AIMC Topic: Magnetic Resonance Imaging

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MRI-based automated multitask deep learning system to evaluate supraspinatus tendon injuries.

European radiology
OBJECTIVE: To establish an automated, multitask, MRI-based deep learning system for the detailed evaluation of supraspinatus tendon (SST) injuries.

Brain age predicted using graph convolutional neural network explains neurodevelopmental trajectory in preterm neonates.

European radiology
OBJECTIVES: Dramatic brain morphological changes occur throughout the third trimester of gestation. In this study, we investigated whether the predicted brain age (PBA) derived from graph convolutional network (GCN) that accounts for cortical morphom...

Use of artificial intelligence in determination of bone age of the healthy individuals: A scoping review.

Journal of the World federation of orthodontists
BACKGROUND: Bone age assessment, as an indicator of biological age, is widely used in orthodontics and pediatric endocrinology. Owing to significant subject variations in the manual method of assessment, artificial intelligence (AI), machine learning...

Glioblastoma and Solitary Brain Metastasis: Differentiation by Integrating Demographic-MRI and Deep-Learning Radiomics Signatures.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Studies have shown that deep-learning radiomics (DLR) could help differentiate glioblastoma (GBM) from solitary brain metastasis (SBM), but whether integrating demographic-MRI and DLR features can more accurately distinguish GBM from SBM ...

Feasibility of Monte Carlo dropout-based uncertainty maps to evaluate deep learning-based synthetic CTs for adaptive proton therapy.

Medical physics
BACKGROUND: Deep learning has shown promising results to generate MRI-based synthetic CTs and to enable accurate proton dose calculations on MRIs. For clinical implementation of synthetic CTs, quality assurance tools that verify their quality and rel...

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

Medical physics
BACKGROUND: Despite extensive efforts to obtain accurate segmentation of magnetic resonance imaging (MRI) scans of a head, it remains challenging primarily due to variations in intensity distribution, which depend on the equipment and parameters used...

Brain metastasis tumor segmentation and detection using deep learning algorithms: A systematic review and meta-analysis.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
BACKGROUND: Manual detection of brain metastases is both laborious and inconsistent, driving the need for more efficient solutions. Accordingly, our systematic review and meta-analysis assessed the efficacy of deep learning algorithms in detecting an...

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

Scientific reports
The segmentation of acute stroke lesions plays a vital role in healthcare by assisting doctors in making prompt and well-informed treatment choices. Although Magnetic Resonance Imaging (MRI) is a time-intensive procedure, it produces high-fidelity im...

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

Archives of orthopaedic and trauma surgery
PURPOSE: The aim of this prospective cohort study was to assess the factual accuracy, completeness of medical information, and potential harmfulness of incorrect conclusions by medical professionals in automatically generated texts of varying complex...