Injuries to the anterior cruciate ligament (ACL) are very common, and ACL injuries increase the risk of developing post-traumatic knee osteoarthritis and total knee replacement (TKR). At present, Magnetic Resonance Imaging (MRI) is the most effective imaging modality for distinguishing structural properties of the ACL in relation to adjacent musculoskeletal structures. Several multi-grading scoring systems have been developed to standardize reporting of knee joint abnormalities using MRI including the Whole-Organ Magnetic Resonance Imaging Scale (WORMS) and the Anterior Cruciate Ligament OsteoArthritis Score (ACLOAS). However, both of these grading metrics are susceptible to inter-rater variability.
Deep learning methods have recently shown potential to serve as an aid for clinicians with limited time or experience in osteoarthritis grading of the knee menisci and cartilage. Recently a team of scientists from the UCSF Center for Intelligent Imaging (ci2) evaluated the diagnostic utility of two convolutional neural networks (CNNs) for severity staging of anterior cruciate ligament (ACL) injuries. “Previous studies have developed binary classifiers to distinguish fully torn ACLs from intact ACLs,” said Nikan Namiri, medical student at UCSF School of Medicine and corresponding author. “And our study is the first to take deep learning a step further to help classify a broader spectrum of injury, which may be more useful in the clinical setting… Continue reading.
UC San Francisco is launching a new center to accelerate the application of artificial intelligence (AI) technology to radiology, leveraging advanced computational techniques and industry collaborations to improve patient diagnoses and care.
The Center for Intelligent Imaging, or ci2, will develop and apply AI to devise powerful new ways to look inside the body and to evaluate health and disease. Investigators in ci2 will team with Santa Clara, Calif.-based NVIDIA Corp., an industry leader in AI computing, to build infrastructure and tools focused on enabling the translation of AI into clinical practice.
“The volume of medical imaging has been rapidly increasing and radiologists are struggling to keep up with the sheer number of images,” said Sharmila Majumdar, PhD, a professor and vice chair in the UCSF Department of Radiology and Biomedical Imaging. “ci2 aims to impact the entire value chain of imaging, from the time the patient comes for a scan to the final delivery of individualized, quantitative, prognostic and care-defining information… Continue reading.
Deep learning has become a powerful tool in radiology in recent years. Researchers at the UC San Francisco Department of Radiology and Biomedical Imaging have started using deep learning methods to characterize joint degeneration and osteoarthritis, which will ultimately reduce the number of total joint replacements. In a recent paper published in Radiology (PubMed) they demonstrate that it is possible to automatically identify (segment) cartilage and meniscus tissue in the knee joint and extract measures of tissue structure such as volume and thickness, as well as tissue biochemistry, by a method know as MR relaxometry. Cartilage and meniscus morphological and biochemical changes are tissue-level symptoms of joint degeneration.
To perform automatic segmentation of cartilage and meniscus, they developed a deep learning model based on the U-Net convolutional network architecture using 638 image datasets. Performance of the automatic segmentation was evaluated using the Dice coefficient overlap with manual segmentation done by many radiologists, which took more than an hour for each data set. The models averaged five seconds to generate automatic segmentations with excellent agreement with those done by radiologists. The precision and agreement between measures of cartilage thickness and biochemistry provided by the deep learning models and those using manual methods was also excellent. Measure of relaxation times (biochemical information) and morphologic characterization of joint tissues (such as thickness and volume of cartilage) are not available in the clinic today, due to the long analysis times required. These advances bring new hope for extracting quantitative information from magnetic resonance images, thus standardizing and making the staging the extent of joint degeneration precise and accurate. These advances will have significant impact in the monitoring and diagnosis of osteoarthritis (OA), a debilitating condition affecting the quality of life for millions of adults and the leading cause of total joint replacements… Continue reading.
Sharmila Majumdar, PhD, has been awarded the 2016 Gold Medal of the International Society for Magnetic Resonance in Medicine (ISMRM) for her innovative contributions to the development of quantitative imaging methods.
Her research has potential for personalizing treatments for patients, and it is a significant step forward in setting up the precision medicine framework for musculoskeletal diseases.
“Dr. Majumdar has been a leader in MRI for 30 years and has in particular pioneered the development and applications of quantitative imaging to diagnose and understand musculoskeletal disorders,” said John C. Gore, PhD, professor of Radiology and Radiologic Sciences and Director of the Institute for Imaging Sciences at Vanderbilt University School of Medicine. “Her work has had broad translational impact on the clinical management of common problems of joints and cartilage.”
How people walk, jump and run and how their knees look in an MRI scanner may hold the secret to predicting years or even decades in advance whether they will develop osteoarthritis, the common degenerative joint disease that strikes half of all Americans by the time they reach the age of 70.
Doctors today cannot look at a person’s gait, leap, stride or scan and tell you definitively whether or not they will develop osteoarthritis, but a new translational research center at the University of California, San Francisco (UCSF) Medical Center and the University of California, Davis seeks to change this.
Funded by a $6.3 million grant from the National Institutes of Health, the center will bring together radiologists, orthopedic surgeons, rheumatologists, laboratory scientists, mathematicians and physical therapists under one umbrella with a single purpose: finding new tools for predicting and preventing osteoarthritis in young people and improving care and outcomes for the tens of millions of American adults already suffering from the disease.
“Osteoarthritis is one of the major age-related illnesses of our times, and there’s no way to slow or reverse it once it starts,” said Sharmila Majumdar, PhD, UCSF Professor in Residence and Vice-Chair of Research in the Department of Radiology and Biomedical Imaging. “The diverse group of experts at the center will all be seeking to address this problem, but from different perspectives, integrating imaging, biomechanics and the symptoms of the individual.”
UCSF students will soon have the opportunity to broaden their investigative projects with a comprehensive understanding of imaging as part of a new Master’s Degree Program in Biomedical Imaging (MBI) launching this fall.
“We are the leading health science campus for the UC system and have faculty and physicians who have embraced quantitative imaging,” said Sharmila Majumdar, PhD, professor of radiology and biomedical imaging and co-chair of the MBI program committee. “We are uniquely positioned because of our resources and faculty expertise.”
One of the first programs of its kind, the MBI is intended for students with bachelor’s degrees, advanced pre-doctoral students, postdoctoral fellows, residents, researchers and faculty who want to have a deeper knowledge of imaging techniques. The master’s degree may be completed in one year of full time study or completed on a part time schedule but in an interval not to exceed three years.