The KNee OsteoArthritis Prediction (KNOAP2020) challenge: An image analysis challenge to predict incident symptomatic radiographic knee osteoarthritis from MRI and X-ray images

Ensemble approach for predicting the diagnosis of osteoarthritis using physical activity factors

BACKGROUND

Osteoarthritis (OA) is a common degenerative disease of the joints. Risk factors for OA include non-modifiable factors such as age and sex, as well as modifiable factors like physical activity.

OBJECTIVES

this study aimed to construct a soft voting ensemble model to predict OA diagnosis using variables related to individual characteristics and physical activity and identify important variables in constructing the model through permutation importance.

METHODS

By using the recursive feature elimination, cross-validated technique, the variables with the best predictive performance were selected among variables, and an ensemble model combining RandomForest, XGBoost, and LightGBM algorithms was constructed. The predictive performance and permutation importance of each variable were evaluated.

RESULTS

The variables selected to construct the model were age, sex, grip strength, and quality of life, and the accuracy of the ensemble model was 0.828. The most important variable in constructing the model was age (0.199), followed by grip strength (0.053), quality of life (0.043), and sex (0.034).

CONCLUSION

The performance of the model for predicting OA was relatively good. If this model is continuously used and updated, it could be used to predict OA diagnosis, and the predictive performance of the OA model may be further improved.

The STOIC2021 COVID-19 AI challenge: Applying reusable training methodologies to private data

Challenges drive the state-of-the-art of automated medical image analysis. The quantity of public training data that they provide can limit the performance of their solutions. Public access to the training methodology for these solutions remains absent. This study implements the Type Three (T3) challenge format, which allows for training solutions on private data and guarantees reusable training methodologies. With T3, challenge organizers train a codebase provided by the participants on sequestered training data. T3 was implemented in the STOIC2021 challenge, with the goal of predicting from a computed tomography (CT) scan whether subjects had a severe COVID-19 infection, defined as intubation or death within one month. STOIC2021 consisted of a Qualification phase, where participants developed challenge solutions using 2000 publicly available CT scans, and a Final phase, where participants submitted their training methodologies with which solutions were trained on CT scans of 9724 subjects. The organizers successfully trained six of the eight Final phase submissions. The submitted codebases for training and running inference were released publicly. The winning solution obtained an area under the receiver operating characteristic curve for discerning between severe and non-severe COVID-19 of 0.815. The Final phase solutions of all finalists improved upon their Qualification phase solutions.

X-ray with finite element analysis is a viable alternative for MRI to predict knee osteoarthritis: Data from the Osteoarthritis Initiative

Magnetic resonance imaging (MRI) offers superior soft tissue contrast compared to clinical X-ray imaging methods, while also providing accurate three-dimensional (3D) geometries, it could be reasoned to be the best imaging modality to create 3D finite element (FE) geometries of the knee joint. However, MRI may not necessarily be superior for making tissue-level FE simulations of internal stress distributions within knee joint, which can be utilized to calculate subject-specific risk for the onset and development of knee osteoarthritis (KOA). Specifically, MRI does not provide any information about tissue stiffness, as the imaging is usually performed with the patient lying on their back. In contrast, native X-rays taken while the patient is standing indirectly reveal information of the overall health of the knee that is not seen in MRI. To determine the feasibility of X-ray workflow to generate FE models based on the baseline information (clinical image data and subject characteristics), we compared MRI and X-ray-based simulations of volumetric cartilage degenerations (N = 1213) against 8-year follow-up data. The results suggest that X-ray-based predictions of KOA are at least as good as MRI-based predictions for subjects with no previous knee injuries. This finding may have important implications for preventive care, as X-ray imaging is much more accessible than MRI.

Integrating Radiomics and Neural Networks for Knee Osteoarthritis Incidence Prediction

OBJECTIVE

Accurately predicting knee osteoarthritis (KOA) is essential for early detection and personalized treatment. We aimed to develop and test a magnetic resonance imaging (MRI)-based joint space (JS) radiomic model (RM) to predict radiographic KOA incidence through neural networks by integrating meniscus and femorotibial cartilage radiomic features.

METHODS

In the Osteoarthritis Initiative cohort, participants with knees without radiographic KOA at baseline but at high risk for radiographic KOA were included. Patients' knees developed radiographic KOA, whereas control knees did not over four years. We randomly split the participants into development and test cohorts (8:2) and extracted features from baseline three-dimensional double-echo steady-state sequence MRI. Model performance was evaluated using an area under the receiver operating characteristic curve (AUC), sensitivity, and specificity in both cohorts. Nine resident surgeons performed the reader experiment without/with the JS-RM aid.

RESULTS

Our study included 549 knees in the development cohort (275 knees of patients with KOA vs 274 knees of controls) and 137 knees in the test cohort (68 knees of patients with KOA vs 69 knees of controls). In the test cohort, JS-RM had a favorable accuracy for predicting the radiographic KOA incidence with an AUC of 0.931 (95% confidence interval [CI] 0.876-0.963), a sensitivity of 84.4% (95% CI 83.9%-84.9%), and a specificity of 85.6% (95% CI 85.2%-86.0%). The mean specificity and sensitivity of resident surgeons through MRI reading in predicting radiographic KOA incidence were increased from 0.474 (95% CI 0.333-0.614) and 0.586 (95% CI 0.429-0.743) without the assistance of JS-RM to 0.874 (95% CI 0.847-0.901) and 0.812 (95% CI 0.742-0.881) with JS-RM assistance, respectively (P < 0.001).

CONCLUSION

JS-RM integrating the features of the meniscus and cartilage showed improved predictive values in radiographic KOA incidence.

Two-Stage Classification of Future Knee Osteoarthritis Severity After 8 Years Using MRI: Data from the Osteoarthritis Initiative

Currently, there are no methods or tools available in clinical practice for classifying future knee osteoarthritis (KOA). In this study, we aimed to fill this gap by classifying future KOA into three severity grades: KL01 (healthy), KL2 (moderate), and KL34 (severe) based on the Kellgren-Lawrance scale. Due to the complex nature of multiclass classification, we used a two-stage method, which separates the classification task into two binary classifications (KL01 vs. KL234 in the first stage and KL2 vs. KL34 in the second stage). Our machine learning (ML) model used two Balanced Random Forest algorithms and was trained with gender, age, height, weight, and quantitative knee morphology obtained from magnetic resonance imaging. Our training dataset comprised longitudinal 8-year follow-up data of 1213 knees from the Osteoarthritis Initiative. Through extensive experimentation with various feature combinations, we identified KL baseline and weight as the most essential features, while gender surprisingly proved to be one of the least influential feature. Our best classification model generated a weighted F1 score of 79.0% and a balanced accuracy of 65.9%. The area under the receiver operating characteristic curve was 83.0% for healthy (KL01) versus moderate (KL2) or severe (KL34) KOA patients and 86.6% for moderate (KL2) versus severe (KL34) KOA patients. We found a statistically significant difference in performance between our two-stage classification model and the traditional single-stage classification model. These findings demonstrate the encouraging results of our two-stage classification model for multiclass KOA severity classification, suggesting its potential application in clinical settings in future.

Automated system for classifying uni-bicompartmental knee osteoarthritis by using redefined residual learning with convolutional neural network

Knee Osteoarthritis (OA) is one of the most common joint diseases that may cause physical disability associated with a significant personal and socioeconomic burden. X-ray imaging is the cheapest and most common method to detect Knee (OA). Accurate classification of knee OA can help physicians manage treatment efficiently and slow knee OA progression. This study aims to classify knee OA X-ray images according to anatomical types, such as uni or bicompartmental. The study proposes a deep learning model for classifying uni or bicompartmental knee OA based on redefined residual learning with CNN. The proposed model was trained, validated, and tested on a dataset containing 733 knee X-ray images (331 normal Knee images, 205 unicompartmental, and 197 bicompartmental knee images). The results show 61.81 % and 68.33 % for accuracy and specificity, respectively. Then, the performance of the proposed model was compared with different pre-trained CNNs. The proposed model achieved better results than all pre-trained CNNs.

ShapeMed-Knee: A Dataset and Neural Shape Model Benchmark for Modeling 3D Femurs

Analyzing anatomic shapes of tissues and organs is pivotal for accurate disease diagnostics and clinical decision-making. One prominent disease that depends on anatomic shape analysis is osteoarthritis, which affects 30 million Americans. To advance osteoarthritis diagnostics and prognostics, we introduce ShapeMed-Knee, a 3D shape dataset with 9,376 high-resolution, medical-imaging-based 3D shapes of both femur bone and cartilage. Besides data, ShapeMed-Knee includes two benchmarks for assessing reconstruction accuracy and five clinical prediction tasks that assess the utility of learned shape representations. Leveraging ShapeMed-Knee, we develop and evaluate a novel hybrid explicit-implicit neural shape model which achieves up to 40% better reconstruction accuracy than a statistical shape model and implicit neural shape model. Our hybrid models achieve state-of-the-art performance for preserving cartilage biomarkers; they're also the first models to successfully predict localized structural features of osteoarthritis, outperforming shape models and convolutional neural networks applied to raw magnetic resonance images and segmentations. The ShapeMed-Knee dataset provides medical evaluations to reconstruct multiple anatomic surfaces and embed meaningful disease-specific information. ShapeMed-Knee reduces barriers to applying 3D modeling in medicine, and our benchmarks highlight that advancements in 3D modeling can enhance the diagnosis and risk stratification for complex diseases. The dataset, code, and benchmarks will be made freely accessible.

Artificial intelligence to analyze magnetic resonance imaging in rheumatology

Rheumatic disorders present a global health challenge, marked by inflammation and damage to joints, bones, and connective tissues. Accurate, timely diagnosis and appropriate management are crucial for favorable patient outcomes. Magnetic resonance imaging (MRI) has become indispensable in rheumatology, but interpretation remains laborious and variable. Artificial intelligence (AI), including machine learning (ML) and deep learning (DL), offers a means to improve and advance MRI analysis. This review examines current AI applications in rheumatology MRI analysis, addressing diagnostic support, disease classification, activity assessment, and progression monitoring. AI demonstrates promise, with high sensitivity, specificity, and accuracy, achieving or surpassing expert performance. The review also discusses clinical implementation challenges and future research directions to enhance rheumatic disease diagnosis and management.

An osteoarthritis pathophysiological continuum revealed by molecular biomarkers

We aimed to identify serum biomarkers that predict knee osteoarthritis (OA) before the appearance of radiographic abnormalities in a cohort of 200 women. As few as six serum peptides, corresponding to six proteins, reached AUC 77% probability to distinguish those who developed OA from age-matched individuals who did not develop OA up to 8 years later. Prediction based on these blood biomarkers was superior to traditional prediction based on age and BMI (AUC 51%) or knee pain (AUC 57%). These results identify a prolonged molecular derangement of joint tissue before the onset of radiographic OA abnormalities consistent with an unresolved acute phase response. Among all 24 protein biomarkers predicting incident knee OA, the majority (58%) also predicted knee OA progression, revealing the existence of a pathophysiological "OA continuum" based on considerable similarity in the molecular pathophysiology of the progression to incident OA and the progression of established OA.

Deep learning in rheumatological image interpretation

Artificial intelligence techniques, specifically deep learning, have already affected daily life in a wide range of areas. Likewise, initial applications have been explored in rheumatology. Deep learning might not easily surpass the accuracy of classic techniques when performing classification or regression on low-dimensional numerical data. With images as input, however, deep learning has become so successful that it has already outperformed the majority of conventional image-processing techniques developed during the past 50 years. As with any new imaging technology, rheumatologists and radiologists need to consider adapting their arsenal of diagnostic, prognostic and monitoring tools, and even their clinical role and collaborations. This adaptation requires a basic understanding of the technical background of deep learning, to efficiently utilize its benefits but also to recognize its drawbacks and pitfalls, as blindly relying on deep learning might be at odds with its capabilities. To facilitate such an understanding, it is necessary to provide an overview of deep-learning techniques for automatic image analysis in detecting, quantifying, predicting and monitoring rheumatic diseases, and of currently published deep-learning applications in radiological imaging for rheumatology, with critical assessment of possible limitations, errors and confounders, and conceivable consequences for rheumatologists and radiologists in clinical practice.

Exploration of the potential mechanism of Duhuo Jisheng Decoction in osteoarthritis treatment by using network pharmacology and molecular dynamics simulation

In this study, the active ingredients of 15 Chinese herbal medicines of Duhuo Jisheng Decoction and their corresponding targets were obtained from the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database. The microarray data of Osteoarthritis (OA) were obtained through the GEO database for differential analysis and then a drug target-OA-related gene protein-protein interaction (PPI) network was established. The potential targets of Duhuo Jisheng Decoction in the treatment of OA were acquired by intersecting the OA-associated genes with the target genes of active ingredients. Random walk with restart (RWR) analysis of PPI networks was performed using potential targets as seed, and the top 50 genes of affinity coefficients were used as key action genes of Duhuo Jisheng Decoction in the treatment of OA. A drug-active ingredient-gene interaction network was established. AKT1, a key target of Duhuo Jisheng Decoction in the treatment of OA, was obtained by topological analysis of the gene interaction network. Molecular docking and molecular dynamics verified the binding of AKT1 to its corresponding drug active ingredients. CETSA assay demonstrated that the combination of luteolin and AKT1 increased the stability of AKT1, and the combination efficiency was high. In conclusion, the molecular mechanism of Duhuo Jisheng Decoction in treating OA featured by multiple components, targets, and pathways had been further investigated in this study, which is of significance for discovering as well as developing new drugs for this disease. The findings can also offer personalized diagnosis and treatment strategies for patients with OA in clinical practice.

Machine Learning Approaches to the Prediction of Osteoarthritis Phenotypes and Outcomes

PURPOSE OF REVIEW

Osteoarthritis (OA) is a complex heterogeneous disease with no effective treatments. Artificial intelligence (AI) and its subfield machine learning (ML) can be applied to data from different sources to (1) assist clinicians and patients in decision making, based on machine-learned evidence, and (2) improve our understanding of pathophysiology and mechanisms underlying OA, providing new insights into disease management and prevention. The purpose of this review is to improve the ability of clinicians and OA researchers to understand the strengths and limitations of AI/ML methods in applications to OA research.

RECENT FINDINGS

AI/ML can assist clinicians by prediction of OA incidence and progression and by providing tailored personalized treatment. These methods allow using multidimensional multi-source data to understand the nature of OA, to identify different OA phenotypes, and for biomarker discovery. We described the recent implementations of AI/ML in OA research and highlighted potential future directions and associated challenges.

Is the Surface Anatomy of the Popliteal Crease Related to Lower Extremity Alignment or Knee Osseous Morphology? A Radiographic Study

Background and objectives: The popliteal crease varies among individuals, and there has been no prior study on this aspect. We assumed that it may be associated with lower extremity alignment and osseous morphology. To demonstrate this, we conducted a radiographic analysis. Materials and Methods: The study was conducted on 121 knees of 63 patients, whose popliteal creases were well distinguished on clinical photographs. PCOA was defined as the angle between the longitudinal axis of the lower leg and the popliteal crease. Through the radiologic examinations performed, the HKA, MPTA, mLDFA, JLCA, MFCA/TEA, and PCA/TEA were measured. Pearson correlation analysis and multiple linear regression analysis were performed on the PCOA and the six radiologic measurements to analyze the relationship. Results: Pearson correlation analysis found HKA had the highest coefficient at 0.568. In multiple linear regression, only HKA was associated, excluding all other measurements. Conclusions: Popliteal crease obliquity is significantly associated with coronal plane lower extremity alignment and exhibits a stronger correlation than with underlying knee osseous morphology. If future research is conducted based on this, popliteal crease could serve as a valuable clue for predicting lower extremity alignment and the risk of osteoarthritis development.

Artificial intelligence in knee osteoarthritis: A comprehensive review for 2022

Objective

The aim of this literature review is to yield a comprehensive and exhaustive overview of the existing evidence and up-to-date applications of artificial intelligence for knee osteoarthritis.

Methods

A literature review was performed by using PubMed, Google Scholar, and IEEE databases for articles published in peer-reviewed journals in 2022. The articles focusing on the use of artificial intelligence in diagnosis and prognosis of knee osteoarthritis and accelerating the image acquisition were selected. For each selected study, the code availability, considered number of patients and knees, imaging type, covariates, grading type of osteoarthritis, models, validation approaches, objectives, and results were reviewed.

Results

395 articles were screened, and 35 of them were reviewed. Eight articles were based on diagnosis, six on prognosis prediction, three on classification, three on accelerated image acquisition, and 15 on segmentation of knee osteoarthritis. 57% of the articles used MRI, 26% radiography, 6% MRI together with radiography, 6% ultrasonography, and 6% only clinical data. 23% of the articles made the computer codes available for their study, and 26% used clinical data. External validation and nested cross-validation were used in 17% and 14% of articles, respectively.

Conclusions

The use of artificial intelligence provided a promising potential to enhance the detection and management of knee osteoarthritis. Translating the developed models into clinics is still in the early stages of development. The translation of artificial intelligence models is expected to be further examined in prospective studies to support clinicians in improving routine healthcare practice.

Identifying Severity Grading of Knee Osteoarthritis from X-ray Images Using an Efficient Mixture of Deep Learning and Machine Learning Models

Recently, many diseases have negatively impacted people's lifestyles. Among these, knee osteoarthritis (OA) has been regarded as the primary cause of activity restriction and impairment, particularly in older people. Therefore, quick, accurate, and low-cost computer-based tools for the early prediction of knee OA patients are urgently needed. In this paper, as part of addressing this issue, we developed a new method to efficiently diagnose and classify knee osteoarthritis severity based on the X-ray images to classify knee OA in (i.e., binary and multiclass) in order to study the impact of different class-based, which has not yet been addressed in previous studies. This will provide physicians with a variety of deployment options in the future. Our proposed models are basically divided into two frameworks based on applying pre-trained convolutional neural networks (CNN) for feature extraction as well as fine-tuning the pre-trained CNN using the transfer learning (TL) method. In addition, a traditional machine learning (ML) classifier is used to exploit the enriched feature space to achieve better knee OA classification performance. In the first one, we developed five classes-based models using a proposed pre-trained CNN for feature extraction, principal component analysis (PCA) for dimensionality reduction, and support vector machine (SVM) for classification. While in the second framework, a few changes were made to the steps in the first framework, the concept of TL was used to fine-tune the proposed pre-trained CNN from the first framework to fit the two classes, three classes, and four classes-based models. The proposed models are evaluated on X-ray data, and their performance is compared with the existing state-of-the-art models. It is observed through conducted experimental analysis to demonstrate the efficacy of the proposed approach in improving the classification accuracy in both multiclass and binary class-based in the OA case study. Nonetheless, the empirical results revealed that the fewer multiclass labels used, the better performance achieved, with the binary class labels outperforming all, which reached a 90.8% accuracy rate. Furthermore, the proposed models demonstrated their contribution to early classification in the first stage of the disease to help reduce its progression and improve people's quality of life.