Assessing knee OA severity with CNN attention-based end-to-end architectures

This work proposes a novel end-to-end convolutional neural network (CNN) architecture to automatically quantify the severity of knee osteoarthritis (OA) using X-Ray images, which incorporates trainable attention modules acting as unsupervised fine-grained detectors of the region of interest (ROI). The proposed attention modules can be applied at different levels and scales across any CNN pipeline helping the network to learn relevant attention patterns over the most informative parts of the image at different resolutions. We test the proposed attention mechanism on existing state-of-the-art CNN architectures as our base models, achieving promising results on the benchmark knee OA datasets from the osteoarthritis initiative (OAI) and multicenter osteoarthritis study (MOST).Postprint (published version

Assessing knee OA severity with CNN attention-based end-to-end architectures

This work proposes a novel end-to-end convolutional neural network (CNN) architecture to automatically quantify the severity of knee osteoarthritis (OA) using X-Ray images, which incorporates trainable attention modules acting as unsupervised fine-grained detectors of the region of interest (ROI). The proposed attention modules can be applied at different levels and scales across any CNN pipeline helping the network to learn relevant attention patterns over the most informative parts of the image at different resolutions. We test the proposed attention mechanism on existing state-of-the-art CNN architectures as our base models, achieving promising results on the benchmark knee OA datasets from the osteoarthritis initiative (OAI) and multicenter osteoarthritis study (MOST). All code from our experiments will be publicly available on the github repository: https://github.com/marc-gorriz/KneeOA-CNNAttentio

A review of arthritis diagnosis techniques in artificial intelligence era: Current trends and research challenges

Deep learning, a branch of artificial intelligence, has achieved unprecedented performance in several domains including medicine to assist with efficient diagnosis of diseases, prediction of disease progression and pre-screening step for physicians. Due to its significant breakthroughs, deep learning is now being used for the diagnosis of arthritis, which is a chronic disease affecting young to aged population. This paper provides a survey of recent and the most representative deep learning techniques (published between 2018 to 2020) for the diagnosis of osteoarthritis and rheumatoid arthritis. The paper also reviews traditional machine learning methods (published 2015 onward) and their application for the diagnosis of these diseases. The paper identifies open problems and research gaps. We believe that deep learning can assist general practitioners and consultants to predict the course of the disease, make treatment propositions and appraise their potential benefits

CartiMorph: a framework for automated knee articular cartilage morphometrics

We introduce CartiMorph, a framework for automated knee articular cartilage morphometrics. It takes an image as input and generates quantitative metrics for cartilage subregions, including the percentage of full-thickness cartilage loss (FCL), mean thickness, surface area, and volume. CartiMorph leverages the power of deep learning models for hierarchical image feature representation. Deep learning models were trained and validated for tissue segmentation, template construction, and template-to-image registration. We established methods for surface-normal-based cartilage thickness mapping, FCL estimation, and rule-based cartilage parcellation. Our cartilage thickness map showed less error in thin and peripheral regions. We evaluated the effectiveness of the adopted segmentation model by comparing the quantitative metrics obtained from model segmentation and those from manual segmentation. The root-mean-squared deviation of the FCL measurements was less than 8%, and strong correlations were observed for the mean thickness (Pearson's correlation coefficient $\rho \in [0.82,0.97]$), surface area ($\rho \in [0.82,0.98]$) and volume ($\rho \in [0.89,0.98]$) measurements. We compared our FCL measurements with those from a previous study and found that our measurements deviated less from the ground truths. We observed superior performance of the proposed rule-based cartilage parcellation method compared with the atlas-based approach. CartiMorph has the potential to promote imaging biomarkers discovery for knee osteoarthritis.Comment: To be published in Medical Image Analysi

Predicting knee osteoarthritis severity: comparative modeling based on patient's data and plain X-ray images

Knee osteoarthritis (KOA) is a disease that impairs knee function and causes pain. A radiologist reviews knee X-ray images and grades the severity level of the impairments according to the Kellgren and Lawrence grading scheme; a five-point ordinal scale (0-4). In this study, we used Elastic Net (EN) and Random Forests (RF) to build predictive models using patient assessment data (i.e. signs and symptoms of both knees and medication use) and a convolution neural network (CNN) trained using X-ray images only. Linear mixed effect models (LMM) were used to model the within subject correlation between the two knees. The root mean squared error for the CNN, EN, and RF models was 0.77, 0.97 and 0.94 respectively. The LMM shows similar overall prediction accuracy as the EN regression but correctly accounted for the hierarchical structure of the data resulting in more reliable inference. Useful explanatory variables were identified that could be used for patient monitoring before X-ray imaging. Our analyses suggest that the models trained for predicting the KOA severity levels achieve comparable results when modeling X-ray images and patient data. The subjectivity in the KL grade is still a primary concern

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