  Background: MRI is the modality of choice for cartilage imaging; however, its
diagnostic performance is variable and significantly lower than the gold
standard diagnostic knee arthroscopy. In recent years, deep learning has been
used to automatically interpret medical images to improve diagnostic accuracy
and speed. Purpose: The primary purpose of this study was to evaluate whether
deep learning applied to the interpretation of knee MRI images can be utilized
to identify cartilage defects accurately. Methods: We analyzed data from
patients who underwent knee MRI evaluation and consequently had arthroscopic
knee surgery (207 with cartilage defect, 90 without cartilage defect).
Patients' arthroscopic findings were compared to preoperative MRI images to
verify the presence or absence of isolated tibiofemoral cartilage defects. We
developed three convolutional neural networks (CNNs) to analyze the MRI images
and implemented image-specific saliency maps to visualize the CNNs'
decision-making process. To compare the CNNs' performance against human
interpretation, the same test dataset images were provided to an experienced
orthopaedic surgeon and an orthopaedic resident. Results: Saliency maps
demonstrated that the CNNs learned to focus on the clinically relevant areas of
the tibiofemoral articular cartilage on MRI images during the decision-making
processes. One CNN achieved higher performance than the orthopaedic surgeon,
with two more accurate diagnoses made by the CNN. All the CNNs outperformed the
orthopaedic resident. Conclusion: CNN can be used to enhance the diagnostic
performance of MRI in identifying isolated tibiofemoral cartilage defects and
may replace diagnostic knee arthroscopy in certain cases in the future.
