Keywords: Muscle, Muscle, Data Analysis, MSK
Motivation: Estimating physical performance including muscle function is conventionally done by having a subject complete physical tasks. MRI-based estimates leveraging deep learning could complement such measures.
Goal(s): To investigate the feasibility of predicting measures physical performance including muscle strength from MRI scans of the leg using deep learning.
Approach: We used large MRI databases (OAI and SOMMA) to train a neural network for classification into high or low physical performance. We also tried the method on a small prospective cohort.
Results: We obtained over 70% accuracy for estimating high or low muscle function, indicating high predictive power.
Impact: We demonstrate the feasibility of predicting muscle function from anatomical MRI scans using deep learning, leveraging advances in deep learning and musculoskeletal MRI databases that include functional measures. Such MRI-based predictions could complement conventional methods for estimating muscle function.
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Keywords