11 research outputs found

    Automatic quantification of radiographic knee osteoarthritis severity and associated diagnostic features using deep convolutional neural networks

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    ā€œAutomatic Quantification of Radiographic Knee Osteoarthritis Severity and Associated Diagnostic Features using Deep Convolutional Neural Networksā€ A. Joseph Antony Due to the increasing prevalence of knee Osteoarthritis (OA), a debilitating kneejoint degradation, and total joint arthoplasty as a serious consequence, there is a need for effective clinical and scientific tools to assess knee OA in its early stages. This thesis investigates the use of machine learning algorithms and deep learning architectures, in particular convolutional neural networks (CNN), to quantify the severity and clinical radiographic features of knee OA. The goal is to offer novel and effective solutions to automatically assess the severity of knee OA achieving on par with human accuracy. Instead of conventional hand-crafted features, it is proposed in this thesis that automatically learning features in a supervised manner can be more effective for fine-grained knee OA image classification. The main contributions of this thesis are as follows. First, the use of off-the-shelf CNNs are investigated for classifying knee OA images through transfer learning by fine-tuning the CNNs. Second, CNNs are trained from scratch to quantify the knee OA severity optimising a weighted ratio of two loss functions: categorical cross entropy and mean-squared error. Third, CNNs are jointly trained to quantify the clinical features of knee OA: joint space narrowing (JSN) and osteophytes along with the KL grades. This improves the overall quantification of knee OA severity producing simultaneous predictions of KL grades, JSN and osteophytes. Two public datasets are used to evaluate the approaches, the OAI and the MOST, with extremely promising results that outperform existing approaches. In summary, this thesis primarily contributes to the field of automated methods for localisation and quantification of radiographic knee OA

    Systems Radiology and Personalized Medicine

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    Medicine has evolved into a high level of specialization using the very detailed imaging of organs. This has impressively solved a multitude of acute health-related problems linked to single-organ diseases. Many diseases and pathophysiological processes, however, involve more than one organ. An organ-based approach is challenging when considering disease prevention and caring for elderly patients, or those with systemic chronic diseases or multiple co-morbidities. In addition, medical imaging provides more than a pretty picture. Much of the data are now revealed by quantitating algorithms with or without artificial intelligence. This Special Issue on ā€œSystems Radiology and Personalized Medicineā€ includes reviews and original studies that show the strengths and weaknesses of structural and functional whole-body imaging for personalized medicine

    Automated cortical thickness measurement of the mandibular condyle head on CBCT images using a deep learning method

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    This study proposes a deep learning model for cortical bone segmentation in the mandibular condyle head using cone-beam computed tomography (CBCT) and an automated method for measuring cortical thickness with a color display based on the segmentation results. In total, 12,800 CBCT images from 25 normal subjects, manually labeled by an oral radiologist, served as the gold-standard. The segmentation model combined a modified U-Net and a convolutional neural network for target region classification. Model performance was evaluated using intersection over union (IoU) and the Hausdorff distance in comparison with the gold standard. The second automated model measured the cortical thickness based on a three-dimensional (3D) model rendered from the segmentation results and presented a color visualization of the measurements. The IoU and Hausdorff distance showed high accuracy (0.870 and 0.928 for marrow bone and 0.734 and 1.247 for cortical bone, respectively). A visual comparison of the 3D color maps showed a similar trend to the gold standard. This algorithm for automatic segmentation of the mandibular condyle head and visualization of the measured cortical thickness as a 3D-rendered model with a color map may contribute to the automated quantification of bone thickness changes of the temporomandibular joint complex on CBCT.ope

    Patient-Specific Implants in Musculoskeletal (Orthopedic) Surgery

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    Most of the treatments in medicine are patient specific, arenā€™t they? So why should we bother with individualizing implants if we adapt our therapy to patients anyway? Looking at the neighboring field of oncologic treatment, you would not question the fact that individualization of tumor therapy with personalized antibodies has led to the thriving of this field in terms of success in patient survival and positive responses to alternatives for conventional treatments. Regarding the latest cutting-edge developments in orthopedic surgery and biotechnology, including new imaging techniques and 3D-printing of bone substitutes as well as implants, we do have an armamentarium available to stimulate the race for innovation in medicine. This Special Issue of Journal of Personalized Medicine will gather all relevant new and developed techniques already in clinical practice. Examples include the developments in revision arthroplasty and tumor (pelvic replacement) surgery to recreate individual defects, individualized implants for primary arthroplasty to establish physiological joint kinematics, and personalized implants in fracture treatment, to name but a few

    Changes in Femoral Structure and Function Following Anterior Cruciate Ligament Injury and with Aging

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    The ACL, a ligament connected to the distal femur, has little regenerative capacity. In consequence, surgical intervention is required if a patient hopes to remain active following ACL injury. In addition to the long recovery time and associated morbidities (e.g., osteoarthritis) following surgery, up to 12% of the primary reconstructed ACL grafts will fail within 15 years. Revision reconstructions are inferior to primary ACL reconstructions, thus, understanding the mechanism of failure is critical to mitigating worst-case outcomes. Reasons for revision risk have largely focused on technical errors despite that biological factors may also be a cause. Bone, a biological factor, decreases in mass following ACL injury. However, how bone microstructure changes following injury has remained largely unexplored. It was determined in this study that bone microstructure differs on a patient-by-patient basis undergoing ACL reconstructive surgery. Differences in microarchitecture could not be explained by time from injury to operation (i.e. time of disuse) or activity the patient was participating in at the moment of injury. Thus, differences in bone quality are due to variability present at baseline, in response to injury, and/or activity level following injury. Clinically, these findings are important because we are the first to show that bone quality varies across patient groups, pointing out that microstructure may be an important factor to consider in assessing ACL injury risk and surgical outcomes. The second half of this thesis compared age-related and sex-specific differences in bone microstructure to whole bone strength in the proximal femur with the long term goal of improving diagnostic methods to assess osteoporotic hip fracture risk. Hip fragility fractures are costly, associated with a severe decrease in the quality of life, and nearly half of patients (>65 years) who suffer a hip fracture never regain normal function. Unfortunately, approximately fifty percent of patients that experience a hip fracture receive no prophylactic treatment prior to fragility fracture because they are not diagnosed as osteoporotic using current clinical diagnostic methods. Both bone mass and microstructure change with age and the progression of osteoporosis. However, technical limitations have made it difficult to measure fracture risk from a biomechanical perspective - relating proximal femur bone strength and microstructure in synergy. The second study determined that the magnitude of sex-specific differences in bone strength was greater than age-related strength loss endured throughout life. Further, there was no sex-specific difference in the rate of loss observed herein. Clinically, these findings demonstrate that if females could maximize bone quality early in life, they may be able to maintain the structural strength later on, even with bone loss, to mitigate fragility fractures altogether. Further, mechanical variables (i.e., stiffness and post-yield-displacement) and demographic data (i.e., age and sex) could not adequately explain variability in whole bone strength. Microstructural analysis in the femoral neck improved our ability to predict whole bone strength but demonstrated that sub-regional microstructural detail only modestly improved strength predictability in comparison to average measures across the femoral neck. Despite this, we found that increased levels of micro-architectural detail are needed to identify sex-specific differences in whole bone strength. Clinically, these findings demonstrate that regional analysis may be useful for identifying those at greatest risk of fracture earlier in life and in a sex-specific manner.PHDBiomedical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/155081/1/pattondm_1.pd

    Exploring the Application of Wearable Movement Sensors in People with Knee Osteoarthritis

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    People with knee osteoarthritis have difficulty with functional activities, such as walking or get into/out of a chair. This thesis explored the clinical relevance of biomechanics and how wearable sensor technology may be used to assess how people move when their clinician is unable to directly observe them, such as at home or work. The findings of this thesis suggest that artificial intelligence can be used to process data from sensors to provide clinically important information about how people perform troublesome activities
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