Deep Learning for Lesion Detection, Progression, and Prediction of Musculoskeletal Disease

Abstract

Deep learning is one of the most exciting new areas in medical imaging. This review article provides a summary of the current clinical applications of deep learning for lesion detection, progression, and prediction of musculoskeletal disease on radiographs, computed tomography (CT), magnetic resonance imaging (MRI), and nuclear medicine. Deep-learning methods have shown success for estimating pediatric bone age, detecting fractures, and assessing the severity of osteoarthritis on radiographs. In particular, the high diagnostic performance of deep-learning approaches for estimating pediatric bone age and detecting fractures suggests that the new technology may soon become available for use in clinical practice. Recent studies have also documented the feasibility of using deep-learning methods for identifying a wide variety of pathologic abnormalities on CT and MRI including internal derangement, metastatic disease, infection, fractures, and joint degeneration. However, the detection of musculoskeletal disease on CT and especially MRI is challenging, as it often requires analyzing complex abnormalities on multiple slices of image datasets with different tissue contrasts. Thus, additional technical development is needed to create deep-learning methods for reliable and repeatable interpretation of musculoskeletal CT and MRI examinations. Furthermore, the diagnostic performance of all deep-learning methods for detecting and characterizing musculoskeletal disease must be evaluated in prospective studies using large image datasets acquired at different institutions with different imaging parameters and different imaging hardware before they can be implemented in clinical practice. Level of Evidence: 5 Technical Efficacy Stage: 2

Improving Quantitative Magnetic Resonance Imaging Using Deep Learning

“…There has been much recent interest in applying deep learning to a wide variety of imaging applications. Although surveys on deep learning in medical image analysis have shown emerging applications in MSK imaging, 19,30 the use of deep learning in quantitative MSK imaging remains somewhat limited. Deep learning methods have shown promising results for accelerating quantitative MSK MRI for T2 and T1ρ relaxometry, which might allow the incorporation of such imaging techniques into clinical practice.…”

“…Significant interest has been expressed recently in using deep learning to perform lesion detection, progression, and prediction of MSK disease on X-ray radiography, CT, and MRI. 19 Many pioneer studies have shown the potential of deep learning to maximize diagnostic performance while reducing subjectivity and errors induced by human interpretation. For example, Liu et al developed a fully automatic cartilage lesion detection system using a cascaded deep learning algorithm to perform cartilage segmentation followed by localized cartilage lesion detection on MRI.…”

“…A comprehensive summary of reported studies can be found in a recent review article. 19 Although most of these studies focused on the use of deep learning to detect tissue abnormality with anatomical MR images, only very limited research has attempted to utilize the information from quantitative images. One pioneer study by Pedoia et al investigated the diagnostic difference of distinguishing knees with and without radiographic OA using conventional and deep learning-based T2 relaxometry measures.…”

“…[16][17][18] More recently, the use of deep learning for lesion detection, progression, and prediction of MSK disease on X-ray radiography, computed tomography (CT), and magnetic resonance (MR) imaging has further demonstrated the potential of deep learning to maximize diagnostic performance while reducing subjectivity and errors induced by human interpretation. 19 Another important image subspecialty in MSK imaging is quantitative imaging, which is less studied, however, and remains very limited to the use of deep learning. Quantitative mapping of MR parameters, such as the spin-spin relaxation time (T2) and the spin-lattice relaxation time in the rotating frame (T1ρ), were shown to be valuable methods for the improved assessment of a range of MSK diseases.…”

“…[25][26][27][28][29] A few recent review articles focused on the use of deep learning for MSK image reconstruction, 30 tissue segmentation, and disease diagnosis and prediction. 19 This article reviews the current deep learning applications with a primary focus on the improvement of quantitative imaging using deep learning and the use of deep learning for quantitative imaging analysis in MSK imaging.…”