Comparison of Evaluation Metrics of Deep Learning for Imbalanced Imaging Data in Osteoarthritis Studies

Objective To compare the evaluation metrics for deep learning methods in the imbalanced imaging data in osteoarthritis (OA) studies. Method We first divided MOAKS (MRI Osteoarthritis Knee Score) grades into the presence (MOAKS > 0) and absence (MOAKS = 0) categories. Second, a deep-learning model was trained to the sagittal intermediate-weighted (IW) fat-suppressed (FS) knee MRI images with MOAKS readings from the Osteoarthritis Initiative (OAI) study to predict the presence of bone marrow lesions (BMLs). After the deep learning models were trained, we obtained probabilities of the presence of BMLs from MRI images at the sub-region (15 sub-regions), compartment, and whole-knee levels. We compared different evaluation metrics (e.g., receiver operating characteristic (ROC) and precision-recall (PR) curves) of the deep learning model in the testing data with various class ratios (presence of BMLs vs. absence of BMLs) at these three data levels to assess the model’s performance. Results We have demonstrated that the commonly used ROC curve is not sufficiently informative when evaluating the performance of deep learning models in the imbalanced data in OA studies. Conclusion The class ratios coupled with results of ROC, PR, and Matthews correlation coefficient (MCC) should be reported in OA studies. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This study was funded by the NIH grants U19AG065169 and R01AR078187. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Osteoarthritis Initiative (OAI) I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes All data produced in the present study are available upon reasonable request to the authors