Deep-learning-assisted diagnosis for knee magnetic resonance imaging: Development and retrospective validation of MRNet
Magnetic resonance imaging (MRI) of the knee is the preferred method for diagnosing knee injuries. However, interpretation of knee MRI is time-intensive and subject to diagnostic error and variability. An automated system for interpreting knee MRI could prioritize highrisk patients and assist clinicians in making diagnoses. Deep learning methods, in being able 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 a deep learning model for detecting general abnormalities and specific diagnoses (anterior cruciate ligament [ACL] tears and meniscal tears) on knee MRI exams. We then measured the effect of providing the model’s predictions to clinical experts during interpretation.
PDF AbstractCode
ImageNet
| Task | Dataset | Model | Metric Name | Metric Value | Global Rank | Benchmark |
|---|---|---|---|---|---|---|
| Multi-Label Classification | MRNet | MRNet | Average AUC | 0.894 | # 1 | |
| AUC on Abnormality (ABN) | 0.944 | # 1 | ||||
| AUC on ACL Tear (ACL) | 0.915 | # 1 | ||||
| AUC on Meniscus Tear (MEN) | 0.822 | # 1 | ||||
| Average Accuracy | 0.814 | # 1 | ||||
| Accuracy on Abnormality (ABN) | 0.850 | # 2 | ||||
| Accuracy on ACL Tear (ACL) | 0.867 | # 1 | ||||
| Accuracy on Meniscus Tear (MEN) | 0.725 | # 1 |
