MRI and biomechanics multidimensional data analysis reveals R2 -R1ρ as an early predictor of cartilage lesion progression in knee osteoarthritis

Effects of T1p Characteristics of Load-Bearing Hip Cartilage on Bilateral Knee Patellar Cartilage Subregions: Subjects With None to Moderate Radiographic Hip Osteoarthritis

BACKGROUND

The polyarticular nature of Osteoarthritis (OA) tends to manifest in multi-joints. Associations between cartilage health in connected joints can help identify early degeneration and offer the potential for biomechanical intervention. Such associations between hip and knee cartilages remain understudied.

PURPOSE

To investigate T1p associations between hip-femoral and acetabular-cartilage subregions with Intra-limb and Inter-limb patellar cartilage; whole and deep-medial (DM), deep-lateral (DL), superficial-medial (SM), superficial-lateral (SL) subregions.

STUDY TYPE

Prospective.

SUBJECTS

Twenty-eight subjects (age 55.1 ± 12.8 years, 15 females) with none-to-moderate hip-OA while no radiographic knee-OA.

FIELD STRENGTH/SEQUENCE

3-T, bilateral hip, and knee: 3D-proton-density-fat-saturated (PDFS) Cube and Magnetization-Prepared-Angle-Modulated-Partitioned-k-Space-Spoiled-Gradient-Echo-Snapshots (MAPSS).

ASSESSMENT

Ages of subjects were categorized into Group-1 (≤40), Group-2 (41-50), Group-3 (51-60), Group-4 (61-70), Group-5 (71-80), and Group-6 (≥81). Hip T1p maps, co-registered to Cube, underwent an atlas-based algorithm to quantify femoral and acetabular subregional (R2-R7) cartilage T1p. For knee Cube, a combination of V-Net architectures was used to segment the patellar cartilage and subregions (DM, DL, SM, SL). T1p values were computed from co-registered MAPSS.

STATISTICAL TESTS

For Intra-and-Inter-limb, 5 optimum predictors out of 13 (Hip subregional T1p, age group, gender) were selected by univariate linear-regression, to predict outcome (patellar T1p). The top five predictors were stepwise added to six linear mixed-effect (LME) models. In all LME models, we assume the data come from the same subject sharing the same random effect. The best-performing models (LME-modelbest) selected via ANOVA, were tested with DM, SM, SL, and DL subregional-mean T1p. LME assumptions were verified (normality of residuals, random-effects, and posterior-predictive-checks).

RESULTS

LME-modelbest (Intra-limb) had significant negative and positive fixed-effects of femoral-R5 and acetabular-R2 T1p, respectively (conditional-R2 = 0.581). LME-modelbest (Inter-limb) had significant positive fixed-effects of femoral-R3 T1p (conditional-R2 = 0.26).

DATA CONCLUSION

Significant positive and negative T1p associations were identified between load-bearing hip cartilage-subregions vs. ipsilateral and contralateral patellar cartilages respectively. The effects were localized on medial subregions of Inter-limb, in particular.

EVIDENCE LEVEL

1 TECHNICAL EFFICACY: Stage 1.

ChatGPT performance on the American Shoulder and Elbow Surgeons maintenance of certification exam

BACKGROUND

While multiple studies have tested the ability of large language models (LLMs), such as ChatGPT, to pass standardized medical exams at different levels of training, LLMs have never been tested on surgical sub-specialty examinations, such as the American Shoulder and Elbow Surgeons (ASES) Maintenance of Certification (MOC). The purpose of this study was to compare results of ChatGPT 3.5, GPT-4, and fellowship-trained surgeons on the 2023 ASES MOC self-assessment exam.

METHODS

ChatGPT 3.5 and GPT-4 were subjected to the same set of text-only questions from the ASES MOC exam, and GPT-4 was additionally subjected to image-based MOC exam questions. Question responses from both models were compared against the correct answers. Performance of both models was compared to corresponding average human performance on the same question subsets. One sided proportional z-test were utilized to analyze data.

RESULTS

Humans performed significantly better than Chat GPT 3.5 on exclusively text-based questions (76.4% vs. 60.8%, P = .044). Humans also performed significantly better than GPT 4 on image-based questions (73.9% vs. 53.2%, P = .019). There was no significant difference between humans and GPT 4 in text-based questions (76.4% vs. 66.7%, P = .136). Accounting for all questions, humans significantly outperformed GPT-4 (75.3% vs. 60.2%, P = .012). GPT-4 did not perform statistically significantly betterer than ChatGPT 3.5 on text-only questions (66.7% vs. 60.8%, P = .268).

DISCUSSION

Although human performance was overall superior, ChatGPT demonstrated the capacity to analyze orthopedic information and answer specialty-specific questions on the ASES MOC exam for both text and image-based questions. With continued advancements in deep learning, LLMs may someday rival exam performance of fellowship-trained surgeons.

Synthetic Knee MRI T1p Maps as an Avenue for Clinical Translation of Quantitative Osteoarthritis Biomarkers

A 2D U-Net was trained to generate synthetic T1p maps from T2 maps for knee MRI to explore the feasibility of domain adaptation for enriching existing datasets and enabling rapid, reliable image reconstruction. The network was developed using 509 healthy contralateral and injured ipsilateral knee images from patients with ACL injuries and reconstruction surgeries acquired across three institutions. Network generalizability was evaluated on 343 knees acquired in a clinical setting and 46 knees from simultaneous bilateral acquisition in a research setting. The deep neural network synthesized high-fidelity reconstructions of T1p maps, preserving textures and local T1p elevation patterns in cartilage with a normalized mean square error of 2.4% and Pearson's correlation coefficient of 0.93. Analysis of reconstructed T1p maps within cartilage compartments revealed minimal bias (-0.10 ms), tight limits of agreement, and quantification error (5.7%) below the threshold for clinically significant change (6.42%) associated with osteoarthritis. In an out-of-distribution external test set, synthetic maps preserved T1p textures, but exhibited increased bias and wider limits of agreement. This study demonstrates the capability of image synthesis to reduce acquisition time, derive meaningful information from existing datasets, and suggest a pathway for standardizing T1p as a quantitative biomarker for osteoarthritis.

Artificial intelligence and ChatGPT in Orthopaedics and sports medicine

Artificial intelligence (AI) is looked upon nowadays as the potential major catalyst for the fourth industrial revolution. In the last decade, AI use in Orthopaedics increased approximately tenfold. Artificial intelligence helps with tracking activities, evaluating diagnostic images, predicting injury risk, and several other uses. Chat Generated Pre-trained Transformer (ChatGPT), which is an AI-chatbot, represents an extremely controversial topic in the academic community. The aim of this review article is to simplify the concept of AI and study the extent of AI use in Orthopaedics and sports medicine literature. Additionally, the article will also evaluate the role of ChatGPT in scientific research and publications.Level of evidence: Level V, letter to review.

Assessing post-traumatic changes in cartilage using T1ρ dispersion parameters

Degeneration of cartilage can be studied non-invasively with quantitative MRI. A promising parameter for detecting early osteoarthritis in articular cartilage is T_1ρ, which can be tuned via the amplitude of the spin-lock pulse. By measuring T_1ρ at several spin-lock amplitudes, the dispersion of T_1ρ is obtained. The aim of this study is to find out if the dispersion contains diagnostically relevant information complementary to a T_1ρ measurement at a single spin-lock amplitude. To this end, five differently acquired dispersion parameters are utilized; A, B, τ_c, T_1ρ/T₂, and R₂ - R_1ρ. An open dataset of an equine model of post-traumatic cartilage was utilized to assess the T_1ρ dispersion parameters for the evaluation of cartilage degeneration. Firstly, the parameters were compared for their sensitivity in detecting degenerative changes. Secondly, the relationship of the dispersion parameters to histological and biomechanical reference parameters was studied. Parameters A, T_1ρ/T₂, and R₂ - R_1ρ were found to be sensitive to lesion-induced changes in the cartilage within sample. Strong correlations of several dispersion parameters with optical density, as well as with collagen fibril angle were found. Most of the dispersion parameters correlated strongly with individual T_1ρ values. The results suggest that dispersion parameters can in some cases provide a more accurate description of the biochemical composition of cartilage as compared to conventional MRI parameters. However, in most cases the information given by the dispersion parameters is more of a refinement than complementary to conventional quantitative MRI.

Global research landscape on artificial intelligence in arthroplasty: A bibliometric analysis

Background

Artificial intelligence (AI) has promising applications in arthroplasty. In response to the knowledge explosion resulting from the rapid growth of publications, we applied bibliometric analysis to explore the research profile and topical trends in this field.

Methods

The articles and reviews related to AI in arthroplasty were retrieved from 2000 to 2021. The Java-based Citespace, VOSviewer, R software-based Bibiometrix, and an online platform systematically evaluated publications by countries, institutions, authors, journals, references, and keywords.

Results

A total of 867 publications were included. Over the past 22 years, the number of AI-related publications in the field of arthroplasty has grown exponentially. The United States was the most productive and academically influential country. The Cleveland Clinic was the most prolific institution. Most publications were published in high academic impact journals. However, collaborative networks revealed a lack and imbalance of inter-regional, inter-institutional, and inter-author cooperation. Two emerging research areas represented the development trends: major AI subfields such as machine learning and deep learning, and the other is research related to clinical outcomes.

Conclusion

AI in arthroplasty is evolving rapidly. Collaboration between different regions and institutions should be strengthened to deepen our understanding further and exert critical implications for decision-making. Predicting clinical outcomes of arthroplasty using novel AI strategies may be a promising application in this field.

The application of machine learning in early diagnosis of osteoarthritis: a narrative review

Osteoarthritis (OA) is the commonest musculoskeletal disease worldwide, with an increasing prevalence due to aging. It causes joint pain and disability, decreased quality of life, and a huge burden on healthcare services for society. However, the current main diagnostic methods are not suitable for early diagnosing patients of OA. The use of machine learning (ML) in OA diagnosis has increased dramatically in the past few years. Hence, in this review article, we describe the research progress in the application of ML in the early diagnosis of OA, discuss the current trends and limitations of ML approaches, and propose future research priorities to apply the tools in the field of OA. Accurate ML-based predictive models with imaging techniques that are sensitive to early changes in OA ahead of the emergence of clinical features are expected to address the current dilemma. The diagnostic ability of the fusion model that combines multidimensional information makes patient-specific early diagnosis and prognosis estimation of OA possible in the future.

Adversarial Evolving Neural Network for Longitudinal Knee Osteoarthritis Prediction

Knee osteoarthritis (KOA) as a disabling joint disease has doubled in prevalence since the mid-20th century. Early diagnosis for the longitudinal KOA grades has been increasingly important for effective monitoring and intervention. Although recent studies have achieved promising performance for baseline KOA grading, longitudinal KOA grading has been seldom studied and the KOA domain knowledge has not been well explored yet. In this paper, a novel deep learning architecture, namely adversarial evolving neural network (A-ENN), is proposed for longitudinal grading of KOA severity. As the disease progresses from mild to severe level, ENN involves the progression patterns for accurately characterizing the disease by comparing an input image it to the template images of different KL grades using convolution and deconvolution computations. In addition, an adversarial training scheme with a discriminator is developed to obtain the evolution traces. Thus, the evolution traces as fine-grained domain knowledge are further fused with the general convolutional image representations for longitudinal grading. Note that ENN can be applied to other learning tasks together with existing deep architectures, in which the responses characterize progressive representations. Comprehensive experiments on the Osteoarthritis Initiative (OAI) dataset were conducted to evaluate the proposed method. An overall accuracy was achieved as 62.7%, with the baseline, 12-month, 24-month, 36-month, and 48-month accuracy as 64.6%, 63.9%, 63.2%, 61.8% and 60.2%, respectively.

Topological data analysis in biomedicine: A review

Significant technological advances made in recent years have shepherded a dramatic increase in utilization of digital technologies for biomedicine- everything from the widespread use of electronic health records to improved medical imaging capabilities and the rising ubiquity of genomic sequencing contribute to a "digitization" of biomedical research and clinical care. With this shift toward computerized tools comes a dramatic increase in the amount of available data, and current tools for data analysis capable of extracting meaningful knowledge from this wealth of information have yet to catch up. This article seeks to provide an overview of emerging mathematical methods with the potential to improve the abilities of clinicians and researchers to analyze biomedical data, but may be hindered from doing so by a lack of conceptual accessibility and awareness in the life sciences research community. In particular, we focus on topological data analysis (TDA), a set of methods grounded in the mathematical field of algebraic topology that seeks to describe and harness features related to the "shape" of data. We aim to make such techniques more approachable to non-mathematicians by providing a conceptual discussion of their theoretical foundations followed by a survey of their published applications to scientific research. Finally, we discuss the limitations of these methods and suggest potential avenues for future work integrating mathematical tools into clinical care and biomedical informatics.

Leveraging explainable machine learning to identify gait biomechanical parameters associated with anterior cruciate ligament injury

Anterior cruciate ligament (ACL) deficient and reconstructed knees display altered biomechanics during gait. Identifying significant gait changes is important for understanding normal and ACL function and is typically performed by statistical approaches. This paper focuses on the development of an explainable machine learning (ML) empowered methodology to: (i) identify important gait kinematic, kinetic parameters and quantify their contribution in the diagnosis of ACL injury and (ii) investigate the differences in sagittal plane kinematics and kinetics of the gait cycle between ACL deficient, ACL reconstructed and healthy individuals. For this aim, an extensive experimental setup was designed in which three-dimensional ground reaction forces and sagittal plane kinematic as well as kinetic parameters were collected from 151 subjects. The effectiveness of the proposed methodology was evaluated using a comparative analysis with eight well-known classifiers. Support Vector Machines were proved to be the best performing model (accuracy of 94.95%) on a group of 21 selected biomechanical parameters. Neural Networks accomplished the second best performance (92.89%). A state-of-the-art explainability analysis based on SHapley Additive exPlanations (SHAP) and conventional statistical analysis were then employed to quantify the contribution of the input biomechanical parameters in the diagnosis of ACL injury. Features, that would have been neglected by the traditional statistical analysis, were identified as contributing parameters having significant impact on the ML model’s output for ACL injury during gait.

Use of machine learning in osteoarthritis research: a systematic literature review

Objective

The aim of this systematic literature review was to provide a comprehensive and exhaustive overview of the use of machine learning (ML) in the clinical care of osteoarthritis (OA).

Methods

A systematic literature review was performed in July 2021 using MEDLINE PubMed with key words and MeSH terms. For each selected article, the number of patients, ML algorithms used, type of data analysed, validation methods and data availability were collected.

Results

From 1148 screened articles, 46 were selected and analysed; most were published after 2017. Twelve articles were related to diagnosis, 7 to prediction, 4 to phenotyping, 12 to severity and 11 to progression. The number of patients included ranged from 18 to 5749. Overall, 35% of the articles described the use of deep learning And 74% imaging analyses. A total of 85% of the articles involved knee OA and 15% hip OA. No study investigated hand OA. Most of the studies involved the same cohort, with data from the OA initiative described in 46% of the articles and the MOST and Cohort Hip and Cohort Knee cohorts in 11% and 7%. Data and source codes were described as publicly available respectively in 54% and 22% of the articles. External validation was provided in only 7% of the articles.

Conclusion

This review proposes an up-to-date overview of ML approaches used in clinical OA research and will help to enhance its application in this field.

Applications of artificial intelligence and machine learning for the hip and knee surgeon: current state and implications for the future

BACKGROUND

Artificial Intelligence (AI)/Machine Learning (ML) applications have been proven efficient to improve diagnosis, to stratify risk, and to predict outcomes in many respective medical specialties, including in orthopaedics.

CHALLENGES AND DISCUSSION

Regarding hip and knee reconstruction surgery, AI/ML have not made it yet to clinical practice. In this review, we present sound AI/ML applications in the field of hip and knee degenerative disease and reconstruction. From osteoarthritis (OA) diagnosis and prediction of its advancement, clinical decision-making, identification of hip and knee implants to prediction of clinical outcome and complications following a reconstruction procedure of these joints, we report how AI/ML systems could facilitate data-driven personalized care for our patients.

Investigating the Association of Metabolic Biomarkers With Knee Cartilage Composition and Structural Abnormalities Using MRI: A Pilot Study

OBJECTIVE

The goal of this study was to explore the metabolic syndrome-associated phenotype of osteoarthritis by investigating the cross-sectional associations of glycemic markers and serum lipids with knee cartilage composition and structural abnormalities in middle-aged adults.

DESIGN

Twenty participants between 40 to 70 years of age with Kellgren-Lawrence score 0-1 in at least one knee were recruited at a single center. Knee cartilage composition was assessed using 3.0 T cartilage T₂ and T_1ρ mapping. Evaluation of structural knee abnormalities was performed using the modified Whole-Organ Magnetic Resonance Imaging Score (WORMS). Linear regression was used to assess the associations of standardized fasting glucose (FG), hemoglobin A1c (HbA1c), insulin, total cholesterol, high-density lipoprotein (HDL), low-density lipoprotein (LDL), non-HDL cholesterol, and triglycerides with cartilage T₂ and T_1ρ as well as WORMS subscores, adjusting for body mass index.

RESULTS

Higher FG and higher HbA1c were associated with higher WORMS meniscus sum (beta coefficient 1.31 [95% confidence interval (CI): 0.57, 2.05], P = 0.002 per standard deviation [SD] increase in FG; beta coefficient 0.90 [95% CI: 0.07, 1.73], P = 0.035 per SD increase in HbA1c). Also, higher total cholesterol and higher non-HDL cholesterol were associated with higher WORMS cartilage sum (beta coefficient 0.94 [95% CI: 0.01, 1.86], P = 0.048 per SD increase in total cholesterol; beta coefficient 1.05 [95% CI: 0.14, 1.96], P = 0.03 per SD increase in non-HDL cholesterol).

CONCLUSIONS

Higher FG and HbA1c were associated with increased meniscal degeneration while higher total and non-HDL cholesterol were associated with increased cartilage degeneration.

Magnetization-prepared spoiled gradient-echo snapshot imaging for efficient measurement of R2 -R1ρ in knee cartilage

PURPOSE

To validate the potential of quantifying R₂ -R_1ρ using one pair of signals with T_1ρ preparation and T₂ preparation incorporated to magnetization-prepared angle-modulated partitioned k-space spoiled gradient-echo snapshots (MAPSS) acquisition and to find an optimal preparation time (T_prep ) for in vivo knee MRI.

METHODS

Bloch equation simulations were first performed to assess the accuracy of quantifying R₂ -R_1ρ using T_1ρ - and T₂ -prepared signals with an equivalent T_prep . For validation of this technique in comparison to the conventional approach that calculates R₂ -R_1ρ after estimating both T₂ and T_1ρ , phantom experiments and in vivo validation with five healthy subjects and five osteoarthritis patients were performed at a clinical 3T scanner.

RESULTS

Bloch equation simulations demonstrated that the accuracy of this efficient R₂ -R_1ρ quantification method and the optimal T_prep can be affected by image signal-to-noise ratio (SNR) and tissue relaxation times, but quantification can be closest to the reference with an around 25 ms T_prep for knee cartilage. Phantom experiments demonstrated that the proposed method can depict R₂ -R_1ρ changes with agarose gel concentration. With in vivo data, significant correlation was observed between cartilage R₂ -R_1ρ measured from the conventional and the proposed methods, and a T_prep of 25.6 ms provided the most agreement by Bland-Altman analysis. R₂ -R_1ρ was significantly lower in patients than in healthy subjects for most cartilage compartments.

CONCLUSION

As a potential biomarker to indicate cartilage degeneration, R₂ -R_1ρ can be efficiently measured using one pair of T_1ρ -prepared and T₂ -prepared signals with an optimal T_prep considering cartilage relaxation times and image SNR.

A Deep Learning Model to Predict Knee Osteoarthritis Based on Nonimage Longitudinal Medical Record

PURPOSE

To develop deep learning model (Deep-KOA) that can predict the risk of knee osteoarthritis (KOA) within the next year by using the previous three years nonimage-based electronic medical record (EMR) data.

PATIENTS AND METHODS

We randomly selected information of two million patients from the Taiwan National Health Insurance Research Database (NHIRD) from January 1, 1999 to December 31, 2013. During the study period, 132,594 patients were diagnosed with KOA, while 1,068,464 patients without KOA were chosen randomly as control. We constructed a feature matrix by using the three-year history of sequential diagnoses, drug prescriptions, age, and sex. Deep learning methods of convolutional neural network (CNN) and artificial neural network (ANN) were used together to develop a risk prediction model. We used the area under the receiver operating characteristic (AUROC), sensitivity, specificity, and precision to evaluate the performance of Deep-KOA. Then, we explored the important features using stepwise feature selection.

RESULTS

This study included 132,594 KOA patients, 83,111 females (62.68%), 49,483 males (37.32%), mean age 64.2 years, and 1,068,464 non-KOA patients, 545,902 females (51.09%), 522,562 males (48.91%), mean age 51.00 years. The Deep-KOA achieved an overall AUROC, sensitivity, specificity, and precision of 0.97, 0.89, 0.93, and 0.80 respectively. The discriminative analysis of Deep-KOA showed important features from several diseases such as disorders of the eye and adnexa, acute respiratory infection, other metabolic and immunity disorders, and diseases of the musculoskeletal and connective tissue. Age and sex were not found as the most discriminative features, with AUROC of 0.9593 (-0.76% loss) and 0.9644 (-0.25% loss) respectively. Whereas medications including antacid, cough suppressant, and expectorants were identified as discriminative features.

CONCLUSION

Deep-KOA was developed to predict the risk of KOA within one year earlier, which may provide clues for clinical decision support systems to target patients with high risk of KOA to get precision prevention program.

Real-World Evidence: A Primer

SUMMARY

The insights that real-world data (RWD) can provide, beyond what can be learned within the traditional clinical trial setting, have gained enormous traction in recent years. RWD, which are increasingly available and accessible, can further our understanding of disease, disease progression, and safety and effectiveness of treatments with the speed and accuracy required by the health care environment and patients today. Over the decades since RWD were first recognized, innovation has evolved to take real-world research beyond finding ways to identify, store, and analyze large volumes of data. The research community has developed strong methods to address challenges of using RWD and as a result has increased the acceptance of RWD in research, practice, and policy. Historic concerns about RWD relate to data quality, privacy, and transparency; however, new tools, methods, and approaches mitigate these challenges and expand the utility of RWD to new applications. Specific guidelines for RWD use have been developed and published by numerous groups, including regulatory authorities. These and other efforts have shown that the more RWD are used and understood and the more the tools for handling it are refined, the more useful it will be.

Identifying Robust Risk Factors for Knee Osteoarthritis Progression: An Evolutionary Machine Learning Approach

Knee osteoarthritis (KOA) is a multifactorial disease which is responsible for more than 80% of the osteoarthritis disease's total burden. KOA is heterogeneous in terms of rates of progression with several different phenotypes and a large number of risk factors, which often interact with each other. A number of modifiable and non-modifiable systemic and mechanical parameters along with comorbidities as well as pain-related factors contribute to the development of KOA. Although models exist to predict the onset of the disease or discriminate between asymptotic and OA patients, there are just a few studies in the recent literature that focused on the identification of risk factors associated with KOA progression. This paper contributes to the identification of risk factors for KOA progression via a robust feature selection (FS) methodology that overcomes two crucial challenges: (i) the observed high dimensionality and heterogeneity of the available data that are obtained from the Osteoarthritis Initiative (OAI) database and (ii) a severe class imbalance problem posed by the fact that the KOA progressors class is significantly smaller than the non-progressors' class. The proposed feature selection methodology relies on a combination of evolutionary algorithms and machine learning (ML) models, leading to the selection of a relatively small feature subset of 35 risk factors that generalizes well on the whole dataset (mean accuracy of 71.25%). We investigated the effectiveness of the proposed approach in a comparative analysis with well-known FS techniques with respect to metrics related to both prediction accuracy and generalization capability. The impact of the selected risk factors on the prediction output was further investigated using SHapley Additive exPlanations (SHAP). The proposed FS methodology may contribute to the development of new, efficient risk stratification strategies and identification of risk phenotypes of each KOA patient to enable appropriate interventions.

Prediction of Joint Space Narrowing Progression in Knee Osteoarthritis Patients

Osteoarthritis is a joint disease that commonly occurs in the knee (KOA). The continuous increase in medical data regarding KOA has triggered researchers to incorporate artificial intelligence analytics for KOA prognosis or treatment. In this study, two approaches are presented to predict the progression of knee joint space narrowing (JSN) in each knee and in both knees combined. A machine learning approach is proposed with the use of multidisciplinary data from the osteoarthritis initiative database. The proposed methodology employs: (i) A clustering process to identify groups of people with progressing and non-progressing JSN; (ii) a robust feature selection (FS) process consisting of filter, wrapper, and embedded techniques that identifies the most informative risk factors; (iii) a decision making process based on the evaluation and comparison of various classification algorithms towards the selection and development of the final predictive model for JSN; and (iv) post-hoc interpretation of the features' impact on the best performing model. The results showed that bounding the JSN progression of both knees can result to more robust prediction models with a higher accuracy (83.3%) and with fewer risk factors (29) compared to the right knee (77.7%, 88 risk factors) and the left knee (78.3%, 164 risk factors), separately.

Current status of functional MRI of osteoarthritis for diagnosis and prognosis

PURPOSE OF REVIEW

Osteoarthritis is a major source of disability, pain and socioeconomic cost worldwide. The epidemiology of the disorder is multifactorial including genetic, biological and biomechanical components, some of them detectable by MRI. This review provides the most recent update on MRI biomarkers which can provide functional information of the joint structures for diagnosis, prognosis and treatment response monitoring in osteoarthritis trials.

RECENT FINDINGS

Compositional or functional MRI can provide clinicians with valuable information on glycosaminoglycan content (chemical exchange saturation transfer, sodium MRI, T1ρ) and collagen organization (T2, T2, apparent diffusion coefficient, magnetization transfer) in joint structures. Other parameters may also provide useful information, such as volumetric measurements of joint structures or advanced image data postprocessing and analysis. Automated tools seem to have a great potential to be included in these efforts providing standardization and acceleration of the image data analysis process.

SUMMARY

Functional or compositional MRI has great potential to provide noninvasive imaging biomarkers for osteoarthritis. Osteoarthritis as a whole joint condition needs to be diagnosed in early stages to facilitate selection of patients into clinical trials and/or to measure treatment effectiveness. Advanced evaluation including machine learning, neural networks and multidimensional data analysis allow for wall-to-wall understanding of parameter interactions and their role in clinical evaluation of osteoarthritis.

Use of machine learning in geriatric clinical care for chronic diseases: a systematic literature review

Objectives

Geriatric clinical care is a multidisciplinary assessment designed to evaluate older patients’ (age 65 years and above) functional ability, physical health, and cognitive well-being. The majority of these patients suffer from multiple chronic conditions and require special attention. Recently, hospitals utilize various artificial intelligence (AI) systems to improve care for elderly patients. The purpose of this systematic literature review is to understand the current use of AI systems, particularly machine learning (ML), in geriatric clinical care for chronic diseases.

Materials and Methods

We restricted our search to eight databases, namely PubMed, WorldCat, MEDLINE, ProQuest, ScienceDirect, SpringerLink, Wiley, and ERIC, to analyze research articles published in English between January 2010 and June 2019. We focused on studies that used ML algorithms in the care of geriatrics patients with chronic conditions.

Results

We identified 35 eligible studies and classified in three groups: psychological disorder (n = 22), eye diseases (n = 6), and others (n = 7). This review identified the lack of standardized ML evaluation metrics and the need for data governance specific to health care applications.

Conclusion

More studies and ML standardization tailored to health care applications are required to confirm whether ML could aid in improving geriatric clinical care.

Identification of Risk Factors and Machine Learning-Based Prediction Models for Knee Osteoarthritis Patients

Knee Osteoarthritis (KOA) is a multifactorial disease that causes low quality of life, poor psychology and resignation from life. Furthermore, KOA is a big data problem in terms of data complexity, heterogeneity and size as it has been commonly considered in the literature with most of the reported studies being limited in the amount of information they can adequately process. The aim of this paper is: (i) To provide a robust feature selection (FS) approach that could identify important risk factors which contribute to the prediction of KOA and (ii) to develop machine learning (ML) prediction models for KOA. The current study considers multidisciplinary data from the osteoarthritis initiative (OAI) database, the available features of which come from heterogeneous sources such as questionnaire data, physical activity indexes, self-reported data about joint symptoms, disability and function as well as general health and physical exams’ data. The novelty of the proposed FS methodology lies on the combination of different well-known approaches including filter, wrapper and embedded techniques, whereas feature ranking is decided on the basis of a majority vote scheme to avoid bias. The validation of the selected factors was performed in data subgroups employing seven well-known classifiers in five different approaches. A 74.07% classification accuracy was achieved by SVM on the group of the first fifty-five selected risk factors. The effectiveness of the proposed approach was evaluated in a comparative analysis with respect to classification errors and confusion matrices to confirm its clinical relevance. The results are the basis for the development of reliable tools for the prediction of KOA progression.

Automating three-dimensional osteoarthritis histopathological grading of human osteochondral tissue using machine learning on contrast-enhanced micro-computed tomography

OBJECTIVE

To develop and validate a machine learning (ML) approach for automatic three-dimensional (3D) histopathological grading of osteochondral samples imaged with contrast-enhanced micro-computed tomography (CEμCT).

DESIGN

A total of 79 osteochondral cores from 24 total knee arthroplasty patients and two asymptomatic donors were imaged using CEμCT with phosphotungstic acid -staining. Volumes-of-interest (VOI) in surface (SZ), deep (DZ) and calcified (CZ) zones were extracted depth-wise and subjected to dimensionally reduced Local Binary Pattern -textural feature analysis. Regularized linear and logistic regression (LR) models were trained zone-wise against the manually assessed semi-quantitative histopathological CEμCT grades (diameter = 2 mm samples). Models were validated using nested leave-one-out cross-validation and an independent test set (4 mm samples). The performance was primarily assessed using Mean Squared Error (MSE) and Average Precision (AP, confidence intervals are given in square brackets).

RESULTS

Highest performance on cross-validation was observed for SZ, both on linear regression (MSE = 0.49, 0.69 and 0.71 for SZ, DZ and CZ, respectively) and LR (AP = 0.9 [0.77-0.99], 0.46 [0.28-0.67] and 0.65 [0.41-0.85] for SZ, DZ and CZ, respectively). The test set evaluations yielded increased MSE on all zones. For LR, the performance was also best for the SZ (AP = 0.85 [0.73-0.93], 0.82 [0.70-0.92] and 0.8 [0.67-0.9], for SZ, DZ and CZ, respectively).

CONCLUSION

We present the first ML-based automatic 3D histopathological osteoarthritis (OA) grading method which also adequately perform on grading unseen data, especially in SZ. After further development, the method could potentially be applied by OA researchers since the grading software and all source codes are publicly available.

Generating dynamical neuroimaging spatiotemporal representations (DyNeuSR) using topological data analysis

In this article, we present an open source neuroinformatics platform for exploring, analyzing, and validating distilled graphical representations of high-dimensional neuroimaging data extracted using topological data analysis (TDA). TDA techniques like Mapper have been recently applied to examine the brain's dynamical organization during ongoing cognition without averaging data in space, in time, or across participants at the outset. Such TDA-based approaches mark an important deviation from standard neuroimaging analyses by distilling complex high-dimensional neuroimaging data into simple-yet neurophysiologically valid and behaviorally relevant-representations that can be interactively explored at the single-participant level. To facilitate wider use of such techniques within neuroimaging and general neuroscience communities, our work provides several tools for visualizing, interacting with, and grounding TDA-generated graphical representations in neurophysiology. Through Python-based Jupyter notebooks and open datasets, we provide a platform to assess and visualize different intermittent stages of Mapper and examine the influence of Mapper parameters on the generated representations. We hope this platform could enable researchers and clinicians alike to explore topological representations of neuroimaging data and generate biological insights underlying complex mental disorders.

Diagnosing osteoarthritis from T2 maps using deep learning: an analysis of the entire Osteoarthritis Initiative baseline cohort

OBJECTIVE

We aim to study to what extent conventional and deep-learning-based T2 relaxometry patterns are able to distinguish between knees with and without radiographic osteoarthritis (OA).

METHODS

T2 relaxation time maps were analyzed for 4,384 subjects from the baseline Osteoarthritis Initiative (OAI) Dataset. Voxel Based Relaxometry (VBR) was used for automatic quantification and voxel-based analysis of the differences in T2 between subjects with and without radiographic OA. A Densely Connected Convolutional Neural Network (DenseNet) was trained to diagnose OA from T2 data. For comparison, more classical feature extraction techniques and shallow classifiers were used to benchmark the performance of our algorithm's results. Deep and shallow models were evaluated with and without the inclusion of risk factors. Sensitivity and Specificity values and McNemar test were used to compare the performance of the different classifiers.

RESULTS

The best shallow model was obtained when the first ten Principal Components, demographics and pain score were included as features (AUC = 77.77%, Sensitivity = 67.01%, Specificity = 71.79%). In comparison, DenseNet trained on raw T2 data obtained AUC = 83.44%, Sensitivity = 76.99%, Specificity = 77.94%. McNemar test on two misclassified proportions form the shallow and deep model showed that the boost in performance was statistically significant (McNemar's chi-squared = 10.33, degree of freedom (DF) = 1, P-value = 0.0013).

CONCLUSION

In this study, we presented a Magnetic Resonance Imaging (MRI)-based data-driven platform using T2 measurements to characterize radiographic OA. Our results showed that feature learning from T2 maps has potential in uncovering information that can potentially better diagnose OA than simple averages or linear patterns decomposition.

Machine Learning Accurately Predicts Short-Term Outcomes Following Open Reduction and Internal Fixation of Ankle Fractures

Ankle fractures are common orthopedic injuries with favorable outcomes when managed with open reduction and internal fixation (ORIF). Several patient-related risk factors may contribute to poor short-term outcomes, and machine learning may be a valuable tool for predicting outcomes. The objective of this study was to evaluate machine-learning algorithms for accurately predicting short-term outcomes after ORIF for ankle fractures. The Nationwide Inpatient Sample and Nationwide Readmissions Database were queried for adult patients ≥18 years old who underwent ORIF of an ankle fracture during 2013 or 2014. Morbidity and mortality, length of stay >3 days, and 30-day all-cause readmission were the outcomes of interest. Two machine-learning models were created to identify patient and hospital characteristics associated with the 3 outcomes. The machine learning models were evaluated using confusion matrices and receiver operating characteristic area under the curve values. A total of 16,501 cases were drawn from the Nationwide Inpatient Sample and used to assess morbidity and mortality and length of stay >3 days, and 33,504 cases were drawn from the Nationwide Readmissions Database to assess 30-day readmission. Older age, Medicaid, Medicare, deficiency anemia, congestive heart failure, chronic lung disease, diabetes, hypertension, and renal failure were the variables associated with a statistically significant increased risk of developing all 3 adverse events. Logistic regression and gradient boosting had similar area under the curve values for each outcome, but gradient boosting was more accurate and more specific for predicting each outcome. Our results suggest that several comorbidities may be associated with adverse short-term outcomes after ORIF of ankle fractures, and that machine learning can accurately predict these outcomes.

Osteoarthritis year in review 2018: mechanics

OBJECTIVE

To review recent biomechanics literature focused on the interactions between biomechanics and articular cartilage health, particularly focused on macro-scale and human studies.

DESIGN

A literature search was conducted in PubMed using the search terms (biomechanics AND osteoarthritis) OR (biomechanics AND cartilage) OR (mechanics AND osteoarthritis) OR (mechanics AND cartilage) for publications from April 2017 to April 2018.

RESULTS

Abstracts from the 559 articles generated from the literature search were reviewed. Due to the wide range of topics, 62 full texts with a focus on in vivo biomechanical studies were included for further discussion. Several overarching themes in the recent literature were identified and are summarized, including 1) new methods to detect early osteoarthritis (OA) development, 2) studies describing healthy and OA cartilage and biomechanics, 3) ACL injury and OA development, 4) meniscus injury and OA development, and 5) OA prevention, treatment, and management.

CONCLUSIONS

Mechanical loading is a critical factor in the maintenance of joint health. Abnormal mechanical loading can lead to the onset and progression of OA. Thus, recent studies have utilized various biomechanical models to better describe the etiology of OA development and the subsequent effects of OA on the mechanics of joint tissues and whole body biomechanics.

Osteoarthritis year in review 2018: imaging

PURPOSE

To provide a narrative review of the most relevant original research published in 2017/2018 on osteoarthritis imaging.

METHODS

The PubMed database was used to recover all relevant articles pertaining to osteoarthritis and medical imaging published between 1 April 2017 and 31 March 2018. Review articles, case studies and in vitro or animal studies were excluded. The original publications were subjectively sorted based on relevance, novelty and impact.

RESULTS AND CONCLUSIONS

The publication search yielded 1,155 references. In the assessed publications, the most common imaging modalities were radiography (N = 708) and magnetic resonance imaging (MRI) (355), followed by computed tomography (CT) (220), ultrasound (85) and nuclear medicine (17). An overview of the most important publications to the osteoarthritis (OA) research community is presented in this narrative review. Imaging studies play an increasingly important role in OA research, and have helped us to understand better the pathophysiology of OA. Radiography and MRI continue to be the most applied imaging modalities, while quantitative MRI methods and texture analysis are becoming more popular. The value of ultrasound in OA research has been demonstrated. Several multi-modality predictive models have been developed. Deep learning has potential for more automatic and standardized analyses in future OA imaging research.

A unique anisotropic R2 of collagen degeneration (ARCADE) mapping as an efficient alternative to composite relaxation metric (R2 -R1 ρ ) in human knee cartilage study

PURPOSE

Anisotropic transverse R₂ (1/T₂ ) relaxation of water proton is sensitive to cartilage degenerative changes. The purpose is to develop an efficient method to extract this relaxation metric in clinical studies.

METHODS

Anisotropic R₂ can be measured inefficiently by standard R₂ mapping after removing an isotropic contribution obtained from R_{1 ρ} mapping. In the proposed method, named as a unique anisotropic R₂ of collagen degeneration (ARCADE) mapping, an assumed uniform isotropic R₂ was estimated at magic angle locations in the deep cartilage, and an anisotropic R₂ was thus isolated in a single T2W sagittal image. Five human knees from 4 volunteers were studied with standard R₂ and R_{1 ρ} mappings at 3T, and anisotropic R₂ derived from ARCADE on the T2W (TE = 48.8 ms) image from R₂ mapping was compared with the composite relaxation (R₂ - R_{1 ρ} ) using statistical analysis including Student's t-test and Pearson's correlation coefficient.

RESULTS

Anisotropic R₂ (1/s) from ARCADE was highly positively correlated with but not significantly different from standard R₂ - R_{1 ρ} (1/s) in the segmented deep (r = 0.83 ± 0.06; 8.3 ± 2.9 vs. 7.3 ± 1.9, P = .50) and the superficial (r = 0.82 ± 0.05; 3.5 ± 2.4 vs. 4.5 ± 1.6, P = .39) zones. However, after eliminating systematic errors by the normalization in terms of zonal contrast, anisotropic R₂ was significantly higher (60.2 ± 18.5% vs. 38.4 ± 16.6%, P < .01) than R₂ - R_{1 ρ} as predicted.

CONCLUSION

The proposed anisotropic R₂ mapping could be an efficient alternative to the conventional approach, holding great promise in providing both high-resolution morphological and more sensitive transverse relaxation imaging from a single T2W scan in a clinical setting.

Translation of morphological and functional musculoskeletal imaging

In an effort to develop quantitative biomarkers for degenerative joint disease and fill the void that exists for diagnosing, monitoring, and assessing the extent of whole joint degeneration, the past decade has been marked by a greatly increased role of noninvasive imaging. This coupled with recent advances in image processing and deep learning opens new possibilities for promising quantitative techniques. The clinical translation of quantitative imaging was previously hampered by tedious non-scalable and subjective image analysis. Osteoarthritis (OA) diagnosis using X-rays can be automated by the use of deep learning models and pilot studies showed feasibility of using similar techniques to reliably segment multiple musculoskeletal tissues and detect and stage the severity of morphological abnormalities in magnetic resonance imaging (MRI). Automation and more advanced feature extraction techniques have applications on larger more heterogeneous samples. Analyses based on voxel based relaxometry have shown local patterns in relaxation time elevations and local correlations with outcome variables. Bone cartilage interactions are also enhanced by the analysis of three-dimensional bone morphology and the potential for the assessment of metabolic activity with simultaneous Positron Emission Tomography (PET)/MR systems. Novel techniques in image processing and deep learning are augmenting imaging to be a source of quantitative and reliable data and new multidimensional analytics allow us to exploit the interactions of data from various sources. In this review, we aim to summarize recent advances in quantitative imaging, the application of image processing and deep learning techniques to study knee and hip OA. ©2018 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res XX:XX-XX, 2018.

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.

Using multidimensional topological data analysis to identify traits of hip osteoarthritis

BACKGROUND

Osteoarthritis (OA) is a multifaceted disease with many variables affecting diagnosis and progression. Topological data analysis (TDA) is a state-of-the-art big data analytics tool that can combine all variables into multidimensional space. TDA is used to simultaneously analyze imaging and gait analysis techniques.

PURPOSE

To identify biochemical and biomechanical biomarkers able to classify different disease progression phenotypes in subjects with and without radiographic signs of hip OA.

STUDY TYPE

Longitudinal study for comparison of progressive and nonprogressive subjects.

POPULATION

In all, 102 subjects with and without radiographic signs of hip osteoarthritis.

FIELD STRENGTH/SEQUENCE

3T, SPGR 3D MAPSS T_1ρ /T₂ , intermediate-weighted fat-suppressed fast spin-echo (FSE).

ASSESSMENT

Multidimensional data analysis including cartilage composition, bone shape, Kellgren-Lawrence (KL) classification of osteoarthritis, scoring hip osteoarthritis with MRI (SHOMRI), hip disability and osteoarthritis outcome score (HOOS).

STATISTICAL TESTS

Analysis done using TDA, Kolmogorov-Smirnov (KS) testing, and Benjamini-Hochberg to rank P-value results to correct for multiple comparisons.

RESULTS

Subjects in the later stages of the disease had an increased SHOMRI score (P < 0.0001), increased KL (P = 0.0012), and older age (P < 0.0001). Subjects in the healthier group showed intact cartilage and less pain. Subjects found between these two groups had a range of symptoms. Analysis of this subgroup identified knee biomechanics (P < 0.0001) as an initial marker of the disease that is noticeable before the morphological progression and degeneration. Further analysis of an OA subgroup with femoroacetabular impingement (FAI) showed anterior labral tears to be the most significant marker (P = 0.0017) between those FAI subjects with and without OA symptoms.

DATA CONCLUSION

The data-driven analysis obtained with TDA proposes new phenotypes of these subjects that partially overlap with the radiographic-based classical disease status classification and also shows the potential for further examination of an early onset biomechanical intervention.

LEVEL OF EVIDENCE

2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;48:1046-1058.