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Abstract #1687

Pre-Operative Evaluation of Patients Undergoing Knee Articular Cartilage Defect Repair: MRI Thickness Maps Derived from a Validated, Automated Segmentation Platform - Initial Results

Joshua Farber1, 2, Jose Tamez-Pena3, Saara Totterman4, Bruce Holladay5, Forest Heis5, John Larkin5, Michael Greiwe5

1Radiology, Radiology Ass of N KY, Cincinnati, OH, United States; 2Imaging, Qmetrics, Rochester, NY, United States; 3Mathematics and Statistics, Tec de Monterrey, Monterry, NL, Mexico; 4Qmetrics, Rchester, NY, United States; 5Orthopedic Surgery, Commonwealth Orthopeadic centers, Edgewood, KY, United States

The material presented here demonstrates a validated, automated knee articular cartilage segmentation platform that can create 3D Thickness maps of knee articular cartilage. These thickness maps delineate the location and size of articular cartilage defects, as well as the integrity of the surrounding cartilage, and provide an accurate guide for pre operative evaluation and facilitate physician - patient interactions.

Keywords

accuracy accurate accurately addition allocation allowing anatomical articular assess automated automatically axis biomedical bridging cartilage clinical clinically commonwealth confirmed date dedicated defect defects delineate delineates delineating depict depiction derived detect discussing displayed edits engineering ensure evaluation experience facility features femoral focus forest generated generates gold graft guide include initial initiative integrity interactions intra john knee lesion location made mapping maps material mathematics medial methodology miss necessary newsletter objective operative optima optimize options orthopedic osteoarthritis patient patients physician planning platform process proper prospectively quantification radiologist radiology repair required reviewed robust routine segmentation segmented segments sent serve serving sets source special station statistical statistics subsequent surgery surgical technologies template tool transactions treatment undergoing underwent unsupervised useful validate validated validation wall walls