AIMC Topic: Radiography

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Radiomics: from qualitative to quantitative imaging.

The British journal of radiology Feb 26, 2020
Historically, medical imaging has been a qualitative or semi-quantitative modality. It is difficult to quantify what can be seen in an image, and to turn it into valuable predictive outcomes. As a result of advances in both computational hardware and...
Diagnostic Imaging Workflow Technology, Radiologic Image Processing, Computer-Assisted Lung Neoplasms Forecasting Female Breast Neoplasms Brain Neoplasms Male Deep Learning Radiography Humans
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Patient Perspectives and Priorities Regarding Artificial Intelligence in Radiology: Opportunities for Patient-Centered Radiology.

Journal of the American College of Radiology : JACR Feb 14, 2020
Radiology Patient-Centered Care Humans Artificial Intelligence Radiography
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Assessment of knee pain from MR imaging using a convolutional Siamese network.

European radiology Feb 13, 2020
OBJECTIVES: It remains difficult to characterize the source of pain in knee joints either using radiographs or magnetic resonance imaging (MRI). We sought to determine if advanced machine learning methods such as deep neural networks could distinguis...
Female Knee Joint Knee Humans Arthralgia Magnetic Resonance Imaging Aged Synovitis Middle Aged Area Under Curve Neural Networks, Computer Osteoarthritis, Knee Male Severity of Illness Index Radiography Deep Learning
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Noninterpretive Uses of Artificial Intelligence in Radiology.

Academic radiology Feb 12, 2020
We deem a computer to exhibit artificial intelligence (AI) when it performs a task that would normally require intelligent action by a human. Much of the recent excitement about AI in the medical literature has revolved around the ability of AI model...
Radiologists Artificial Intelligence Radiography Radiology Humans
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Artificial Intelligence for MR Image Reconstruction: An Overview for Clinicians.

Journal of magnetic resonance imaging : JMRI Feb 12, 2020
Artificial intelligence (AI) shows tremendous promise in the field of medical imaging, with recent breakthroughs applying deep-learning models for data acquisition, classification problems, segmentation, image synthesis, and image reconstruction. Wit...
Artifacts Humans Image Processing, Computer-Assisted Radiography Artificial Intelligence Algorithms
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Detecting total hip replacement prosthesis design on plain radiographs using deep convolutional neural network.

Journal of orthopaedic research : official publication of the Orthopaedic Research Society Feb 11, 2020
Identifying the design of a failed implant is a key step in the preoperative planning of revision total joint arthroplasty. Manual identification of the implant design from radiographic images is time-consuming and prone to error. Failure to identify...
Aged Aged, 80 and over Male Humans Retrospective Studies Female Prosthesis Design Hip Joint Arthroplasty, Replacement, Hip Hip Prosthesis Middle Aged Deep Learning Radiography
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Deep learning risk assessment models for predicting progression of radiographic medial joint space loss over a 48-MONTH follow-up period.

Osteoarthritis and cartilage Feb 6, 2020
OBJECTIVE: To develop and evaluate deep learning (DL) risk assessment models for predicting the progression of radiographic medial joint space loss using baseline knee X-rays.
Osteoarthritis, Knee Middle Aged Risk Assessment Disease Progression Deep Learning Area Under Curve Risk Factors Radiography Aged Male Humans Female
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Ruling out rotator cuff tear in shoulder radiograph series using deep learning: redefining the role of conventional radiograph.

European radiology Feb 5, 2020
OBJECTIVE: To develop a deep learning algorithm that can rule out significant rotator cuff tear based on conventional shoulder radiographs in patients suspected of rotator cuff tear.
Female Reproducibility of Results Sensitivity and Specificity Humans Retrospective Studies Radiography Radiographic Image Interpretation, Computer-Assisted Rotator Cuff Injuries Male Middle Aged Deep Learning Rotator Cuff
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Development and Validation of a Multitask Deep Learning Model for Severity Grading of Hip Osteoarthritis Features on Radiographs.

Radiology Feb 4, 2020
Background A multitask deep learning model might be useful in large epidemiologic studies wherein detailed structural assessment of osteoarthritis still relies on expert radiologists' readings. The potential of such a model in clinical routine should...
Middle Aged Female Humans Severity of Illness Index Aged Models, Theoretical Retrospective Studies Male Deep Learning Radiography Osteoarthritis, Hip
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Deep Learning for Natural Language Processing in Radiology-Fundamentals and a Systematic Review.

Journal of the American College of Radiology : JACR Jan 28, 2020
PURPOSE: Natural language processing (NLP) enables conversion of free text into structured data. Recent innovations in deep learning technology provide improved NLP performance. We aimed to survey deep learning NLP fundamentals and review radiology-r...
Radiography Neural Networks, Computer Radiology Deep Learning Natural Language Processing
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