Radiology

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

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Performance of artificial intelligence in 7533 consecutive prevalent screening mammograms from the BreastScreen Australia program.

OBJECTIVES: To assess the performance of an artificial intelligence (AI) algorithm in the Australian...

Study of multistep Dense U-Net-based automatic segmentation for head MRI scans.

BACKGROUND: Despite extensive efforts to obtain accurate segmentation of magnetic resonance imaging ...

Deep learning catheter tip locations for photoacoustic-guided cardiac interventions.

SIGNIFICANCE: Interventional cardiac procedures often require ionizing radiation to guide cardiac ca...

Utilizing deep learning via the 3D U-net neural network for the delineation of brain stroke lesions in MRI image.

The segmentation of acute stroke lesions plays a vital role in healthcare by assisting doctors in ma...

Label-free deep learning-based species classification of bacteria imaged by phase-contrast microscopy.

Reliable detection and classification of bacteria and other pathogens in the human body, animals, fo...

Simplifying radiologic reports with natural language processing: a novel approach using ChatGPT in enhancing patient understanding of MRI results.

PURPOSE: The aim of this prospective cohort study was to assess the factual accuracy, completeness o...

The Progressive Veterinary Practice.

Veterinary practices must be forward-thinking to effectively serve today's pet owners and move into ...

Liver fibrosis classification from ultrasound using machine learning: a systematic literature review.

PURPOSE: Liver biopsy was considered the gold standard for diagnosing liver fibrosis; however, with ...

SIMPLEX: Multiple phase-cycled bSSFP quantitative magnetization transfer imaging with physic-guided simulation learning of neural network.

Most quantitative magnetization transfer (qMT) imaging methods require acquiring additional quantita...

A narrative review of radiomics and deep learning advances in neuroblastoma: updates and challenges.

Neuroblastoma is an extremely heterogeneous tumor that commonly occurs in children. The diagnosis an...

MRI-based automatic identification and segmentation of extrahepatic cholangiocarcinoma using deep learning network.

BACKGROUND: Accurate identification of extrahepatic cholangiocarcinoma (ECC) from an image is challe...

A multi-centric evaluation of self-learning GAN based pseudo-CT generation software for low field pelvic magnetic resonance imaging.

PURPOSE/OBJECTIVES: An artificial intelligence-based pseudo-CT from low-field MR images is proposed ...

Quantitative analysis of deep learning-based denoising model efficacy on optical coherence tomography images with different noise levels.

BACKGROUND: To quantitatively evaluate the effectiveness of the Noise2Noise (N2N) model, a deep lear...

Localization of early infarction on non-contrast CT images in acute ischemic stroke with deep learning approach.

Localization of early infarction on first-line Non-contrast computed tomogram (NCCT) guides prompt t...

Accurate classification of major brain cell types using in vivo imaging and neural network processing.

Comprehensive analysis of tissue cell type composition using microscopic techniques has primarily be...

Classification of brain tumours from MRI images using deep learning-enabled hybrid optimization algorithm.

Brain tumours are produced by the uncontrolled, and unusual tissue growth of brain. Because of the w...

Population-wide evaluation of artificial intelligence and radiologist assessment of screening mammograms.

OBJECTIVES: To validate an AI system for standalone breast cancer detection on an entire screening p...

Improving mammography interpretation for both novice and experienced readers: a comparative study of two commercial artificial intelligence software.

OBJECTIVES: To evaluate the improvement of mammography interpretation for novice and experienced rad...

Evaluating the performance of Generative Pre-trained Transformer-4 (GPT-4) in standardizing radiology reports.

OBJECTIVE: Radiology reporting is an essential component of clinical diagnosis and decision-making. ...

BUS-BRA: A breast ultrasound dataset for assessing computer-aided diagnosis systems.

PURPOSE: Computer-aided diagnosis (CAD) systems on breast ultrasound (BUS) aim to increase the effic...

Artificial Intelligence-Driven Mammography-Based Future Breast Cancer Risk Prediction: A Systematic Review.

PURPOSE: To summarize the literature regarding the performance of mammography-image based artificial...

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