Introduced by Claudia Iriondo, Devina Jain, Raouf Muhamedrahimov, Vasileious Papanikolau, Kosta Trotskovsky and Leo Sun

More than 25 percent of adults in the United States suffer from knee pain, creating an urgent need for an efficient way to match patients who have cartilage and bone lesions with effective treatments. Patients undergo Magnetic Resonance Imaging (MRI) scans across the country, but they require a large amount of time and access to a radiology expert. 

But Cartilage-X offers a new way of assessing knee injuries. The new tool uses AI-guided lesion detection to predict the location and severity of cartilage and bone lesions, reducing the amount of time and expertise needed to assess these injuries.

The team  that created this tool consists of graduate and undergraduate students from UC Berkeley. With the help of Python, Matlab and Tensorflow, the team used a data set of more than 1,800 patients’ knee MRIs and integrated with existing UCSF research to create an automated pipeline for MRI analysis. The pipeline ingests MRI images stored in its database, segments cartilage compartments, automatically preprocesses to include only signal of interest and predicts the presence of a cartilage or bone lesion using neural networks.

The team’s future work will focus on improving model performance, introducing an image quality control step before training and testing model generalizability on a fresh data set from a different cohort.

By providing insight into lesion location and severity, and doing so in under a minute, Cartilage-X brings quantitative metrics for knee MRIs into the clinic.

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