Knee osteoarthritis (OA) is one of the highest disability factors in the world. This musculoskeletal disorder is assessed from clinical symptoms, and typically confirmed via radiographic assessment. This visual assessment done by a radiologist requires experience, and suffers from moderate to high inter-observer variability. The recent literature has shown that deep learning methods can reliably perform the OA severity assessment according to the gold standard Kellgren-Lawrence (KL) grading system. However, these methods require large amounts of labeled data, which are costly to obtain. In this study, we propose the Semixup algorithm, a semi-supervised learning (SSL) approach to leverage unlabeled data. Semixup relies on consistency regularization using in- and out-of-manifold samples, together with interpolated consistency. On an independent test set, our method significantly outperformed other state-of-the-art SSL methods in most cases. Finally, when compared to a well-tuned fully supervised baseline that yielded a balanced accuracy (BA) of $70.9\pm0.8%$ on the test set, Semixup had comparable performance -- BA of $71\pm0.8%$ $(p=0.368)$ while requiring $6$ times less labeled data. These results show that our proposed SSL method allows building fully automatic OA severity assessment tools with datasets that are available outside research settings.
University of Oulu（奥卢大学）；Oulu University Hospital（奥卢大学医院）；Medical Research Center, University of Oulu and Oulu University Hospital（奥卢大学与奥卢大学医院医学研究中心）
Knee osteoarthritis (OA) is very common progressive and degenerative musculoskeletal disease worldwide creates a heavy burden on patients with reduced quality of life and also on society due to financial impact. Therefore, any attempt to reduce the burden of the disease could help both patients and society. In this study, we propose a fully automated novel method, based on combination of joint shape and convolutional neural network (CNN) based bone texture features, to distinguish between the knee radiographs with and without radiographic osteoarthritis. Moreover, we report the first attempt a…
University of Oulu（奥卢大学）；Oulu University Hospital（奥卢大学医院）
Knee osteoarthritis (OA) is the most common musculoskeletal disease in the world. In primary healthcare, knee OA is diagnosed using clinical examination and radiographic assessment. Osteoarthritis Research Society International (OARSI) atlas of OA radiographic features allows to perform independent assessment of knee osteophytes, joint space narrowing and other knee features. This provides a fine-grained OA severity assessment of the knee, compared to the gold standard and most commonly used Kellgren-Lawrence (KL) composite score. However, both OARSI and KL grading systems suffer from moderate…
The severity of knee osteoarthritis is graded using the 5-point Kellgren-Lawrence (KL) scale where healthy knees are assigned grade 0, and the subsequent grades 1-4 represent increasing severity of the affliction. Although several methods have been proposed in recent years to develop models that can automatically predict the KL grade from a given radiograph, most models have been developed and evaluated on datasets not sourced from India. These models fail to perform well on the radiographs of Indian patients. In this paper, we propose a novel method using convolutional neural networks to auto…
University of Oulu（奥卢大学）；Oulu University Hospital（奥卢大学医院）；Medical Research Center, University of Oulu and Oulu University Hospital（奥卢大学与奥卢大学医院医学研究中心）
Objective: To assess the ability of imaging-based deep learning to predict radiographic patellofemoral osteoarthritis (PFOA) from knee lateral view radiographs. Design: Knee lateral view radiographs were extracted from The Multicenter Osteoarthritis Study (MOST) (n = 18,436 knees). Patellar region-of-interest (ROI) was first automatically detected, and subsequently, end-to-end deep convolutional neural networks (CNNs) were trained and validated to detect the status of patellofemoral OA. Patellar ROI was detected using deep-learning-based object detection method. Manual PFOA status assessment p…
VRVis Center for Virtual Reality and Visualization（VRVis虚拟现实与可视化中心）；Braincon Handels-GmbH（Braincon贸易有限公司）；NOVA Medical School, Universidade Nova de Lisboa（新里斯本大学NOVA医学院）；Instituto de Medicina Molecular（分子医学研究所）；Centro Hospitalar Lisboa Ocidental（里斯本西部医疗中心）
The relationship between knee osteoarthritis progression and changes in tibial bone structure has long been recognized and various texture descriptors have been proposed to detect early osteoarthritis (OA) from radiographs. This work aims to investigate (1) femoral textures as an OA indicator and (2) the potential of entropy as a computationally efficient alternative to established texture descriptors. We design a robust semi-automatically placed layout for regions of interest (ROI), compute the Hurst coefficient and the entropy in each ROI, and employ statistical and machine learning methods …
Insight Centre for Data Analytics, Dublin City University（都柏林城市大学数据洞察中心）；School of Health and Human Performance, Dublin City University（都柏林城市大学健康与人体表现学院）
This paper proposes a new approach to automatically quantify the severity of knee osteoarthritis (OA) from radiographs using deep convolutional neural networks (CNN). Clinically, knee OA severity is assessed using Kellgren \& Lawrence (KL) grades, a five point scale. Previous work on automatically predicting KL grades from radiograph images were based on training shallow classifiers using a variety of hand engineered features. We demonstrate that classification accuracy can be significantly improved using deep convolutional neural network models pre-trained on ImageNet and fine-tuned on knee O…
Insight Centre for Data Analytics（洞察数据分析中心）；Dublin City University（都柏林城市大学）
This chapter presents the investigations and the results of feature learning using convolutional neural networks to automatically assess knee osteoarthritis (OA) severity and the associated clinical and diagnostic features of knee OA from X-ray images. Also, this chapter demonstrates that feature learning in a supervised manner is more effective than using conventional handcrafted features for automatic detection of knee joints and fine-grained knee OA image classification. In the general machine learning approach to automatically assess knee OA severity, the first step is to localize the regi…
Deep Learning for Predicting Progression of Patellofemoral Osteoarthritis Based on Lateral Knee Radiographs, Demographic Data and Symptomatic Assessments
University of Oulu（奥卢大学）；Lund University（隆德大学）；Diakonhjemmet Hospital（迪亚科涅米特医院）；Juntendo University（顺天堂大学）；Oulu University Hospital（奥卢大学医院）
University of Oulu（奥卢大学）；Tampere University of Technology（坦佩雷理工大学）；Oulu University Hospital（奥卢大学医院）
Knee osteoarthritis (OA) is the most common musculoskeletal disorder. OA diagnosis is currently conducted by assessing symptoms and evaluating plain radiographs, but this process suffers from subjectivity. In this study, we present a new transparent computer-aided diagnosis method based on the Deep Siamese Convolutional Neural Network to automatically score knee OA severity according to the Kellgren-Lawrence grading scale. We trained our method using the data solely from the Multicenter Osteoarthritis Study and validated it on randomly selected 3,000 subjects (5,960 knees) from Osteoarthritis …
University of Oulu（奥卢大学）；Oulu University Hospital（奥卢大学医院）；Medical Research Center（医学研究中心）
Objective is to assess the ability of texture features for detecting radiographic patellofemoral osteoarthritis (PFOA) from knee lateral view radiographs. We used lateral view knee radiographs from MOST public use datasets (n = 5507 knees). Patellar region-of-interest (ROI) was automatically detected using landmark detection tool (BoneFinder). Hand-crafted features, based on LocalBinary Patterns (LBP), were then extracted to describe the patellar texture. First, a machine learning model (Gradient Boosting Machine) was trained to detect radiographic PFOA from the LBP features. Furthermore, we u…
University of Oulu（奥卢大学）；Oulu University Hospital（奥卢大学医院）；Erasmus MC, University Medical Center Rotterdam（伊拉斯谟医学中心，鹿特丹大学医学中心）；University Medical Center Rotterdam（鹿特丹大学医学中心）；Tampere University of Technology（坦佩雷理工大学）
Knee osteoarthritis (OA) is the most common musculoskeletal disease without a cure, and current treatment options are limited to symptomatic relief. Prediction of OA progression is a very challenging and timely issue, and it could, if resolved, accelerate the disease modifying drug development and ultimately help to prevent millions of total joint replacement surgeries performed annually. Here, we present a multi-modal machine learning-based OA progression prediction model that utilizes raw radiographic data, clinical examination results and previous medical history of the patient. We validate…
Compal Electronics（仁宝电脑）；Chang Gung Memorial Hospital（长庚纪念医院）；Chang Gung University（长庚大学）
Purpose Automated detection of region of interest (ROI) is a critical step for many medical image applications such as heart ROIs detection in perfusion MRI images, lung boundary detection in chest X-rays, and femoral head detection in pelvic radiographs. Thus, we proposed a practical framework of ROIs detection in medical images, with a case study for hip detection in anteroposterior (AP) pelvic radiographs. Materials and Methods: We conducted a retrospective study which analyzed hip joints seen on 7,399 AP pelvic radiographs from three diverse sources, including 4,290 high resolution radiogr…
A Progressive Risk Formulation for Enhanced Deep Learning based Total Knee Replacement Prediction in Knee Osteoarthritis
Center for Data Science, New York University（纽约大学数据科学中心）；Department of Radiology, New York University Langone Health（纽约大学朗格尼健康放射科）；Bernard and Irene Schwartz Center for Biomedical Imaging, New York University Langone Health（纽约大学朗格尼健康伯纳德与艾琳·施瓦茨生物医学影像中心）
Knee-xRAI: An Explainable AI Framework for Automatic Kellgren-Lawrence Grading of Knee Osteoarthritis
Orthopaedic Department, Faculty of Medicine UIN Syarif Hidayatullah Jakarta（乌姆尼大学医学学院骨科部）；Informatics Engineering, Institut Teknologi Sepuluh Nopember（十月份技术研究所信息工程系）；Information Technology, Universitas Muhammadiyah Yogyakarta（尤科阿卡塔大学信息技术系）；Industrial and Systems Engineering, King Fahd University of Petroleum and Minerals（国王法赫德石油与矿物大学工业与系统工程系）
University of Oulu（奥卢大学）；Oulu University Hospital（奥卢大学医院）
Osteoarthritis (OA) is a common musculoskeletal condition typically diagnosed from radiographic assessment after clinical examination. However, a visual evaluation made by a practitioner suffers from subjectivity and is highly dependent on the experience. Computer-aided diagnostics (CAD) could improve the objectivity of knee radiographic examination. The first essential step of knee OA CAD is to automatically localize the joint area. However, according to the literature this task itself remains challenging. The aim of this study was to develop novel and computationally efficient method to tack…
Self-Supervised Learning for Knee Osteoarthritis: Diagnostic Limitations and Prognostic Value of Hospital Data
Department of Physics, J.K. Institute of Science（物理系，J.K. 科学研究所）；World Scientific University（世界科学大学）；University of Intelligent Studies（智能研究大学）；Center for Data Science, New York University（数据科学中心，纽约大学）；Bernard and Irene Schwartz Center for Biomedical Imaging, New York University Langone Health（伯纳德和艾琳·施瓦茨生物医学成像中心，纽约大学朗格尼健康中心）；Department of Radiology, New York University Langone Health（放射学系，纽约大学朗格尼健康中心）；Perlmutter Cancer Center, New York University Langone Health（珀尔马特癌症中心，纽约大学朗格尼健康中心）
Knee Osteoarthritis Severity Prediction using an Attentive Multi-Scale Deep Convolutional Neural Network
Indian Institute of Technology Guwahati（印度古瓦哈蒂印度理工学院）；Cleveland Clinic（克利夫兰诊所）；Duke-NUS Medical School（杜克-新加坡国立大学医学院）；National University of Singapore（新加坡国立大学）
Duke University（杜克大学）；Duke Clinical Research Institute（杜克临床研究所）；Duke-Margolis Center for Health Policy（杜克-马戈利斯卫生政策中心）；Duke Cancer Institute（杜克癌症研究所）
The assessment of knee osteoarthritis (KOA) severity on knee X-rays is a central criteria for the use of total knee arthroplasty. However, this assessment suffers from imprecise standards and a remarkably high inter-reader variability. An algorithmic, automated assessment of KOA severity could improve overall outcomes of knee replacement procedures by increasing the appropriateness of its use. We propose a novel deep learning-based five-step algorithm to automatically grade KOA from posterior-anterior (PA) views of radiographs: (1) image preprocessing (2) localization of knees joints in the im…
University of Oulu（奥卢大学）；Oulu University Hospital（奥卢大学医院）；Erasmus MC, University Medical Center（伊拉斯谟医学中心大学医院）；Medical Research Center, University of Oulu and Oulu University Hospital（奥卢大学与奥卢大学医院医学研究中心）
The purposes of this study were to investigate: 1) the effect of placement of region-of-interest (ROI) for texture analysis of subchondral bone in knee radiographs, and 2) the ability of several texture descriptors to distinguish between the knees with and without radiographic osteoarthritis (OA). Bilateral posterior-anterior knee radiographs were analyzed from the baseline of OAI and MOST datasets. A fully automatic method to locate the most informative region from subchondral bone using adaptive segmentation was developed. We used an oversegmentation strategy for partitioning knee images int…
PAII Inc.（PAII 公司）；Chang Gung Memorial Hospital（长庚纪念医院）；Ping An Technology（平安科技）
Knee osteoarthritis (OA) is a common degenerate joint disorder that affects a large population of elderly people worldwide. Accurate radiographic assessment of knee OA severity plays a critical role in chronic patient management. Current clinically-adopted knee OA grading systems are observer subjective and suffer from inter-rater disagreements. In this work, we propose a computer-aided diagnosis approach to provide more accurate and consistent assessments of both composite and fine-grained OA grades simultaneously. A novel semi-supervised learning method is presented to exploit the underlying…
A generalizable large-scale foundation model for musculoskeletal radiographs
Seoul National University Hospital（首尔大学医院）；Seoul National University（首尔大学）；Seoul National University College of Medicine（首尔大学医学院）；Seoul National University Bundang Hospital（首尔大学盆唐医院）
Feasibility Study of a Diffusion-Based Model for Cross-Modal Generation of Knee MRI from X-ray: Integrating Radiographic Feature Information
Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；University Sorbonne Paris Nord（巴黎北大索邦大学）；German Cancer Research Center（德国癌症研究中心）；Jiangsu Institute of Hematology（江苏血液学研究所）；The First Affiliated Hospital of Soochow University（苏州大学附属第一医院）；Henan University of Chinese Medicine（河南中医药大学）；Athinoula A. Martinos Centre for Biomedical Imaging（阿西努拉·A.马蒂诺斯生物医学成像中心）；IDP Institute（IDP研究所）；University of Orleans（奥尔良大学）
H-SemiS: Hierarchical Fusion of Semi and Self-Supervised Learning for Knee Osteoarthritis Severity Grading
Department of Computer Science and Engineering, Indian Institute of Technology (IIT) Indore, Indore 453552, India（印度理工学院Indore计算机科学与工程系）；Department of Computer Science and Engineering, National Institute of Technology Tiruchirappalli, Tiruchirappalli 620015, India（印度理工学院Tiruchirappalli计算机科学与工程系）
From Kellgren-Lawrence to Calcium Pyrophosphate Crystal Deposition: A Soft-Labelling Framework for Knee Osteoarthritis Assessmen
organization= Department of Political Science, Communication；International Relations, University of Macerata , city= Macerata , country= Italy；organization= Department of Economics；Law, University of Macerata , city= Macerata , country= Italy；organization= Department of Innovative Technologies in Medicine \& Dentistry, Università degli Studi "G. D'Annunzio" Chieti - Pescara , city= Chieti , country= Italy；organization= Department of Internal Medicine, Azienda Ospedaliero Universitaria delle Marche , city = Ancona , country= Italy；organization= Academic Rheumatology, University of Nottingham , city = Nottingham , country= UK；organization= Department of Rheumatology, Polytechnic University of Marche , city= Ancona , country= Italy
Opportunistic Bone-Loss Screening from Routine Knee Radiographs Using a Multi-Task Deep Learning Framework with Sensitivity-Constrained Threshold Optimization
ORBIT Lab, College of Medicine and Biological Information Engineering, Northeastern University（ORBIT实验室，医学院和生物信息工程学院，东北大学）；Department of Radiology, Liaoning Provincial Key Laboratory of Medical Imaging, Liaoning Provincial Key Laboratory of Imaging Technology and Artificial Intelligence, Shengjing Hospital of China Medical University（放射科，辽宁省医学影像重点实验室，辽宁省影像技术与人工智能重点实验室，中国医科大学盛京医院）；Rehabilitation Center, Liaoning Provincial Key Laboratory of Medical Imaging, Liaoning Provincial Key Laboratory of Imaging Technology and Artificial Intelligence, Shengjing Hospital of China Medical University（康复中心，辽宁省医学影像重点实验室，辽宁省影像技术与人工智能重点实验室，中国医科大学盛京医院）；The University of Sydney, Sydney Musculoskeletal Health and the Kolling Institute, Northern Clinical School, Faculty of Medicine and Health and the Northern Sydney Local Health District（悉尼大学，悉尼骨科健康与Kolling研究所，北方临床医学院，医学院与健康学院，北悉尼地方卫生区）
Adaptive Variance Thresholding: A Novel Approach to Improve Existing Deep Transfer Vision Models and Advance Automatic Knee-Joint Osteoarthritis Classification
University of Jyväskylä（于韦斯屈莱大学）；University of Helsinki（赫尔辛基大学）
Diffusion-based Counterfactual Augmentation: Towards Robust and Interpretable Knee Osteoarthritis Grading
Department of Radiology, Harvard Medical School（哈佛医学院放射科）；Jiangsu Institute of Hematology, The First Affiliated Hospital of Soochow University（苏州大学第一附属医院江苏血液研究所）；L2TI Laboratory, University Sorbonne Paris Nord（巴黎-萨克雷大学L2TI实验室）；department of Medical School, Henan University of Chinese Medicine（河南中医药大学医学院）；Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne（科隆大学医学院和科隆大学医院诊断与介入放射学研究所）；Department of Electrical and Electronic Engineering, University of Hong Kong（香港大学电子与电气工程系）；Nuclear Medicine Division, Geneva University Hospital（日内瓦大学医院核医学科）；IDP Institute, UMR CNRS 7013, University of Orleans（奥尔良大学IDP研究所，CNRS UMR 7013）
