(ISMRM 2024) Meniscus tear detection on knee magnetic resonance images with U-net model
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Abstract #1635

Meniscus tear detection on knee magnetic resonance images with U-net model

Kensuke Yoshino1, Chanon Chantaduly1, Peter Chang1, and Hiroshi Yoshioka1
1The Center for Artificial Intelligence in Diagnostic Medicine, University of California Irvine, Irvine, CA, United States

Synopsis

Keywords: Other Musculoskeletal, Machine Learning/Artificial Intelligence, Meniscus

Motivation: Making an accurate diagnosis of meniscus tears from knee magnetic resonance imaging (MRI) is difficult.

Goal(s): To evaluate the accuracy of the 3D/2D deeply supervised U-net model for meniscus tears detection on knee MRI.

Approach: A total of 391 adult knee MRI scans were annotated in the tear regions of the menisci in coronal and sagittal images as ground truth. Tear detection was performed as a segmentation within the exam in the 679 test dataset.

Results: The accuracy of the coronal model and sagittal model were 0.76 and 0.74, respectively.

Impact: The diagnostic model for meniscus tears on knee magnetic resonance imaging might be useful as a screening tool and diagnostic aid for radiologists but further improvement is required for more accurate detection.

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Keywords

modelsagittalcoronal