for additional imaging (RAIs) from ra...

Jan 2025 39878409
Enhancing quantitative coronary angiography (QCA) with advanced artificial intelligence: comparison with manual QCA and visual estimation.

Artificial intelligence-based quantitative coronary angiography (AI-QCA) was introduced to address m...

Jan 2025 39875702
A novel approach in cancer diagnosis: integrating holography microscopic medical imaging and deep learning techniques-challenges and future trends.

Medical imaging is pivotal in early disease diagnosis, providing essential insights that enable time...

Jan 2025 39671712
Automatic segmentation of the midfacial bone surface from ultrasound images using deep learning methods.

With developments in computer science and technology, great progress has been made in three-dimensio...

Jan 2025 39880737
Artificial intelligence for brain neuroanatomical segmentation in magnetic resonance imaging: A literature review.

PURPOSE: This literature review aims to synthesise current research on the application of artificial...

Jan 2025 39879724
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