AIMC Topic:
Magnetic Resonance Imaging
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Anti-HER2 therapy response assessment for guiding treatment (de-)escalation in early HER2-positive breast cancer using a novel deep learning radiomics model.
European radiology
Feb 8, 2024
OBJECTIVES: Anti-HER2 targeted therapy significantly reduces risk of relapse in HER2 + breast cancer. New measures are needed for a precise risk stratification to guide (de-)escalation of anti-HER2 strategy.
Deep learning based detection of osteophytes in radiographs and magnetic resonance imagings of the knee using 2D and 3D morphology.
Journal of orthopaedic research : official publication of the Orthopaedic Research Society
Feb 7, 2024
In this study, we investigated the discriminative capacity of knee morphology in automatic detection of osteophytes defined by the Osteoarthritis Research Society International atlas, using X-ray and magnetic resonance imaging (MRI) data. For the X-r...
The Role of Artificial Intelligence in Cardiac Imaging.
Radiologic clinics of North America
Feb 7, 2024
Artificial intelligence (AI) is having a significant impact in medical imaging, advancing almost every aspect of the field, from image acquisition and postprocessing to automated image analysis with outreach toward supporting decision making. Noninva...
From jargon to clarity: Improving the readability of foot and ankle radiology reports with an artificial intelligence large language model.
Foot and ankle surgery : official journal of the European Society of Foot and Ankle Surgeons
Feb 5, 2024
BACKGROUND: The purpose of this study was to evaluate the efficacy of an Artificial Intelligence Large Language Model (AI-LLM) at improving the readability foot and ankle orthopedic radiology reports.
NPB-REC: A non-parametric Bayesian deep-learning approach for undersampled MRI reconstruction with uncertainty estimation.
Artificial intelligence in medicine
Feb 5, 2024
The ability to reconstruct high-quality images from undersampled MRI data is vital in improving MRI temporal resolution and reducing acquisition times. Deep learning methods have been proposed for this task, but the lack of verified methods to quanti...
Deep learning algorithm-based multimodal MRI radiomics and pathomics data improve prediction of bone metastases in primary prostate cancer.
Journal of cancer research and clinical oncology
Feb 5, 2024
PURPOSE: Bone metastasis is a significant contributor to morbidity and mortality in advanced prostate cancer, and early diagnosis is challenging due to its insidious onset. The use of machine learning to obtain prognostic information from pathologica...
Magnetic resonance imaging-based radiomics and deep learning models for predicting lymph node metastasis of squamous cell carcinoma of the tongue.
Oral surgery, oral medicine, oral pathology and oral radiology
Feb 3, 2024
OBJECTIVE: This study aimed to establish a combined method of radiomics and deep learning (DL) in magnetic resonance imaging (MRI) to predict lymph node metastasis (LNM) preoperatively in patients with squamous cell carcinoma of the tongue.
Deep Learning for Retrospective Motion Correction in MRI: A Comprehensive Review.
IEEE transactions on medical imaging
Feb 2, 2024
Motion represents one of the major challenges in magnetic resonance imaging (MRI). Since the MR signal is acquired in frequency space, any motion of the imaged object leads to complex artefacts in the reconstructed image in addition to other MR imagi...
deepPGSegNet: MRI-based pituitary gland segmentation using deep learning.
Frontiers in endocrinology
Feb 2, 2024
INTRODUCTION: In clinical research on pituitary disorders, pituitary gland (PG) segmentation plays a pivotal role, which impacts the diagnosis and treatment of conditions such as endocrine dysfunctions and visual impairments. Manual segmentation, whi...
Automated abdominal adipose tissue segmentation and volume quantification on longitudinal MRI using 3D convolutional neural networks with multi-contrast inputs.
Magma (New York, N.Y.)
Feb 1, 2024
OBJECTIVE: Increased subcutaneous and visceral adipose tissue (SAT/VAT) volume is associated with risk for cardiometabolic diseases. This work aimed to develop and evaluate automated abdominal SAT/VAT segmentation on longitudinal MRI in adults with o...
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