Machine learning in orthopedics: a literature review

In this paper we present the findings of a systematic literature review covering the articles published in the last two decades in which the authors described the application of a machine learning technique and method to an orthopedic problem or purpose. By searching both in the Scopus and Medline databases, we retrieved, screened and analyzed the content of 70 journal articles, and coded these resources following an iterative method within a Grounded Theory approach. We report the survey findings by outlining the articles\u2019 content in terms of the main machine learning techniques mentioned therein, the orthopedic application domains, the source data and the quality of their predictive performance

퇴행성 슬 관절염의 객관적 평가를 위한 기계학습 기반의 보행 데이터 분석 연구

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 협동과정 바이오엔지니어링전공, 2020. 8. 김희찬.Osteoarthritis (OA) is a disease that affects above 30% of the elderly population aged 60 years and older. Western Ontario and McMaster Osteoarthritis (WOMAC) and radiographic-based Kellgren–Lawrence (KL) grade methods are currently used to evaluate the severity of knee osteoarthritis (KOA). However, the WOMAC is a subjective method which cannot be performed to certain patients, and is not suitable for tracking changes in severity over time. KL grade requires highly trained experts and is a time consuming process. This dissertation hypothesized that objective and biomechanical gait data can supplement unmet needs of current gold standard. It was hypothesized that specific features from gait data would reflect the severity of KOA. Therefore, this study aims to identify key gait features associated with the severity of KOA and provide a new objective and explainable evaluation method for KOA based on gait analysis. Features were extracted from the gait signal and an automated severity evaluation model was designed based on machine learning technique for WOMAC severity evaluation model. To develop an automated severity evaluation algorithm for KL grade, features were extracted from the plain radiography image using deep learning network, and machine learning was applied to select features from the gait data. Both image and gait features were used to develop a machine learning algorithm for KL grade evaluation. The evaluation algorithm for WOMAC and KL grade showed a correlation of 0.741 and an accuracy of 75.2% with gold standard method, respectively. This dissertation proposed a new evaluation method for KOA and showed the clinical utility of the gait data application that was limited in clinical practice due to the complexity of the signal.퇴행성 관절염은 60세 이상의 노인 인구 약 30%에서 발병하는 질병이다. 현재 퇴행성 슬 관절염의 진단은 Western Ontario and McMaster Osteoarthritis (WOMAC) 방법과 방사선 촬영 기반의 Kellgren–Lawrence (KL) grade 방법이 사용되고 있다. 그러나 WOMAC 환자의 주관적인 판단을 토대로 중증도를 정량화하는 방법이어서 일부 환자들에게 적용이 불가능하고, 수술 후의 중증도를 반영하지 못한다는 단점이 있다. KL grade은 고도로 훈련된 전문가를 필요로 하며, 정확한 진단을 위하여서는 많은 시간을 필요로 한다. 반면 보행 신호는 환자의 보행에 따른 객관적인 생체 역학 신호를 제공하며, 보행이 가능한 모든 사람에게 적용이 가능하며, 주기적인 추적 관찰에 용의하다. 따라서 본 연구는 보행 신호를 이용하여 객관적이며, 결과에 대한 생체 역학적 이유를 알 수 있는 퇴행성 슬 관절염의 새로운 분석 방법을 제시함에 있다. 먼저 자동으로 WOMAC 방법을 진단하기 위해 보행신호에서 특징들을 추출하고 기계학습 기법을 이용하여 평가하는 모델을 개발하였다. 또한 KL grade 방법을 평가하기 위해 방사선 영상에서 딥러닝 알고리즘으로 추출한 특징들과 보행신호에서 추출한 특징들을 기계학습 기법을 이용하였다. 제안하는 퇴행성 슬 관절염의 평가 방법은 WOMAC 및 KL grade 방법과 각각 상관관계 0.741, 정확도 75.2%를 보였다. 본 연구는 퇴행성 슬 관절염의 새로운 평가 방법을 제시하였으며, 신호의 복잡성으로 인하여 임상에서 사용되지 못했던 보행 신호의 임상적 활용성을 보여주었다.1. Introduction 1 1.1. Knee Osteoarthritis 2 1.2. Severity Evaluation of Knee Osteoarthritis 4 1.2.1. Symptomatic Severity evaluation 4 1.2.2. Structural Severity evaluation 5 1.3. Unmet Clinical Needs 7 1.4. Gait analysis and KOA 8 1.5. Thesis objectives 12 2. Symptomatic Severity of Knee Osteoarthritis 14 2.1. Introduction 15 2.2. Methods 18 2.2.1. Participants 18 2.2.2. Gait Data Collection 20 2.2.3. Statistical Analysis and WOMAC Estimation Model 21 2.3. Results 25 2.4. Discussion 34 2.5. Conclusion 41 3. Structural Severity of Knee Osteoarthritis 42 3.1. Introduction 43 3.2. Methods 49 3.2.1. Participants 49 3.2.2. Gait Data Collection 52 3.2.3. Radiographic Assessment 53 3.2.4. Feature Extraction and Classification 54 3.3. Results 62 3.3.1. Feature Analysis 62 3.3.2. Deep Learning Approach Based on Radiographic Images 72 3.3.3. Proposed Model Based on Gait Data and Radiographic Images 74 3.4. Discussion 76 3.5. Conclusion 83 4. Conclusion 84 4.1. Thesis Summary and Contributions 85 4.2. Future Direction 87 Bibliography 89 Abstract in Korean 98Docto

Footwear-integrated force sensing resistor sensors: A machine learning approach for categorizing lower limb disorders

Lower limb disorders are a substantial contributor to both disability and lower standards of life. The prevalent disorders affecting the lower limbs include osteoarthritis of the knee, hip, and ankle. The present study focuses on the use of footwear that incorporates force-sensing resistor sensors to classify lower limb disorders affecting the knee, hip, and ankle joints. The research collected data from a sample of 117 participants who wore footwear integrated with force-sensing resistor sensors while walking on a predetermined walkway of 9 meters. Extensive preprocessing and feature extraction techniques were applied to form a structured dataset. Several machine learning classifiers were trained and evaluated. According to the findings, the Random Forest model exhibited the highest level of performance on the balanced dataset with an accuracy rate of 96%, while the Decision Tree model achieved an accuracy rate of 91%. The accuracy scores of the Logistic Regression, Gaussian Naive Bayes, and Long Short-Term Memory models were comparatively lower. K-fold cross-validation was also performed to evaluate the models’ performance. The results indicate that the integration of force-sensing resistor sensors into footwear, along with the use of machine learning techniques, can accurately categorize lower limb disorders. This offers valuable information for developing customized interventions and treatment plans

Machine Learning in Orthopedics: A Literature Review

In this paper we present the findings of a systematic literature review covering the articles published in the last two decades in which the authors described the application of a machine learning technique and method to an orthopedic problem or purpose. By searching both in the Scopus and Medline databases, we retrieved, screened and analyzed the content of 70 journal articles, and coded these resources following an iterative method within a Grounded Theory approach. We report the survey findings by outlining the articles' content in terms of the main machine learning techniques mentioned therein, the orthopedic application domains, the source data and the quality of their predictive performance

Predictions of Knee Joint Contact Forces Using Only Kinematic Inputs with a Recurrent Neural Network

BACKGROUND: Knee joint contact (bone on bone) forces are commonly estimated using surrogate measures such as external knee adduction moments (with limited success) or musculoskeletal modeling (more successful). Despite its capabilities, modeling is not optimal for clinicians or persons with limited experience and knowledge. Therefore, the purpose of this study was to design a novel prediction method for knee joint contact forces that is equal or more accurate than modeling, yet simplistic in terms of required inputs. METHODS: This study included all six subjects’ (71.3±6.5kg, 1.7±0.1m) data from the opensource “Grand Challenge” datasets (simtk.org) and two subjects from the CAMS datasets, consisting of motion capture and in-vivo instrumented knee prosthesis data (e.g. true knee joint contact forces). Inverse kinematics were used to derive three-dimensional hip, two-dimensional knee (sagittal & frontal), and one-dimensional ankle (sagittal) kinematics during the stance phase of normal walking for all subjects. Medial and lateral knee joint contact forces (normalized to body weight) and inverse kinematics were imported into MATLAB and normalized to 101 data points. A long-short term memory network (LSTM) was created to predict knee forces using combinations of the kinematics inputs. The Grand Challenge data were used for training, while the CAMS data were used for testing. Waveform accuracy was explained by the proportion of variance and root mean square error between network predictions and in-vivo knee joint contact forces data. RESULTS: The top five networks demonstrated excellent fit with the training data, achieving RMSE \u3c 0.26BW for medial and lateral forces, R2 \u3e 0.69 for medial forces, but only R2 \u3e 0.15 for lateral forces. The overall best-selected network contained frontal hip and knee, and sagittal hip and ankle input variables and presented the finest visual waveform agreement with the in vivo data (R2=0.77, RMSE=0.27). CONCLUSIONS: The LSTM network designed in this study revealed knee joint forces could accurately be predicted by using only kinematic input variables. The network’s results outperformed most reports of root mean squared errors and correlation coefficients attained by musculoskeletal modeling and surrogate measures of KAMs

Analysis, Segmentation and Prediction of Knee Cartilage using Statistical Shape Models

Osteoarthritis (OA) of the knee is one of the leading causes of chronic disability (along with the hip). Due to rising healthcare costs associated with OA, it is important to fully understand the disease and how it progresses in the knee. One symptom of knee OA is the degeneration of cartilage in the articulating knee. The cartilage pad plays a major role in painting the biomechanical picture of the knee. This work attempts to quantify the cartilage thickness of healthy male and female knees using statistical shape models (SSMs) for a deep knee bend activity. Additionally, novel cartilage segmentation from magnetic resonance imaging (MRI) and estimation algorithms from computer tomography (CT) or x-rays are proposed to facilitate the efficient development and accurate analysis of future treatments related to the knee. Cartilage morphology results suggest distinct patterns of wear in varus, valgus, and neutral degenerative knees, and examination of contact regions during the deep knee bend activity further emphasizes these patterns. Segmentation results were achieved that were comparable if not of higher quality than existing state-of-the-art techniques for both femoral and tibial cartilage. Likewise, using the point correspondence properties of SSMs, estimation of articulating cartilage was effective in healthy and degenerative knees. In conclusion, this work provides novel, clinically relevant morphological data to compute segmentation and estimate new data in such a way to potentially contribute to improving results and efficiency in evaluation of the femorotibial cartilage layer

Grading the Severity and Progression of Knee Osteoarthritis with Deep Learning Methods

Knee osteoarthritis (KOA) is a highly prevalent form of arthritis and a leading cause of physical disability, given the growing aging population. To assist these KOA assessments, there is a demanding interest in computer-aided grading algorithms. To explore better KOA patterns and overcome the dataset limitation, a self-supervised multi-modal method is studied. In addition, current works in predicting the progression of KOA only produce a predicted longitude severity grade, where the visual contents are ignored. To include the predicted visual information for the future period, the generative methods are researched for comprehensive prognosis. To this end, the visual progression trajectory is involved in building a multimodal prediction network. Specifically, the major contributions of this thesis are as follows:Firstly, a novel Self-supervised Multimodal Fusion Network (S-MFN) is proposed for multimodal unsupervised knee OA grading with X-ray and magnetic resonance imaging (MRI) modalities. To this end, multimodal contrastive learning is introduced in a self-supervised manner through modalityspecific and cross-modal modelling. Secondly, an Identity-Consistent Radiographic Diffusion Network (IC-RDN) is introduced for Knee OA prognosis that predicts the X-ray images for longitude medical imaging tests. In particular, an imageto-image diffusion model backbone and an identity consistency component are introduced to generate knee joint X-rays with persisting the patients' identity information. The generated X-rays have been approved that are beneficial to the prognosis. Comprehensive experimental results on the widely used dataset, Osteoarthritis Initiative (OAI), demonstrate the effectiveness of the proposed methods, where the multimodal KOA patterns are analysed and explored under the scenarios of multi-modalities on medical imaging modalities (i.e., Xray and MRI) and clinical diagnosis results (i.e., severity assessment and visual images)

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

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

Knee osteoarthritis is a bilateral disease

Knee osteoarthritis (OA) is a common cause of pain and disability. Patients often complain that they overload the other limb when they walk, resulting in disease in the unaffected knee. However, it is unknown whether this happens or the mechanism by which it occurs. Data was analysed from an established longitudinal cohort study to examine the development of bilateral knee OA. One hundred and forty-three subjects were examined over a 12 year period with bilateral radiographs. Bilateral knee osteoarthritis was found to be very common over time, and the majority of individuals with unilateral knee OA eventually developed bilateral disease. A gait analysis study was performed on 20 subjects with unilateral knee OA awaiting arthroplasty surgery and 20 healthy age equivalent controls. Abnormal moments and muscle co-contractions were observed in the other knee and hips when they walked due a characteristic slow, cautious, stiff-legged gait pattern. Fifteen subjects re-attended 12 months following their surgery. Whilst moments returned to normal in most of the replaced knees, they remained elevated at the contra-lateral side and co-contraction failed to recover in either knee. A novel study design is presented to examine the effect of gait-derived loading waveforms on fresh human osteochondral plugs. By applying mechano-biology techniques and Finite Element Modelling to fresh human tissue, new observations vi can be made about the relationship between in-vivo loading and cartilage mechano-biology. A characteristic gait pattern was observed in knee OA which is not simply antalgic but tends towards symmetry, with an increase in joint loading bilaterally. The observed gait behaviour does not resolve, despite arthroplasty of the affected joint. This would be expected to contribute to the development of disease in an inherently vulnerable joint. Additional training may have a role to play in restoring normal biomechanics and protecting the other knee from disease