Editorial for “Automatic Detection of Meniscus Tears Using Backbone Convolutional Neural Networks on Knee MRI”

Editorial for “Automatic Detection of Meniscus Tears Using Backbone Convolutional Neural Networks on Knee MRI” Meniscal tears: The meniscus is a tough rubbery cartilage in the knee joint that acts as a shock absorber. Meniscal tears occur when one or more of the fibrocartilage strips rupture, which commonly occurs in athletes, heavy weight active workers, and older subjects. Injuries occur in one or more of the fibrocartilage strips. Joint line tenderness and a positive McMurray’s test support the diagnosis of meniscal tears. Early diagnosis and treatment are crucial for preventing complications, such as knee dysfunction, saphenous neuropathy, arthrofibrosis, sterile effusion, and infections. Treatment can be nonsurgical (rest, physical therapy) or surgical (partial meniscectomy, meniscal repair, meniscal transplantation), depending on multiple factors, namely extent of the damage, cartilage root involvement, and patient’s tolerance. Diagnosis: Meniscal tears are not visible on plain radiographs. Likewise, ultrasound has limited use as a diagnostic technique for meniscal injuries. Arthrography was the reference diagnostic tool in the 1970s and 1980s and then replaced by MRI. Since its introduction to clinical practice in late 1980s, MRI is a proven, highly accurate modality for detecting meniscal injuries, with excellent arthroscopic correlation. MRI advantages include analysis of meniscal lesions on three-dimensional (3D) planes with high resolution and contrast. Its multiparametric characteristics allow visualization of almost all structures depending on the sequences chosen. T1and T2-weighted spin echo sequences are routinely used in characterizing meniscal tears. T1-weighted sequences showed hyperintense lines in meniscal tears, but it is limited in differentiating cartilage degeneration from tears. Similarly, T2-weighted images showed hyperintense line in meniscal tears indicative of synovial fluid accumulation. It has been shown that 3D isotropic turbo spinecho MRI improves the diagnosis of meniscal tears by creating multiplanar reformatted images from a single-plane acquisition. The emerging science of deep learning-based image provides additional information. Deep learning-based image enhancement models: Deep learning-based image enhancement models attempt to simulate how human brains learn and process information by creating artificial “neural networks” that can extract concepts and produce better insights and predictions. It consists of a set of simple processing units, artificial neurons, connected in a network, organized in layers, and trained with a backpropagation algorithm. To date, published clinical studies have provided evidence that deep neural networks achieve excellent performance in enhancing image contrast of patients with various types of pathologies, such as breast cancers, pulmonary tuberculosis, and cardiovascular diseases. Therefore, radiologists and surgeons are focusing on deep learning for characterizing injuries of knee ligaments and menisci. They have observed that deep learning-based image enhancement models improve the imaging efficiency, image analysis process, and interpretation. Mehranian et al also indicated that deep learning-based image enhancement models allow a reduction in scan time by up to 50% and can decrease reconstruction time to a third, while maintaining the image quality. It is useful in reducing the dose of MR contrast agents on cardiovascular MR angiography. Health care workers confirmed that deep learning networks could be a valuable tool in improving workflow and workforce efficiency. The study in this issue of JMRI, Hung et al reported the application of a semi-automatic deep learning MRI-based model for characterizing meniscus injuries using the following criteria: 1) compactness, 2) lightness, and 3) fast processing speed. Hung et al found that the semi-automatic deep learning MRIbased technique matches surgical results. The described model eliminates manual sketching of the lesion on each image as input for the classification model. It also provides high diagnostic accuracy and exact location and orientation of meniscus injuries. The investigators demonstrated a close relationship between internal and external datasets and a lack of significant difference in K-values. Technically, the strength of the study lies in the use of a relatively large number of MRI imaged subjects. Furthermore, the model demonstrated excellent diagnostic performance on standard T1 and T2 images. Based on the high accuracy, the investigators recommended the use of T1over T2-weighted sequence and coronal over