Machine learning classification of OARSI-scored human articular cartilage using magnetic resonance imaging

MRI-derived Articular Cartilage Strains Predict Patient-Reported Outcomes Six Months Post Anterior Cruciate Ligament Reconstruction

Key terms

Multicontrast and Multiparametric, Magnetic Resonance Imaging, Osteoarthritis, Functional Biomechanical Imaging, Knee Joint Degeneration What is known about the subject: dualMRI has been used to quantify strains in a healthy human population in vivo and in cartilage explant models. Previously, OA severity, as determined by histology, has been positively correlated to increased shear and transverse strains in cartilage explants. What this study adds to existing knowledge: This is the first in vivo use of dualMRI in a participant demographic post-ACL reconstruction and at risk for developing osteoarthritis. This study shows that dualMRI-derived strains are more significantly correlated with patient-reported outcomes than any MRI relaxometry metric.

Background

Anterior cruciate ligament (ACL) injuries lead to an increased risk of osteoarthritis, characterized by altered cartilage tissue structure and function. Displacements under applied loading by magnetic resonance imaging (dualMRI) is a novel MRI technique that can be used to quantify mechanical strain in cartilage while undergoing a physiological load.

Purpose

To determine if strains derived by dualMRI and relaxometry measures correlate with patient-reported outcomes at six months post unilateral ACL reconstruction.

Study Design

Cohort study.

Methods

Quantitative MRI (T2, T2*, T1ρ) measurements and transverse, axial, and shear strains were quantified in the medial articular tibiofemoral cartilage of 35 participants at six-months post unilateral ACL reconstruction. The relationships between patient-reported outcomes (WOMAC, KOOS, MARS) and all qMRI relaxation times were quantified using general linear mixed-effects models. A combined best-fit multicontrast MRI model was then developed using backwards regression to determine the patient features and MRI metrics that are most predictive of patient-reported outcome scores.

Results

Higher femoral strains were significantly correlated with worse patient-reported functional outcomes. Femoral shear and transverse strains were positively correlated with six-month KOOS and WOMAC scores, after controlling for covariates. No relaxometry measures were correlated with patient-reported outcome scores. We identified the best-fit model for predicting WOMAC score using multiple MRI measures and patient-specific information, including sex, age, graft type, femoral transverse strain, femoral axial strain, and femoral shear strain. The best-fit model significantly predicted WOMAC score (p<0.001) better than any one individual MRI metric alone. When we regressed the model-predicted WOMAC scores against the patient-reported WOMAC scores, we found that our model achieved a goodness of fit exceeding 0.52.

Conclusions

This work presents the first use of dualMRI in vivo in a cohort of participants at risk for developing osteoarthritis. Our results indicate that both shear and transverse strains are highly correlated with patient-reported outcome severity could serve as novel imaging biomarkers to predict the development of osteoarthritis.

Articular Cartilage-From Basic Science Structural Imaging to Non-Invasive Clinical Quantitative Molecular Functional Information for AI Classification and Prediction

This review presents the changes that the imaging of articular cartilage has undergone throughout the last decades. It highlights that the expectation is no longer to image the structure and associated functions of articular cartilage but, instead, to devise methods for generating non-invasive, function-depicting images with quantitative information that is useful for detecting the early, pre-clinical stage of diseases such as primary or post-traumatic osteoarthritis (OA/PTOA). In this context, this review summarizes (a) the structure and function of articular cartilage as a molecular imaging target, (b) quantitative MRI for non-invasive assessment of articular cartilage composition, microstructure, and function with the current state of medical diagnostic imaging, (c), non-destructive imaging methods, (c) non-destructive quantitative articular cartilage live-imaging methods, (d) artificial intelligence (AI) classification of degeneration and prediction of OA progression, and (e) our contribution to this field, which is an AI-supported, non-destructive quantitative optical biopsy for early disease detection that operates on a digital tissue architectural fingerprint. Collectively, this review shows that articular cartilage imaging has undergone profound changes in the purpose and expectations for which cartilage imaging is used; the image is becoming an AI-usable biomarker with non-invasive quantitative functional information. This may aid in the development of translational diagnostic applications and preventive or early therapeutic interventions that are yet beyond our reach.

Systematic review of artificial intelligence tack in preventive orthopaedics: is the land coming soon?

PURPOSE

This study aims to describe and assess the current stage of the artificial intelligence (AI) technology integration in preventive orthopaedics of the knee and hip joints.

MATERIALS AND METHODS

The study was conducted in strict compliance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement. Literature databases were searched for articles describing the development and validation of AI models aimed at diagnosing knee or hip joint pathologies or predicting their development or course in patients. The quality of the included articles was assessed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) and QUADAS-AI tools.

RESULTS

56 articles were found that meet all the inclusion criteria. We identified two problems that block the full integration of AI into the routine of an orthopaedic physician. The first of them is related to the insufficient amount, variety and quality of data for training, and validation and testing of AI models. The second problem is the rarity of rational evaluation of models, which is why their real quality cannot always be evaluated.

CONCLUSION

The vastness and relevance of the studied topic are beyond doubt. Qualitative and optimally validated models exist in all four scopes considered. Additional optimization and confirmation of the models' quality on various datasets are the last technical stumbling blocks for creating usable software and integrating them into the routine of an orthopaedic physician.

Spatial Gradients of Quantitative MRI as Biomarkers for Early Detection of Osteoarthritis: Data From Human Explants and the Osteoarthritis Initiative

BACKGROUND

Healthy articular cartilage presents structural gradients defined by distinct zonal patterns through the thickness, which may be disrupted in the pathogenesis of several disorders. Analysis of textural patterns using quantitative MRI data may identify structural gradients of healthy or degenerating tissue that correlate with early osteoarthritis (OA).

PURPOSE

To quantify spatial gradients and patterns in MRI data, and to probe new candidate biomarkers for early severity of OA.

STUDY TYPE

Retrospective study.

SUBJECTS

Fourteen volunteers receiving total knee replacement surgery (eight males/two females/four unknown, average age ± standard deviation: 68.1 ± 9.6 years) and 10 patients from the OA Initiative (OAI) with radiographic OA onset (two males/eight females, average age ± standard deviation: 57.7 ± 9.4 years; initial Kellgren-Lawrence [KL] grade: 0; final KL grade: 3 over the 10-year study).

FIELD STRENGTH/SEQUENCE

3.0-T and 14.1-T, biomechanics-based displacement-encoded imaging, fast spin echo, multi-slice multi-echo T2 mapping.

ASSESSMENT

We studied structure and strain in cartilage explants from volunteers receiving total knee replacement, or structure in cartilage of OAI patients with progressive OA. We calculated spatial gradients of quantitative MRI measures (eg, T2) normal to the cartilage surface to enhance zonal variations. We compared gradient values against histologically OA severity, conventional relaxometry, and/or KL grades.

STATISTICAL TESTS

Multiparametric linear regression for evaluation of the relationship between residuals of the mixed effects models and histologically determined OA severity scoring, with a significance threshold at α = 0.05.

RESULTS

Gradients of individual relaxometry and biomechanics measures significantly correlated with OA severity, outperforming conventional relaxometry and strain metrics. In human explants, analysis of spatial gradients provided the strongest relationship to OA severity (R²  = 0.627). Spatial gradients of T2 from OAI data identified variations in radiographic (KL Grade 2) OA severity in single subjects, while conventional T2 alone did not.

DATA CONCLUSION

Spatial gradients of quantitative MRI data may improve the predictive power of noninvasive imaging for early-stage degeneration.

EVIDENCE LEVEL

1 TECHNICAL EFFICACY: Stage 1.

Zoledronic acid generates a spatiotemporal effect to attenuate osteoarthritis by inhibiting potential Wnt5a-associated abnormal subchondral bone resorption

This study aimed to determine the effects of zoledronic acid (ZOL) on OA in rats and explored the molecular mechanism of osteoclast activation in early OA. A knee OA rat model was designed by surgically destabilizing the medial meniscus (DMM). Seventy-two male rats were randomly assigned to Sham+phosphate-buffered saline (PBS), DMM+PBS, and DMM+ZOL groups; rats were administered with 100 μg/Kg ZOL or PBS, twice weekly for 4 weeks. After 2, 4, 8, and 12 weeks of OA induction, the thickness of the hyaline and calcified cartilage layers was calculated using hematoxylin and eosin staining, degenerated cartilage stained with Safranin O-fast green staining was evaluated and scored, tartrate-resistant acid phosphatase (TRAP)-stained osteoclasts were counted, changes in subchondral bone using micro-computed tomography were analyzed, and PINP and CTX-I levels were detected using enzyme-linked immunosorbent assay. Using these results, 18 male rats were randomly assigned to three groups. Four weeks after surgery, Wnt5a, RANKL, CXCL12, and NFATc1 protein levels were measured in subchondral bone using western blotting, and mRNA levels of genes related to osteoclastogenesis in subchondral bone were measured using quantitative polymerase chain reaction. Bone marrow-derived macrophages were isolated as osteoclast precursors, and cell differentiation, migration, and adhesion were assessed by TRAP staining and Transwell assays, revealing that DMM induced knee OA in rats. Progressive cartilage loss was observed 12 weeks after OA induction. Subchondral bone remodeling was dominated by bone resorption during early OA (within 4 weeks), whereas bone formation was increased 8 weeks later. ZOL suppressed bone resorption by inhibiting Wnt5a signaling in early OA, improved the imbalance of subchondral bone remodeling, reduced cartilage degeneration, and delayed OA progression. Additionally, ZOL delayed OA progression and reduced cartilage degeneration via a spatiotemporal effect in DMM-induced OA. Osteoclast activity in early OA might be associated with Wnt5a signaling, indicating a possible novel strategy for OA treatment.

A Comprehensive Survey on Bone Segmentation Techniques in Knee Osteoarthritis Research: From Conventional Methods to Deep Learning

Knee osteoarthritis (KOA) is a degenerative joint disease, which significantly affects middle-aged and elderly people. The majority of KOA is primarily based on hyaline cartilage change, according to medical images. However, technical bottlenecks such as noise, artifacts, and modality pose enormous challenges for an objective and efficient early diagnosis. Therefore, the correct prediction of arthritis is an essential step for effective diagnosis and the prevention of acute arthritis, where early diagnosis and treatment can assist to reduce the progression of KOA. However, predicting the development of KOA is a difficult and urgent problem that, if addressed, could accelerate the development of disease-modifying drugs, in turn helping to avoid millions of total joint replacement procedures each year. In knee joint research and clinical practice there are segmentation approaches that play a significant role in KOA diagnosis and categorization. In this paper, we seek to give an in-depth understanding of a wide range of the most recent methodologies for knee articular bone segmentation; segmentation methods allow the estimation of articular cartilage loss rate, which is utilized in clinical practice for assessing the disease progression and morphological change, ranging from traditional techniques to deep learning (DL)-based techniques. Moreover, the purpose of this work is to give researchers a general review of the currently available methodologies in the area. Therefore, it will help researchers who want to conduct research in the field of KOA, as well as highlight deficiencies and potential considerations in application in clinical practice. Finally, we highlight the diagnostic value of deep learning for future computer-aided diagnostic applications to complete this review.

Basic science approaches to common hand surgery problems

The field of hand surgery is constantly evolving to meet challenges of populations with increasing age and higher demands for active living. While our surgical care has improved over the last decades, it seems that future major improvement in outcomes of clinical treatment will come through advances in biologics and the translation of major discoveries in basic science. This article aims to provide an update on where basic science solutions may answer some of the most critical issues in hand surgery, with a focus on augmentation of tissue repair.

Imaging Manifestations and Evaluation of Postoperative Complications of Bone and Joint Infections under Deep Learning

To explore and evaluate the imaging manifestations of postoperative complications of bone and joint infections based on deep learning, a retrospective study was performed on 40 patients with bone and joint infections in the Department of Orthopedics of Orthopedics Hospital of Henan Province of Luoyang City. Sensitivity and Dice similarity coefficient (DSC) were used to evaluate the image results by convolutional neural network (CNN) algorithm. Imaging features of postoperative complications in 40 patients were analyzed. Then, three imaging methods were used to diagnose the features. Sensitivity and specificity were used to evaluate the diagnostic performance of three imaging methods for imaging features. Compared with professional doctors and biomarker algorithms, the sensitivity of CNN algorithm proposed was 90.6%, and DSC was 84.1%. Compared with traditional methods, the CNN algorithm has higher image resolution and wider and more accurate lesion area recognition and division. The three manifestations of soft tissue abscess, periosteum swelling, and bone damage were postoperative imaging features of bone and joint infections. In addition, compared with X-ray, CT examination and MRI examination were better for the examination of imaging characteristics. CT and MRI had higher sensitivity and specificity than X-ray. The experimental results show that CNN algorithm can effectively identify and divide pathological images and assist doctors to diagnose the images more efficiently in clinic.

Radiographic interpretation using high-resolution Cbct to diagnose degenerative temporomandibular joint disease

The objective of this study was to use high-resolution cone-beam computed images (hr- CBCT) to diagnose degenerative joint disease in asymptomatic and symptomatic subjects using the Diagnostic Criteria for Temporomandibular Disorders DC/TMD imaging criteria. This observational study comprised of 92 subjects age-sex matched and divided into two groups: clinical degenerative joint disease (c-DJD, n = 46) and asymptomatic control group (n = 46). Clinical assessment of the DJD and high-resolution CBCT images (isotropic voxel size of 0.08mm) of the temporomandibular joints were performed for each participant. An American Board of Oral and Maxillofacial Radiology certified radiologist and a maxillofacial radiologist used the DC/TMD imaging criteria to evaluate the radiographic findings, followed by a consensus of the radiographic evaluation. The two radiologists presented a high agreement (Cohen's Kappa ranging from 0.80 to 0.87) for all radiographic findings (osteophyte, erosion, cysts, flattening, and sclerosis). Five patients from the c- DJD group did not present radiographic findings, being then classified as arthralgia. In the asymptomatic control group, 82.6% of the patients presented radiographic findings determinant of DJD and were then classified as osteoarthrosis or overdiagnosis. In conclusion, our results showed a high number of radiographic findings in the asymptomatic control group, and for this reason, we suggest that there is a need for additional imaging criteria to classify DJD properly in hr-CBCT images.

Machine learning algorithms trained with pre-hospital acquired history-taking data can accurately differentiate diagnoses in patients with hip complaints

Background and purpose - Machine learning (ML) techniques are a form of artificial intelligence able to analyze big data. Analyzing the outcome of (digital) questionnaires, ML might recognize different patterns in answers that might relate to different types of pathology. With this study, we investigated the proof-of-principle of ML-based diagnosis in patients with hip complaints using a digital questionnaire and the Kellgren and Lawrence (KL) osteoarthritis score.Patients and methods - 548 patients (> 55 years old) scheduled for consultation of hip complaints were asked to participate in this study and fill in an online questionnaire. Our questionnaire consists of 27 questions related to general history-taking and validated patient-related outcome measures (Oxford Hip Score and a Numeric Rating Scale for pain). 336 fully completed questionnaires were related to their classified diagnosis (either hip osteoarthritis, bursitis or tendinitis, or other pathology). Different AI techniques were used to relate questionnaire outcome and hip diagnoses. Resulting area under the curve (AUC) and classification accuracy (CA) are reported to identify the best scoring AI model. The accuracy of different ML models was compared using questionnaire outcome with and without radiologic KL scores for degree of osteoarthritis.Results - The most accurate ML model for diagnosis of patients with hip complaints was the Random Forest model (AUC 82%, 95% CI 0.78-0.86; CA 69%, CI 0.64-0.74) and most accurate analysis with addition of KL scores was with a Support Vector Machine model (AUC 89%, CI 0.86-0.92; CA 83%, CI 0.79-0.87).Interpretation - Analysis of self-reported online questionnaires related to hip complaints can differentiate between basic hip pathologies. The addition of radiological scores for osteoarthritis further improves these outcomes.

Efficient Detection of Knee Anterior Cruciate Ligament from Magnetic Resonance Imaging Using Deep Learning Approach

The most commonly injured ligament in the human body is an anterior cruciate ligament (ACL). ACL injury is standard among the football, basketball and soccer players. The study aims to detect anterior cruciate ligament injury in an early stage via efficient and thorough automatic magnetic resonance imaging without involving radiologists, through a deep learning method. The proposed approach in this paper used a customized 14 layers ResNet-14 architecture of convolutional neural network (CNN) with six different directions by using class balancing and data augmentation. The performance was evaluated using accuracy, sensitivity, specificity, precision and F1 score of our customized ResNet-14 deep learning architecture with hybrid class balancing and real-time data augmentation after 5-fold cross-validation, with results of 0.920%, 0.916%, 0.946%, 0.916% and 0.923%, respectively. For our proposed ResNet-14 CNN the average area under curves (AUCs) for healthy tear, partial tear and fully ruptured tear had results of 0.980%, 0.970%, and 0.999%, respectively. The proposing diagnostic results indicated that our model could be used to detect automatically and evaluate ACL injuries in athletes using the proposed deep-learning approach.

Classification, Categorization, and Algorithms for Articular Cartilage Defects

There is a critical unmet need in the clinical implementation of valid preventative and therapeutic strategies for patients with articular cartilage pathology based on the significant gap in understanding of the relationships between diagnostic data, disease progression, patient-related variables, and symptoms. In this article, the current state of classification and categorization for articular cartilage pathology is discussed with particular focus on machine learning methods and the authors propose a bedside-bench-bedside approach with highly quantitative techniques as a solution to these hurdles. Leveraging computational learning with available data toward articular cartilage pathology patient phenotyping holds promise for clinical research and will likely be an important tool to identify translational solutions into evidence-based clinical applications to benefit patients. Recommendations for successful implementation of these approaches include using standardized definitions of articular cartilage, to include characterization of depth, size, location, and number; using measurements that minimize subjectivity or validated patient-reported outcome measures; considering not just the articular cartilage pathology but the whole joint, and the patient perception and perspective. Application of this approach through a multistep process by a multidisciplinary team of clinicians and scientists holds promise for validating disease mechanism-based phenotypes toward clinically relevant understanding of articular cartilage pathology for evidence-based application to orthopaedic practice.

Enabling early detection of osteoarthritis from presymptomatic cartilage texture maps via transport-based learning

Many diseases have no visual cues in the early stages, eluding image-based detection. Today, osteoarthritis (OA) is detected after bone damage has occurred, at an irreversible stage of the disease. Currently no reliable method exists for OA detection at a reversible stage. We present an approach that enables sensitive OA detection in presymptomatic individuals. Our approach combines optimal mass transport theory with statistical pattern recognition. Eighty-six healthy individuals were selected from the Osteoarthritis Initiative, with no symptoms or visual signs of disease on imaging. On 3-y follow-up, a subset of these individuals had progressed to symptomatic OA. We trained a classifier to differentiate progressors and nonprogressors on baseline cartilage texture maps, which achieved a robust test accuracy of 78% in detecting future symptomatic OA progression 3 y prior to symptoms. This work demonstrates that OA detection may be possible at a potentially reversible stage. A key contribution of our work is direct visualization of the cartilage phenotype defining predictive ability as our technique is generative. We observe early biochemical patterns of fissuring in cartilage that define future onset of OA. In the future, coupling presymptomatic OA detection with emergent clinical therapies could modify the outcome of a disease that costs the United States healthcare system $16.5 billion annually. Furthermore, our technique is broadly applicable to earlier image-based detection of many diseases currently diagnosed at advanced stages today.

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.

A Comparative Systematic Literature Review on Knee Bone Reports from MRI, X-rays and CT Scans Using Deep Learning and Machine Learning Methodologies

The purpose of this research was to provide a “systematic literature review” of knee bone reports that are obtained by MRI, CT scans, and X-rays by using deep learning and machine learning techniques by comparing different approaches—to perform a comprehensive study on the deep learning and machine learning methodologies to diagnose knee bone diseases by detecting symptoms from X-ray, CT scan, and MRI images. This study will help those researchers who want to conduct research in the knee bone field. A comparative systematic literature review was conducted for the accomplishment of our work. A total of 32 papers were reviewed in this research. Six papers consist of X-rays of knee bone with deep learning methodologies, five papers cover the MRI of knee bone using deep learning approaches, and another five papers cover CT scans of knee bone with deep learning techniques. Another 16 papers cover the machine learning techniques for evaluating CT scans, X-rays, and MRIs of knee bone. This research compares the deep learning methodologies for CT scan, MRI, and X-ray reports on knee bone, comparing the accuracy of each technique, which can be used for future development. In the future, this research will be enhanced by comparing X-ray, CT-scan, and MRI reports of knee bone with information retrieval and big data techniques. The results show that deep learning techniques are best for X-ray, MRI, and CT scan images of the knee bone to diagnose diseases.

Osteoarthritis of the Temporomandibular Joint can be diagnosed earlier using biomarkers and machine learning

After chronic low back pain, Temporomandibular Joint (TMJ) disorders are the second most common musculoskeletal condition affecting 5 to 12% of the population, with an annual health cost estimated at $4 billion. Chronic disability in TMJ osteoarthritis (OA) increases with aging, and the main goal is to diagnosis before morphological degeneration occurs. Here, we address this challenge using advanced data science to capture, process and analyze 52 clinical, biological and high-resolution CBCT (radiomics) markers from TMJ OA patients and controls. We tested the diagnostic performance of four machine learning models: Logistic Regression, Random Forest, LightGBM, XGBoost. Headaches, Range of mouth opening without pain, Energy, Haralick Correlation, Entropy and interactions of TGF-β1 in Saliva and Headaches, VE-cadherin in Serum and Angiogenin in Saliva, VE-cadherin in Saliva and Headaches, PA1 in Saliva and Headaches, PA1 in Saliva and Range of mouth opening without pain; Gender and Muscle Soreness; Short Run Low Grey Level Emphasis and Headaches, Inverse Difference Moment and Trabecular Separation accurately diagnose early stages of this clinical condition. Our results show the XGBoost + LightGBM model with these features and interactions achieves the accuracy of 0.823, AUC 0.870, and F1-score 0.823 to diagnose the TMJ OA status. Thus, we expect to boost future studies into osteoarthritis patient-specific therapeutic interventions, and thereby improve the health of articular joints.

Functional MRI for evaluation of hyaline cartilage extracelullar matrix, a physiopathological-based approach

MRI of articular cartilage (AC) integrity has potential to become a biomarker for osteoarthritis progression. Traditional MRI sequences evaluate AC morphology, allowing for the measurement of thickness and its change over time. In the last two decades, more advanced, dedicated MRI cartilage sequences have been developed aiming to assess AC matrix composition non-invasively and detect early changes in cartilage not captured on morphological sequences. T2-mapping and T1ρ sequences can be used to estimate the relaxation times of water inside the AC. These sequences have been introduced into clinical protocols and show promising results for cartilage assessment. Extracelullar matrix can also be assessed using diffusion-weighted imaging and diffusion tensor imaging as the movement of water is limited by the presence of extracellular matrix in AC. Specific techniques for glycosaminoglycans (GAG) evaluation, such as delayed gadolinium enhanced MRI of cartilage or Chemical Exchange Saturation Transfer imaging of GAG, as well as sodium imaging have also shown utility in the detection of AC damage. This manuscript provides an educational update on the physical principles behind advanced AC MRI techniques as well as a comprehensive review of the strengths and weaknesses of each approach. Current clinical applications and potential future applications of these techniques are also discussed.

Cell type discrimination based on image features of molecular component distribution

Machine learning-based cell classifiers use cell images to automate cell-type discrimination, which is increasingly becoming beneficial in biological studies and biomedical applications. Brightfield or fluorescence images are generally employed as the classifier input variables. We propose to use Raman spectral images and a method to extract features from these spatial patterns and explore the value of this information for cell discrimination. Raman images provide information regarding distribution of chemical compounds of the considered biological entity. Since each spectral wavelength can be used to reconstruct the distribution of a given compound, spectral images provide multiple channels of information, each representing a different pattern, in contrast to brightfield and fluorescence images. Using a dataset of single living cells, we demonstrate that the spatial information can be ranked by a Fisher discriminant score, and that the top-ranked features can accurately classify cell types. This method is compared with the conventional Raman spectral analysis. We also propose to combine the information from whole spectral analyses and selected spatial features and show that this yields higher classification accuracy. This method provides the basis for a novel and systematic analysis of cell-type investigation using Raman spectral imaging, which may benefit several studies and biomedical applications.

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.

A machine learning approach for the identification of new biomarkers for knee osteoarthritis development in overweight and obese women

OBJECTIVE

Knee osteoarthritis (OA) is among the higher contributors to global disability. Despite its high prevalence, currently, there is no cure for this disease. Furthermore, the available diagnostic approaches have large precision errors and low sensitivity. Therefore, there is a need for new biomarkers to correctly identify early knee OA.

METHOD

We have created an analytics pipeline based on machine learning to identify small models (having few variables) that predict the 30-months incidence of knee OA (using multiple clinical and structural OA outcome measures) in overweight middle-aged women without knee OA at baseline. The data included clinical variables, food and pain questionnaires, biochemical markers (BM) and imaging-based information.

RESULTS

All the models showed high performance (AUC > 0.7) while using only a few variables. We identified both the importance of each variable within the models as well its direction. Finally, we compared the performance of two models with the state-of-the-art approaches available in the literature.

CONCLUSIONS

We showed the potential of applying machine learning to generate predictive models for the knee OA incidence. Imaging-based information were found particularly important in the proposed models. Furthermore, our analysis confirmed the relevance of known BM for knee OA. Overall, we propose five highly predictive small models that can be possibly adopted for an early prediction of knee OA.

Predicting early symptomatic osteoarthritis in the human knee using machine learning classification of magnetic resonance images from the osteoarthritis initiative

The purpose of this study is to evaluate the ability of a machine learning algorithm to classify in vivo magnetic resonance images (MRI) of human articular cartilage for development of osteoarthritis (OA). Sixty-eight subjects were selected from the osteoarthritis initiative (OAI) control and incidence cohorts. Progression to clinical OA was defined by the development of symptoms as quantified by the Western Ontario and McMaster Universities Arthritis (WOMAC) questionnaire 3 years after baseline evaluation. Multi-slice T2 -weighted knee images, obtained through the OAI, of these subjects were registered using a nonlinear image registration algorithm. T2 maps of cartilage from the central weight bearing slices of the medial femoral condyle were derived from the registered images using the multiple available echo times and were classified for "progression to symptomatic OA" using the machine learning tool, weighted neighbor distance using compound hierarchy of algorithms representing morphology (WND-CHRM). WND-CHRM classified the isolated T2 maps for the progression to symptomatic OA with 75% accuracy.

CLINICAL SIGNIFICANCE

Machine learning algorithms applied to T2 maps have the potential to provide important prognostic information for the development of OA. © 2017 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 35:2243-2250, 2017.