Knee osteoarthritis (OA) is one of the most widespread and burdensome health problems [1-4]. Total knee replacement (TKR) may be offered as treatment for end-stage knee OA. Nevertheless, TKR is an invasive procedure involving prosthesis implantation at the knee joint, and around 10% of patients are dissatisfied following TKR [5,6]. Dissatisfaction is often assessed through patient-reported outcome measures (PROMs) [7], which are usually completed by patients and assessed by health professionals to evaluate the condition of TKR patients. In clinical practice, predicting poor TKR outcomes in advance could help optimise patient selection and improve management strategies. Radiographic knee alignment is an important biomarker for predicting TKR outcomes and long-term joint health. Abnormalities such as femoral or tibial deformities can directly influence surgical planning, implant selection, and postoperative recovery [8,9]. Traditional alignment measurement is manual, time-consuming, and requires long-leg radiographs, which are not always undertaken in clinical practice. Instead, standard anteroposterior (AP) knee radiographs are often the main imaging modality. Automated methods for alignment assessment in standard knee radiographs are potentially clinically valuable for improving efficiency in the knee OA treatment pathway.
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…
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…
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）
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…
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）
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…
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…
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）
Mayo Clinic（梅奥诊所）；Case Western Reserve University（凯斯西储大学）；University of Southern Maine（南缅因大学）；Dubyak Center for Digital Science and Innovation, University of Southern Maine（南缅因大学Dubyak数字科学与创新中心）；Department of Artificial Intelligence and Informatics, Mayo Clinic（梅奥诊所人工智能与信息学系）；Department of Health Sciences Research, Mayo Clinic（梅奥诊所健康科学研究系）；Department of Computer and Data Sciences, Case Western Reserve University（凯斯西储大学计算机与数据科学系）；Department of Orthopedics, Mayo Clinic（梅奥诊所骨科系）；Department of Computer Science, University of Southern Maine（南缅因大学计算机科学系）
Knee pain is undoubtedly the most common musculoskeletal symptom that impairs quality of life, confines mobility and functionality across all ages. Knee pain is clinically evaluated by routine radiographs, where the widespread adoption of radiographic images and their availability at low cost, make them the principle component in the assessment of knee pain and knee pathologies, such as arthritis, trauma, and sport injuries. However, interpretation of the knee radiographs is still highly subjective, and overlapping structures within the radiographs and the large volume of images needing to be …
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…
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…
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）
Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；Brigham and Women’s Hospital（布莱根妇女医院）；Santa Clara University（圣克拉拉大学）
Identifying the design of a failed implant is a key step in preoperative planning of revision total joint arthroplasty. Manual identification of the implant design from radiographic images is time consuming and prone to error. Failure to identify the implant design preoperatively can lead to increased operating room time, more complex surgery, increased blood loss, increased bone loss, increased recovery time, and overall increased healthcare costs. In this study, we present a novel, fully automatic and interpretable approach to identify the design of total hip replacement (THR) implants from …
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…
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…
University of Oulu（奥卢大学）；Oulu University Hospital（奥卢大学医院）；KU Leuven（鲁汶大学）；Ailean Technologies Oy
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 thi…
Deep Learning-based Alignment Measurement in Knee Radiographs
Division of Informatics, Imaging and Data Sciences, The University of Manchester（信息学与数据科学系，曼彻斯特大学）；Northern Care Alliance NHS Foundation Trust（北护理联盟国家健康服务信托基金）；Department of Trauma and Orthopaedics, Stockport NHS Foundation Trust（创伤与骨科部门，史托波特国家健康服务信托基金）；School of Health and Society, University of Salford（健康与社会学院，萨尔福德大学）；School of Biological Sciences, The University of Manchester（生物科学学院，曼彻斯特大学）；Research Unit of Health Sciences and Technology, University of Oulu（健康科学与技术研究单位，奥卢大学）
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…
Three-dimensional medical imaging enables detailed understanding of osteoarthritis structural status. However, there remains a vast need for automatic, thus, reader-independent measures that provide reliable assessment of subject-specific clinical outcomes. To this end, we derive a consistent generalization of the recently proposed B-score to Riemannian shape spaces. We further present an algorithmic treatment yielding simple, yet efficient computations allowing for analysis of large shape populations with several thousand samples. Our intrinsic formulation exhibits improved discrimination abi…
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）
Landmark-free Assessment of Lower-limb Alignment with Implicit Neural Shape Functions from Knee Radiographs
Division of Informatics, Imaging and Data Sciences, The University of Manchester（曼彻斯特大学信息学、影像与数据科学部）；Research Unit of Health Sciences and Technology, University of Oulu（奥卢大学健康科学与技术研究部）；Medical Research Center Oulu, University of Oulu and Oulu University Hospital（奥卢大学与奥卢大学医院医学研究中心）；Department of Trauma and Orthopaedics, Stockport NHS Foundation Trust, Stepping Hill Hospital（斯泰平希尔医院斯托克波特NHS基金会创伤与骨科）；School of Health and Society, University of Salford（索尔福德大学健康与社会学院）；School of Biological Sciences, The University of Manchester（曼彻斯特大学生物科学学院）；Weill Cornell Medicine, Cornell University（康奈尔大学威尔康奈尔医学院）
University of California, Berkeley（加州大学伯克利分校）；University of California, San Francisco（加州大学旧金山分校）
Knee Osteoarthritis (OA) is a common musculoskeletal disorder in the United States. When diagnosed at early stages, lifestyle interventions such as exercise and weight loss can slow OA progression, but at later stages, only an invasive option is available: total knee replacement (TKR). Though a generally successful procedure, only 2/3 of patients who undergo the procedure report their knees feeling ''normal'' post-operation, and complications can arise that require revision. This necessitates a model to identify a population at higher risk of TKR, particularly at less advanced stages of OA, su…
Clinical utility of foundation models in musculoskeletal MRI for biomarker fidelity and predictive outcomes
Center for Intelligent Imaging, Department of Radiology and Biomedical Imaging, University of California, San Francisco（加利福尼亚大学旧金山分校放射与生物医学影像系智能成像中心）；Department of Bioengineering, University of California, Berkeley（加利福尼亚大学伯克利分校生物工程系）；Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco（加利福尼亚大学旧金山分校生物工程与治疗科学系）；Department of Medical Sciences and Technology, Indian Institute of Technology, Madras（印度理工学院马德拉斯分校医学科学与技术系）；Bay Area Institute of Computation, Altos Labs（Altos Labs湾区计算研究所）
The University of Queensland（昆士兰大学）；The Commonwealth Scientific and Industrial Research Organisation（联邦科学与工业研究组织）；University of Science and Technology of China（中国科学技术大学）；Chinese Academy of Sciences（中国科学院）；Institute of Computing Technology, CAS（中国科学院计算技术研究所）
Deep neural networks have been widely studied for predicting a medical condition, such as total knee replacement (TKR). It has shown that data of different modalities, such as imaging data, clinical variables and demographic information, provide complementary information and thus can improve the prediction accuracy together. However, the data sources of various modalities may not always be of high quality, and each modality may have only partial information of medical condition. Thus, predictions from different modalities can be opposite, and the final prediction may fail in the presence of su…
