Prospective deployment of deep learning in MRI: a framework for important considerations, challenges, and recommendations for best practices
Artificial intelligence algorithms based on principles of deep learning (DL) have made a
large impact on the acquisition, reconstruction, and interpretation of MRI data. Despite the …
large impact on the acquisition, reconstruction, and interpretation of MRI data. Despite the …
Studying osteoarthritis with artificial intelligence applied to magnetic resonance imaging
The 3D nature and soft-tissue contrast of MRI makes it an invaluable tool for osteoarthritis
research, by facilitating the elucidation of disease pathogenesis and progression. The recent …
research, by facilitating the elucidation of disease pathogenesis and progression. The recent …
Skm-tea: A dataset for accelerated mri reconstruction with dense image labels for quantitative clinical evaluation
Magnetic resonance imaging (MRI) is a cornerstone of modern medical imaging. However,
long image acquisition times, the need for qualitative expert analysis, and the lack of (and …
long image acquisition times, the need for qualitative expert analysis, and the lack of (and …
The Stanford Medicine data science ecosystem for clinical and translational research
Objective To describe the infrastructure, tools, and services developed at Stanford Medicine
to maintain its data science ecosystem and research patient data repository for clinical and …
to maintain its data science ecosystem and research patient data repository for clinical and …
Deep learning‐based segmentation of knee MRI for fully automatic subregional morphological assessment of cartilage tissues: Data from the Osteoarthritis Initiative
Morphological changes in knee cartilage subregions are valuable imaging‐based
biomarkers for understanding progression of osteoarthritis, and they are typically detected …
biomarkers for understanding progression of osteoarthritis, and they are typically detected …
Artificial intelligence to analyze magnetic resonance imaging in rheumatology
Rheumatic disorders present a global health challenge, marked by inflammation and
damage to joints, bones, and connective tissues. Accurate, timely diagnosis and appropriate …
damage to joints, bones, and connective tissues. Accurate, timely diagnosis and appropriate …
Deep learning for accelerated and robust MRI reconstruction
Deep learning (DL) has recently emerged as a pivotal technology for enhancing magnetic
resonance imaging (MRI), a critical tool in diagnostic radiology. This review paper provides …
resonance imaging (MRI), a critical tool in diagnostic radiology. This review paper provides …
Challenges in ensuring the generalizability of image quantitation methods for MRI
Image quantitation methods including quantitative MRI, multiparametric MRI, and radiomics
offer great promise for clinical use. However, many of these methods have limited clinical …
offer great promise for clinical use. However, many of these methods have limited clinical …
The KNee OsteoArthritis Prediction (KNOAP2020) challenge: An image analysis challenge to predict incident symptomatic radiographic knee osteoarthritis from MRI …
Summary Objectives The KNee OsteoArthritis Prediction (KNOAP2020) challenge was
organized to objectively compare methods for the prediction of incident symptomatic …
organized to objectively compare methods for the prediction of incident symptomatic …
Automated segmentation of articular disc of the temporomandibular joint on magnetic resonance images using deep learning
S Ito, Y Mine, Y Yoshimi, S Takeda, A Tanaka… - Scientific Reports, 2022 - nature.com
Temporomandibular disorders are typically accompanied by a number of clinical
manifestations that involve pain and dysfunction of the masticatory muscles and …
manifestations that involve pain and dysfunction of the masticatory muscles and …