Abstract

Magnetic resonance imaging (MRI) of the knee is the preferred method for diagnosing kneeinjuries. However, interpretation of knee MRI is time-intensive and subject to diagnosticerror and variability. An automated system for interpreting knee MRI could prioritize highrisk patients and assist clinicians in making diagnoses. Deep learning methods, in beingable to automatically learn layers of features, are well suited for modeling the complex relationships between medical images and their interpretations. In this study we developed adeep learning model for detecting general abnormalities and specific diagnoses (anteriorcruciate ligament [ACL] tears and meniscal tears) on knee MRI exams. We then measuredthe effect of providing the model’s predictions to clinical experts during interpretation.