Comprehensive Summary
The authors are studying the use of a deep learning model, MV2SwimNet, to detect knee disease, especially meniscal tears, from MRI scans. MV2SwimNet, a hybrid of previous deep learning models, was trained using two distinct data sets compiled from publicly available knee MRI exam data. Following a training period, MV2SwimNet was tested on both datasets using a 3 fold-cross validation to assess accuracy. The researchers found that the model produced an accuracy of 99.94% and 96.04% on datasets 1 and 2, respectively. A comparative analysis was performed to test MV2SwimNet’s abilities against existing models, and it was found that MV2SwimNet had the highest accuracy of any other pre-existing deep learning models, reflecting the impact the model may have on the field. The accuracy and low-computational cost renders MV2SwimNet a top choice in deep learning models predicting knee diseases.
Outcomes and Implications
Meniscal tears rely heavily on MRI scans for diagnosis, leading to the slow diagnosis times due to the reliance on human interpretation. Using deep learning models as a pre-clinical tool helps to deliver faster and more accurate knee disease detection, hopefully leading to a quicker time to diagnosis and lower secondary complication rate. Knee diseases are an extremely common condition for many adults living in the US, of which meniscal tears lead the charge. Many meniscal tears require surgical intervention, introducing the need for accurate and timely care for patients. Although no official timeline is mentioned for the integration of this model into the clinical setting, the authors mention MV2SwimNet's low-computational costs and possible future as a computer-aided diagnostic platform to enlighten clinicians on the applicability of this technology.