Automatic quantification of joint space narrowing and erosions in rheumatoid arthritis

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.

A Stepwise Approach to Analyzing Musculoskeletal Imaging Data With Artificial Intelligence

The digitization of medical records and expanding electronic health records has created an era of "Big Data" with an abundance of available information ranging from clinical notes to imaging studies. In the field of rheumatology, medical imaging is used to guide both diagnosis and treatment of a wide variety of rheumatic conditions. Although there is an abundance of data to analyze, traditional methods of image analysis are human resource intensive. Fortunately, the growth of artificial intelligence (AI) may be a solution to handle large datasets. In particular, computer vision is a field within AI that analyzes images and extracts information. Computer vision has impressive capabilities and can be applied to rheumatologic conditions, necessitating a need to understand how computer vision works. In this article, we provide an overview of AI in rheumatology and conclude with a five step process to plan and conduct research in the field of computer vision. The five steps include (1) project definition, (2) data handling, (3) model development, (4) performance evaluation, and (5) deployment into clinical care.

Corrections to "A Sub-Pixel Accurate Quantification of Joint Space Narrowing Progression in Rheumatoid Arthritis"

Presents corrections to the article "A Sub-Pixel Accurate Quantification of Joint Space Narrowing Progression in Rheumatoid Arthritis".

Implementation of artificial intelligence models in magnetic resonance imaging with focus on diagnosis of rheumatoid arthritis and axial spondyloarthritis: narrative review

Early diagnosis in rheumatoid arthritis (RA) and axial spondyloarthritis (axSpA) is essential to initiate timely interventions, such as medication and lifestyle changes, preventing irreversible joint damage, reducing symptoms, and improving long-term outcomes for patients. Since magnetic resonance imaging (MRI) of the wrist and hand, in case of RA and MRI of the sacroiliac joints (SIJ) in case of axSpA can identify inflammation before it is clinically discernible, this modality may be crucial for early diagnosis. Artificial intelligence (AI) techniques, together with machine learning (ML) and deep learning (DL) have quickly evolved in the medical field, having an important role in improving diagnosis, prognosis, in evaluating the effectiveness of treatment and monitoring the activity of rheumatic diseases through MRI. The improvements of AI techniques in the last years regarding imaging interpretation have demonstrated that a computer-based analysis can equal and even exceed the human eye. The studies in the field of AI have investigated how specific algorithms could distinguish between tissues, diagnose rheumatic pathology and grade different signs of early inflammation, all of them being crucial for tracking disease activity. The aim of this paper is to highlight the implementation of AI models in MRI with focus on diagnosis of RA and axSpA through a literature review.

Drug Repositioning of Metformin Encapsulated in PLGA Combined with Photothermal Therapy Ameliorates Rheumatoid Arthritis

Purpose

Rheumatoid arthritis (RA) is a highly prevalent form of autoimmune disease that affects nearly 1% of the global population by causing severe cartilage damage and inflammation. Despite its prevalence, previous efforts to prevent the perpetuation of RA have been hampered by therapeutics' cytotoxicity and poor delivery to target cells. The present study exploited drug repositioning and nanotechnology to convert metformin, a widely used antidiabetic agent, into an anti-rheumatoid arthritis drug by designing poly(lactic-co-glycolic acid) (PLGA)-based spheres. Moreover, this study also explored the thermal responsiveness of the IL-22 receptor, a key regulator of Th-17, to incorporate photothermal therapy (PTT) into the nanodrug treatment.

Materials and Methods

PLGA nanoparticles were synthesized using the solvent evaporation method, and metformin and indocyanine green (ICG) were encapsulated in PLGA in a dropwise manner. The nanodrug's in vitro anti-inflammatory properties were examined in J744 and FLS via real-time PCR. PTT was induced by an 808 nm near-infrared (NIR) laser, and the anti-RA effects of the nanodrug with PTT were evaluated in DBA/1 collagen-induced arthritis (CIA) mice models. Further evaluation of anti-RA properties was carried out using flow cytometry, immunofluorescence analysis, and immunohistochemical analysis.

Results

The encapsulation of metformin into PLGA allowed the nanodrug to enter the target cells via macropinocytosis and clathrin-mediated endocytosis. Metformin-encapsulated PLGA (PLGA-MET) demonstrated promising anti-inflammatory effects by decreasing the expression of pro-inflammatory cytokines (IL-1β, IL-6, and TNF-α), increasing the expression of anti-inflammatory cytokines (IL-10 and IL-4), and promoting the polarization of M1 to M2 macrophages in J774 cells. The treatment of the nanodrug with PTT exhibited more potent anti-inflammatory effects than free metformin or PLGA-MET in CIA mice models.

Conclusion

These results demonstrated that PLGA-encapsulated metformin treatment with PTT can effectively ameliorate inflammation in a spatiotemporal manner.

Relationship between condylar surface CT value in coronal plane and condylar morphology in jaw deformity patients

OBJECTIVES

This study aimed to examine the relationship between condylar surface computed tomography (CT) values in the coronal plane and condylar morphology in patients with jaw deformities classes II and III before and after orthognathic surgery.

MATERIALS AND METHODS

The maximum CT values (pixel values) at three points on the condylar surface, height, and joint space were measured on the coronal plane. The condylar width, thickness, and angle were measured on the horizontal plane preoperatively and at 1 year postoperatively.

RESULTS

A total of 112 temporomandibular joints of 56 female patients were divided into two groups according to skeletal class (56 joints each in class II and class III). The maximum CT values of class II were higher than those of class III at the medial, central, and lateral sites on the condylar surface, preoperatively and at 1 year postoperatively (P < 0.05). CT values of the condylar surface were significantly negatively correlated with the condylar heights at the center and lateral sites preoperatively and at 1 year postoperatively (P < 0.05).

CONCLUSIONS

Condylar surface CT values in the coronal plane are associated with condylar morphology, including condylar height.

Imaging in inflammatory arthritis: progress towards precision medicine

Imaging techniques such as ultrasonography and MRI have gained ground in the diagnosis and management of inflammatory arthritis, as these imaging modalities allow a sensitive assessment of musculoskeletal inflammation and damage. However, these techniques cannot discriminate between disease subsets and are currently unable to deliver an accurate prediction of disease progression and therapeutic response in individual patients. This major shortcoming of today's technology hinders a targeted and personalized patient management approach. Technological advances in the areas of high-resolution imaging (for example, high-resolution peripheral quantitative computed tomography and ultra-high field MRI), functional and molecular-based imaging (such as chemical exchange saturation transfer MRI, positron emission tomography, fluorescence optical imaging, optoacoustic imaging and contrast-enhanced ultrasonography) and artificial intelligence-based data analysis could help to tackle these challenges. These new imaging approaches offer detailed anatomical delineation and an in vivo and non-invasive evaluation of the immunometabolic status of inflammatory reactions, thereby facilitating an in-depth characterization of inflammation. By means of these developments, the aim of earlier diagnosis, enhanced monitoring and, ultimately, a personalized treatment strategy looms closer.

A deep registration method for accurate quantification of joint space narrowing progression in rheumatoid arthritis

Rheumatoid arthritis (RA) is a chronic autoimmune inflammatory disease that leads to progressive articular destruction and severe disability. Joint space narrowing (JSN) has been regarded as an important indicator for RA progression and has received significant attention. Radiology plays a crucial role in the diagnosis and monitoring of RA through the assessment of joint space. A new framework for monitoring joint space by quantifying joint space narrowing (JSN) progression through image registration in radiographic images has emerged as a promising research direction. This framework offers the advantage of high accuracy; however, challenges still exist in reducing mismatches and improving reliability. In this work, we utilize a deep intra-subject rigid registration network to automatically quantify JSN progression in the early stages of RA. In our experiments, the mean-square error of the Euclidean distance between the moving and fixed images was 0.0031, the standard deviation was 0.0661 mm and the mismatching rate was 0.48%. Our method achieves sub-pixel level accuracy, surpassing manual measurements significantly. The proposed method is robust to noise, rotation and scaling of joints. Moreover, it provides misalignment visualization, which can assist radiologists and rheumatologists in assessing the reliability of quantification, exhibiting potential for future clinical applications. As a result, we are optimistic that our proposed method will make a significant contribution to the automatic quantification of JSN progression in RA. Code is available at https://github.com/pokeblow/Deep-Registration-QJSN-Finger.git.

IoT-based disease prediction using machine learning

COVID-19 disrupted lives and livelihoods and affected various sectors of the economy. One such domain was the already overburdened healthcare sector, which faced fresh challenges as the number of patients rose exponentially and became difficult to deal with. In such a scenario, telemedicine, teleconsultation, and virtual consultation became increasingly common to comply with social distancing norms. To overcome this pressing need of increasing 'remote' consultations in the 'post-COVID' era, the Internet of Things (IoT) has the potential to play a pivotal role, and this present paper attempts to develop a novel system that implements the most efficient machine learning (ML) algorithm and takes input from the patients such as symptoms, audio recordings, available medical reports, and other histories of illnesses to accurately and holistically predict the disease that the patients are suffering from. A few of the symptoms, such as fever and low blood oxygen, can also be measured via sensors using Arduino and ESP8266. It then provides for the appropriate diagnosis and treatment of the disease based on its constantly updated database, which can be developed as an application-based or website-based platform.

Automatic 3D joint erosion detection for the diagnosis and monitoring of rheumatoid arthritis using hand HR-pQCT images

Rheumatoid arthritis (RA) is a chronic inflammatory disease. It leads to bone erosion in joints and other complications, which severely affect patients' quality of life. To accurately diagnose and monitor the progression of RA, quantitative imaging and analysis tools are desirable. High-resolution peripheral quantitative computed tomography (HR-pQCT) is such a promising tool for monitoring disease progression in RA. However, automatic erosion detection tools using HR-pQCT images are not yet available. Inspired by the consensus among radiologists on the erosions in HR-pQCT images, in this paper we define erosion as the significant concave regions on the cortical layer, and develop a model-based 3D automatic erosion detection method. It mainly consists of two steps: constructing closed cortical surface, and detecting erosion regions on the surface. In the first step, we propose an initialization-robust region competition methods for joint segmentation, and then fill the surface gaps by using joint bone separation and curvature-based surface alignment. In the second step, we analyze the curvature information of each voxel, and then aggregate the candidate voxels into concave surface regions and use the shape information of the regions to detect the erosions. We perform qualitative assessments of the new method using 59 well-annotated joint volumes. Our method has shown satisfactory and consistent performance compared with the annotations provided by medical experts.

Fully automatic software for detecting radiographic joint space narrowing progression in rheumatoid arthritis: phantom study and comparison with visual assessment

PURPOSE

We have developed an in-house software equipped with partial image phase-only correlation (PIPOC) which can automatically quantify radiographic joint space narrowing (JSN) progression. The purpose of this study was to evaluate the software in phantom and clinical assessments.

MATERIALS AND METHODS

In the phantom assessment, the software's performance on radiographic images was compared to the joint space width (JSW) difference using a micrometer as ground truth. A phantom simulating a finger joint was scanned underwater. In the clinical assessment, 15 RA patients were included. The software measured the radiological progression of the finger joints between baseline and the 52nd week. The cases were also evaluated with the Genant-modified Sharp score (GSS), a conventional visual scoring method. We also quantitatively assessed these joints' synovial vascularity (SV) on power Doppler ultrasonography (0, 8, 20 and 52 weeks).

RESULTS

In the phantom assessment, the PIPOC software could detect changes in JSN with a smallest detectable difference of 0.044 mm at 0.1 mm intervals. In the clinical assessment, the JSW change of the joints with GSS progression detected by the software was significantly greater than those without GSS progression (p = 0.004). The JSW change of joints with positive SV at baseline was significantly higher than those with negative SV (p = 0.024).

CONCLUSION

Our in-house software equipped with PIPOC can automatically and quantitatively detect slight radiographic changes of JSW in clinically inactive RA patients.

Artificial Intelligence in Rheumatoid Arthritis: Current Status and Future Perspectives: A State-of-the-Art Review

Investigation of the potential applications of artificial intelligence (AI), including machine learning (ML) and deep learning (DL) techniques, is an exponentially growing field in medicine and healthcare. These methods can be critical in providing high-quality care to patients with chronic rheumatological diseases lacking an optimal treatment, like rheumatoid arthritis (RA), which is the second most prevalent autoimmune disease. Herein, following reviewing the basic concepts of AI, we summarize the advances in its applications in RA clinical practice and research. We provide directions for future investigations in this field after reviewing the current knowledge gaps and technical and ethical challenges in applying AI. Automated models have been largely used to improve RA diagnosis since the early 2000s, and they have used a wide variety of techniques, e.g., support vector machine, random forest, and artificial neural networks. AI algorithms can facilitate screening and identification of susceptible groups, diagnosis using omics, imaging, clinical, and sensor data, patient detection within electronic health record (EHR), i.e., phenotyping, treatment response assessment, monitoring disease course, determining prognosis, novel drug discovery, and enhancing basic science research. They can also aid in risk assessment for incidence of comorbidities, e.g., cardiovascular diseases, in patients with RA. However, the proposed models may vary significantly in their performance and reliability. Despite the promising results achieved by AI models in enhancing early diagnosis and management of patients with RA, they are not fully ready to be incorporated into clinical practice. Future investigations are required to ensure development of reliable and generalizable algorithms while they carefully look for any potential source of bias or misconduct. We showed that a growing body of evidence supports the potential role of AI in revolutionizing screening, diagnosis, and management of patients with RA. However, multiple obstacles hinder clinical applications of AI models. Incorporating the machine and/or deep learning algorithms into real-world settings would be a key step in the progress of AI in medicine.

Practical Implementation of Artificial Intelligence-Based Deep Learning and Cloud Computing on the Application of Traditional Medicine and Western Medicine in the Diagnosis and Treatment of Rheumatoid Arthritis

Rheumatoid arthritis (RA), an autoimmune disease of unknown etiology, is a serious threat to the health of middle-aged and elderly people. Although western medicine, traditional medicine such as traditional Chinese medicine, Tibetan medicine and other ethnic medicine have shown certain advantages in the diagnosis and treatment of RA, there are still some practical shortcomings, such as delayed diagnosis, improper treatment scheme and unclear drug mechanism. At present, the applications of artificial intelligence (AI)-based deep learning and cloud computing has aroused wide attention in the medical and health field, especially in screening potential active ingredients, targets and action pathways of single drugs or prescriptions in traditional medicine and optimizing disease diagnosis and treatment models. Integrated information and analysis of RA patients based on AI and medical big data will unquestionably benefit more RA patients worldwide. In this review, we mainly elaborated the application status and prospect of AI-assisted deep learning and cloud computation-oriented western medicine and traditional medicine on the diagnosis and treatment of RA in different stages. It can be predicted that with the help of AI, more pharmacological mechanisms of effective ethnic drugs against RA will be elucidated and more accurate solutions will be provided for the treatment and diagnosis of RA in the future.

An Efficient CNN for Hand X-Ray Classification of Rheumatoid Arthritis

Hand Radiography (RA) is one of the prime tests for checking the progress of rheumatoid joint inflammation in human bone joints. Recognizing the specific phase of RA is a difficult assignment, as human abilities regularly curb the techniques for it. Convolutional neural network (CNN) is the center for hand recognition for recognizing complex examples. The human cerebrum capacities work in a high-level way, so CNN has been planned depending on organic neural-related organizations in humans for imitating its unpredictable capacities. This article accordingly presents the convolutional neural network (CNN) which has the ability to naturally gain proficiency with the qualities and anticipate the class of hand radiographs from an expansive informational collection. The reproduction of the CNN halfway layers, which depict the elements of the organization, is likewise appeared. For arrangement of the model, a dataset of 290 radiography images is utilized. The result indicates that hand X-rays are rated with an accuracy of 94.46% by the proposed methodology. Our experiments show that the network sensitivity is observed to be 0.95 and the specificity is observed to be 0.82.

A long-term longitudinal study of the osteoarthritic changes to the temporomandibular joint evaluated using a novel three-dimensional superimposition method

The aim of this study was to assess the changes in individual condyles from 5 to 8 years in patients with temporomandibular joint (TMJ) osteoarthritis using 3-dimensional cone beam computed tomography (3D CBCT) reconstruction and superimposition. To assess the longitudinal TMJ changes, CBCT was performed at initial (T₀) and final (T₂) timepoints that were at least 5 years apart and at a middle (T₁) timepoint. To improve the accuracy, we used a novel superimposition method that designated areas of coronoid process and mandibular body. The differences in the resorption and apposition amounts were calculated between each model via maximum surface distances. The greatest resorption and apposition observed were − 7.48 and 2.66 mm, respectively. Evaluation of the changes in each condyle showed that osteoarthritis leads to both resorption and apposition. Resorption was mainly observed in the superior region, while high apposition rates were observed (in decreasing order) in the posterior, lateral, and anterior regions. The medial parts showed greater apposition than the lateral parts in all regions. Our superimposition method reveals that both resorption and apposition were observed in condyles with TMJ osteoarthritis, and resorption/apposition patterns depend on the individual condyle and its sites.

Sind röntgenologische Scoring-Methoden als Parameter zur Verlaufsbeurteilung der rheumatoiden Arthritis noch zeitgemäß?

Die radiologische Progression beschreibt das Ausmaß der Gelenkzerstörung im Verlauf einer rheumatoiden Arthritis. Zur Quantifizierung der radiologischen Progression werden Scoring-Methoden (z. B. van der Heijde Modifikation des Sharp-Score) eingesetzt. In verschiedenen Studien zu biologischen- bzw. target-synthetischen Disease Modifying Anti-Rheumatic Drugs gelang nur unzureichend eine Differenzierung der radiologischen Progression. Zudem finden die Scores oft keinen routinemäßigen Einsatz in der klinischen Entscheidungsfindung. Durch die computerbasierte Analyse von Handröntgenaufnahmen ist eine valide Quantifizierung der radiologischen Progression und die zuverlässige Bewertung von Therapieeffekten möglich. Somit stellen die computerbasierten Methoden eine vielversprechende Alternative in der Quantifizierung der radiologischen Progression dar.

Validation of Fully Automatic Quantitative Software for Finger Joint Space Narrowing Progression for Rheumatoid Arthritis Patients

In rheumatoid arthritis (RA), the radiographic progression of joint space narrowing (JSN) is evaluated using visual assessments. However, those methods are complicated and time-consuming. We developed an automatic system that can detect joint locations and compute the joint space difference index (JSDI), which was defined as the chronological change in JSN between two radiographs. The purpose of this study was to establish the validity of the software that automatically evaluates the temporal change of JSN. This study consisted of 39 patients with RA. All patients were treated with tocilizumab and underwent hand radiography (left and right hand separately) at 0, 6, and 12 months. The JSN was evaluated using mTSS (modified Total Sharp Score) by one musculoskeletal radiologist as well as our automatic system. Software measurement showed that JSDI between 0 and 12 months was significantly higher than that between 0 and 6 months (p < 0.01). While, there was no significant difference in mTSS between 0, 6, and 12 months. The group with higher disease activity at 0 months had significantly higher JSDI between 0 and 6 months than that with lower disease activity (p = 0.02). The automatic software can evaluate JSN progression of RA patients in the finger joint on X-ray.

Fully automatic quantitative software for assessment of minute finger joint space narrowing progression on radiographs: evaluation in rheumatoid arthritis patients with long-term sustained clinical low disease activity

PURPOSE

Rheumatoid arthritis (RA) causes joint space narrowing (JSN) as a form of joint destruction. We developed an automatic system that can detect joint locations and compute the joint space difference index (JSDI), which was defined as the chronological change in JSN between two radiographs. This study aims to evaluate the application of "machine vision" for radiographic image of the finger joints.

MATERIALS AND METHODS

Fifteen RA patients with long-term sustained clinical low disease activity were recruited. All patients underwent hand radiography and power Doppler ultrasonography (PDUS). The JSN was evaluated using the Genant-modified Sharp scoring (GSS) method and the automatic system. Synovial vascularity (SV) was assessed quantitatively using ultrasonography.

RESULTS

There were no significant differences in the JSDI between the joints with JSN and those without JSN on GSS (p = 0.052). The JSDI of the joints with SV was significantly higher than those without SV (p = 0.043). The JSDI of the no therapeutic response group was significantly higher than those of the response group (p < 0.001).

CONCLUSION

Our software can automatically evaluate temporal changes of JSN, which might free rheumatologists / radiologists from the burden of scoring hand radiography.

Use of artificial intelligence in imaging in rheumatology - current status and future perspectives

After decades of basic research with many setbacks, artificial intelligence (AI) has recently obtained significant breakthroughs, enabling computer programs to outperform human interpretation of medical images in very specific areas. After this shock wave that probably exceeds the impact of the first AI victory of defeating the world chess champion in 1997, some reflection may be appropriate on the consequences for clinical imaging in rheumatology. In this narrative review, a short explanation is given about the various AI techniques, including 'deep learning', and how these have been applied to rheumatological imaging, focussing on rheumatoid arthritis and systemic sclerosis as examples. By discussing the principle limitations of AI and deep learning, this review aims to give insight into possible future perspectives of AI applications in rheumatology.

Detection of Fine Radiographic Progression in Finger Joint Space Narrowing Beyond Human Eyes: Phantom Experiment and Clinical Study with Rheumatoid Arthritis Patients

The visual assessment of joint space narrowing (JSN) on radiographs of rheumatoid arthritis (RA) patients such as the Genant-modified Sharp score (GSS) is widely accepted but limited by its subjectivity and insufficient sensitivity. We developed a software application which can assess JSN quantitatively using a temporal subtraction technique for radiographs, in which the chronological change in JSN between two radiographs was defined as the joint space difference index (JSDI). The aim of this study is to prove the superiority of the software in terms of detecting fine radiographic progression in finger JSN over human observers. A micrometer measurement apparatus that can adjust arbitrary joint space width (JSW) in a phantom joint was developed to define true JSW. We compared the smallest detectable changes in JSW between the JSDI and visual assessment using phantom images. In a clinical study, 222 finger joints without interval score change on GSS in 15 RA patients were examined. We compared the JSDI between joints with and without synovial vascularity (SV) on power Doppler ultrasonography during the follow-up period. True JSW difference was correlated with JSDI for JSW differences ranging from 0.10 to 1.00 mm at increments of 0.10 mm (R² = 0.986 and P < 0.001). Rheumatologists were difficult to detect JSW difference of 0.30 mm or less. The JSDI of finger joints with SV was significantly higher than those without SV (P = 0.030). The software can detect fine differences in JSW that are visually unrecognizable.

Analysis of pulmonary sounds for the diagnosis of interstitial lung diseases secondary to rheumatoid arthritis

The diagnosis of interstitial lung diseases in patients affected by rheumatoid arthritis is fundamental to improving their survival rate. In particular, the average survival time of patients affected by rheumatoid arthritis with pulmonary implications is approximately 3 years. The gold standard for confirming the diagnosis of this disease is computer tomography. However, it is very difficult to raise diagnosis suspicion because the symptoms of the disease are extremely common in elderly people. The detection of the so-called velcro crackle in lung sounds can effectively raise the suspicion of an interstitial disease and speed up diagnosis. However, this task largely relies on the experience of physicians and has not yet been standardized in clinical practice. The diagnosis of interstitial lung diseases based on thorax auscultation still represents an underexplored field in the study of rheumatoid arthritis. In this study, we investigate the problem of the automatic detection of velcro crackle in lung sounds. In practice, the patient is auscultated using a digital stethoscope and the lung sounds are saved to a file. The acquired digital data are then analysed using a suitably developed algorithm. In particular, the proposed solution relies on the empirical observation that the audio bandwidth associated with velcro crackle is larger than that associated with healthy breath sounds. Experimental results from a database of 70 patients affected by rheumatoid arthritis demonstrate that the developed tool can outperform specialized physicians in terms of diagnosing pulmonary disorders. The overall accuracy of the proposed solution is 90.0%, with negative and positive predictive values of 95.0% and 83.3%, respectively, whereas the reliability of physician diagnosis is in the range of 60-70%. The devised algorithm represents an enabling technology for a novel approach to the diagnosis of interstitial lung diseases in patients affected by rheumatoid arthritis.

3D superimposition and understanding temporomandibular joint arthritis

OBJECTIVES

To investigate the 3D morphological variations in 169 temporomandibular ioint (TMJ) condyles, using novel imaging statistical modeling approaches.

SETTING AND SAMPLE POPULATION

The Department of Orthodontics and Pediatric Dentistry at the University of Michigan. Cone beam CT scans were acquired from 69 subjects with long-term TMJ osteoarthritis (OA, mean age 39.1±15.7 years), 15 subjects at initial consult diagnosis of OA (mean age 44.9±14.8 years), and seven healthy controls (mean age 43±12.4 years).

MATERIALS AND METHODS

3D surface models of the condyles were constructed, and homologous correspondent points on each model were established. The statistical framework included Direction-Projection-Permutation (DiProPerm) for testing statistical significance of the differences between healthy controls and the OA groups determined by clinical and radiographic diagnoses.

RESULTS

Condylar morphology in OA and healthy subjects varied widely with categorization from mild to severe bone degeneration or overgrowth. DiProPerm statistics supported a significant difference between the healthy control group and the initial diagnosis of OA group (t=6.6, empirical p-value=0.006) and between healthy and long-term diagnosis of OA group (t=7.2, empirical p-value=0). Compared with healthy controls, the average condyle in OA subjects was significantly smaller in all dimensions, except its anterior surface, even in subjects with initial diagnosis of OA.

CONCLUSION

This new statistical modeling of condylar morphology allows the development of more targeted classifications of this condition than previously possible.

Diagnostic index of three-dimensional osteoarthritic changes in temporomandibular joint condylar morphology

This study aimed to investigate imaging statistical approaches for classifying three-dimensional (3-D) osteoarthritic morphological variations among 169 temporomandibular joint (TMJ) condyles. Cone-beam computed tomography scans were acquired from 69 subjects with long-term TMJ osteoarthritis (OA), 15 subjects at initial diagnosis of OA, and 7 healthy controls. Three-dimensional surface models of the condyles were constructed and SPHARM-PDM established correspondent points on each model. Multivariate analysis of covariance and direction-projection-permutation (DiProPerm) were used for testing statistical significance of the differences between the groups determined by clinical and radiographic diagnoses. Unsupervised classification using hierarchical agglomerative clustering was then conducted. Compared with healthy controls, OA average condyle was significantly smaller in all dimensions except its anterior surface. Significant flattening of the lateral pole was noticed at initial diagnosis. We observed areas of 3.88-mm bone resorption at the superior surface and 3.10-mm bone apposition at the anterior aspect of the long-term OA average model. DiProPerm supported a significant difference between the healthy control and OA group ([Formula: see text]). Clinically meaningful unsupervised classification of TMJ condylar morphology determined a preliminary diagnostic index of 3-D osteoarthritic changes, which may be the first step towards a more targeted diagnosis of this condition.

Insights into rheumatoid arthritis from use of MRI

Magnetic resonance imaging (MRI) is ideal for imaging the joints of rheumatoid arthritis (RA) patients. It produces anatomically detailed images of bone, cartilage, tendons and synovial membrane. It can reveal structural damage, in the form of bone erosion, cartilage thinning and/or tendon rupture, and regions of inflammation, using sequences that reveal water content and vascularity. MRI synovitis, tenosynovitis and bone oedema/osteitis all have prognostic significance, and MRI studies of RA have helped elucidate the mechanisms whereby bone and synovial inflammation lead to joint damage. Bone oedema/osteitis has become an important imaging biomarker, and can be used to help predict progression from undifferentiated arthritis to definite RA. Recent MRI studies have confirmed that subclinical inflammation is often present in patients in clinical remission, and these data may affect disease management. Finally, recent clinical trials are reviewed, in which MRI outcome measures are being established as sensitive response markers.

Global localization of 3D anatomical structures by pre-filtered Hough forests and discrete optimization

The accurate localization of anatomical landmarks is a challenging task, often solved by domain specific approaches. We propose a method for the automatic localization of landmarks in complex, repetitive anatomical structures. The key idea is to combine three steps: (1) a classifier for pre-filtering anatomical landmark positions that (2) are refined through a Hough regression model, together with (3) a parts-based model of the global landmark topology to select the final landmark positions. During training landmarks are annotated in a set of example volumes. A classifier learns local landmark appearance, and Hough regressors are trained to aggregate neighborhood information to a precise landmark coordinate position. A non-parametric geometric model encodes the spatial relationships between the landmarks and derives a topology which connects mutually predictive landmarks. During the global search we classify all voxels in the query volume, and perform regression-based agglomeration of landmark probabilities to highly accurate and specific candidate points at potential landmark locations. We encode the candidates' weights together with the conformity of the connecting edges to the learnt geometric model in a Markov Random Field (MRF). By solving the corresponding discrete optimization problem, the most probable location for each model landmark is found in the query volume. We show that this approach is able to consistently localize the model landmarks despite the complex and repetitive character of the anatomical structures on three challenging data sets (hand radiographs, hand CTs, and whole body CTs), with a median localization error of 0.80 mm, 1.19 mm and 2.71 mm, respectively.

Regional 3D superimposition to assess temporomandibular joint condylar morphology

OBJECTIVES

To investigate the reliability of regional three-dimensional registration and superimposition methods for assessment of temporomandibular joint condylar morphology across subjects and longitudinally.

METHODS

The sample consisted of cone beam CT scans of 36 patients. The across-subject comparisons included 12 controls, mean age 41.3 ± 12.0 years, and 12 patients with temporomandibular joint osteoarthritis, mean age 41.3 ± 14.7 years. The individual longitudinal assessments included 12 patients with temporomandibular joint osteoarthritis, mean age 37.8 ± 16.7 years, followed up at pre-operative jaw surgery, immediately after and one-year post-operative. Surface models of all condyles were constructed from the cone beam CT scans. Two previously calibrated observers independently performed all registration methods. A landmark-based approach was used for the registration of across-subject condylar models, and temporomandibular joint osteoarthritis vs control group differences were computed with shape analysis. A voxel-based approach was used for registration of longitudinal scans calculated x, y, z degrees of freedom for translation and rotation. Two-way random intraclass correlation coefficients tested the interobserver reliability.

RESULTS

Statistically significant differences between the control group and the osteoarthritis group were consistently located on the lateral and medial poles for both observers. The interobserver differences were ≤0.2 mm. For individual longitudinal comparisons, the mean interobserver differences were ≤0.6 mm in translation errors and 1.2° in rotation errors, with excellent reliability (intraclass correlation coefficient >0.75).

CONCLUSIONS

Condylar registration for across-subjects and longitudinal assessments is reliable and can be used to quantify subtle bony differences in the three-dimensional condylar morphology.

A new ROC analysis method considering the correlation between neighboring pixels

In this paper, we introduce a novel receiver operating characteristic (ROC) analysis method that considers spatial correlation between pixels to evaluate classification algorithms. ROC analysis is one of the most important tools in the evaluation of medical images and computer aided diagnosis (CAD) systems. It provides a comprehensive description of the detection accuracy of the test image. To evaluate the localization performance, operating points of ROC curves are obtained based on the classification results of individual pixels. To this date, the confidence level or intensity value of each pixel is assumed to be independent within the image. However, this assumption is not satisfied in real problems. In this paper, a new ROC analysis algorithm that considers the correlation between neighboring pixels is proposed. Our results show that the new ROC curves provide a more accurate evaluation of the test image.

Reliability and longitudinal validity of computer-assisted methods for measuring joint damage progression in subjects with rheumatoid arthritis

OBJECTIVE

To compare the metric properties of a computer-assisted erosion segmentation volume measurement with scoring using the Rheumatoid Arthritis Magnetic Resonance Imaging Score (RAMRIS) in a longitudinal cohort of patients with rheumatoid arthritis (RA).

METHODS

Thirty-two sets of baseline and 2-year followup magnetic resonance imaging (MRI) of metacarpal phalangeal 2-5 joints of patients with RA were scored using RAMRIS and segmented using OSIRIS software. The smallest detectable difference (SDD), standardized response mean (SRM), and paired t-test were used to evaluate the sensitivity to change. Eleven of the 32 patients' MRI were segmented by both readers to evaluate interreader agreement. The 28-joint Disease Activity Score (DAS28) and Sharp erosion scores further evaluated construct and longitudinal validity.

RESULTS

Reliability of erosion progression by computer-assisted volume measurement was superior to RAMRIS [intrareader interclass correlation coefficient (ICC) 0.97 (0.94-0.99) vs 0.52 (0.22-0.73)] and interreader ICC of volume measurement was 0.85 (0.53-0.96). Computer-assisted volume measurements identified 10 of 32 patients who progressed more than the SDD progression, whereas RAMRIS identified only 4 of 32 patients (p = 0.0013). By a paired t-test, however, all MRI measures progressed significantly over 2 years (irrespective of treatment arm) and there was little difference by SRM. Construct correlational validity of the MRI methods was 0.47-0.90 for status scores and 0.33-0.81 for progression. There was no relationship between the average DAS28 and erosion progression by any imaging method.

CONCLUSION

Computer-assisted measurement of erosion volume has good performance metrics. It had excellent intrareader and interreader reliability and was more sensitive to change than RAMRIS in this group of patients. www.ClinicalTrials.gov, NCT00451971.

Automatic radiographic quantification of hand osteoarthritis; accuracy and sensitivity to change in joint space width in a phantom and cadaver study

OBJECTIVE

To validate a newly developed quantification method that automatically detects and quantifies the joint space width (JSW) in hand radiographs. Repeatability, accuracy and sensitivity to changes in JSW were determined. The influence of joint location and joint shape on the measurements was tested.

METHODS

A mechanical micrometer set-up was developed to define and adjust the true JSW in an acrylic phantom joint and in human cadaver-derived phalangeal joints. Radiographic measurements of the JSW were compared to the true JSW. Repeatability, systematic error (accuracy) and sensitivity (defined as the smallest detectable difference (SDD)) were determined. The influence of joint position on the JSW measurement was assessed by varying the location of the acrylic phantom on the X-ray detector with respect to the X-ray beam and the influence of joint shape was determined by using morphologically different human cadaver joints.

RESULTS

The mean systematic error was 0.052 mm in the phantom joint and 0.210 mm in the cadaver experiment. In the phantom experiments, the repeatability was high (SDD = 0.028 mm), but differed slightly between joint locations (p = 0.046), and a change in JSW of 0.037 mm could be detected. Dependent of the joint shape in the cadaver hand, a change in JSW between 0.018 and 0.047 mm could be detected.

CONCLUSIONS

The automatic quantification method is sensitive to small changes in JSW. Considering the published data of JSW decline in the normal and osteoarthritic population, the first signs of OA progression with this method can be detected within 1 or 2 years.

Cyst-like lesions in finger joints detected by conventional radiography: comparison with 320-row multidetector computed tomography

OBJECTIVE

Many rheumatologists and radiologists routinely assess conventional radiographs of the hands, and it is often unclear how to proceed if radiography reveals only cyst-like lesions (CLLs), with otherwise normal findings. The present study was undertaken to evaluate the use of 320-row multidetector computed tomography (MDCT) of the hands in the further assessment of CLLs of metacarpophalangeal (MCP) and proximal interphalangeal (PIP) joints identified on conventional radiography.

METHODS

MCP and PIP joints (n = 1,120 joints) of 56 consecutive patients (44 women [mean age 55 years, range 31-72 years] and 12 men [mean age 57 years, range 37-77 years]) were prospectively scored for the presence of cysts, CLLs, and erosions of the PIP and MCP joints, first on conventional radiographs, then on MDCT. Scoring was performed by 2 independent readers under blinded conditions. Intraclass correlation coefficients were calculated.

RESULTS

By conventional hand radiography, 13 patients (total of 260 joints assessed) were identified as having CLLs in 1 or more joints (total of 36 joints [11 PIP and 25 MCP]). By MDCT, the findings in 19 of 36 joints (53%) were diagnosed as erosions, while 7 of 36 (19%) were confirmed as true cysts, and 10 joints (28%) were normal (false positive). Among the patients with CLLs, 10 of 224 joints with no abnormality seen radiographically had erosions as seen on MDCT. Interreader agreement for erosions was 0.854 (95% confidence interval [95% CI] 0.831-0.874) by conventional hand radiography and 0.952 (95% CI 0.943-0.959) by MDCT.

CONCLUSION

Our results indicate that radiographic appearance of cyst-like lesions may actually represent erosions and should lead to initiation of further imaging tests.

Clinical application of SPHARM-PDM to quantify temporomandibular joint osteoarthritis

The severe bone destruction and resorption that can occur in osteoarthritis of the temporomandibular joint (TMJ) is associated with significant pain and limited joint mobility. However, there is no validated method for the quantification of discrete changes in joint morphology in early diagnosis or assessment of disease progression or treatment effects. To achieve this, the objective of this cross-sectional study was to use simulated bone resorption on cone-beam CT (CBCT) to study condylar morphological variation in subjects with temporomandibular joint (TMJ) osteoarthritis (OA). The first part of this study assessed the hypothesis that the agreement between the simulated defects and the shape analysis measurements made of these defects would be within 0.5mm (the image's spatial resolution). One hundred seventy-nine discrete bony defects measuring 3mm and 6mm were simulated on the surfaces of 3D models derived from CBCT images of asymptomatic patients using ITK-Snap software. SPHARM shape correspondence was used to localize and quantify morphological differences of each resorption model with the original asymptomatic control. The size of each simulated defect was analyzed and the values obtained compared to the true defect size. The statistical analysis revealed very high probabilities that mean shape correspondence measured defects within 0.5mm of the true defect size. 95% confidence intervals (CI) were (2.67, 2.92) and (5.99, 6.36) and 95% prediction intervals (PI) were (2.22, 3.37) and (5.54, 6.82), respectively for 3mm and 6mm simulated defects. The second part of this study applied shape correspondence methods to a longitudinal sample of TMJ OA patients. The mapped longitudinal stages of TMJ OA progression identified morphological variants or subtypes, which may explain the heterogeneity of the clinical presentation. This study validated shape correspondence as a method to precisely and predictably quantify 3D condylar resorption.

Quantification of condylar resorption in temporomandibular joint osteoarthritis

OBJECTIVE

This study was performed to determine the condylar morphologic variation of osteoarthritic (OA) and asymptomatic temporomandibular joints (TMJs) and to determine its correlation with pain intensity and duration.

STUDY DESIGN

Three-dimensional surface models of mandibular condyles were constructed from cone-beam computerized tomography images of 29 female patients with TMJ OA (Research Diagnostic Criteria for Temporomandibular Disorders group III) and 36 female asymptomatic subjects. Shape correspondence was used to localize and quantify the condylar morphology. Statistical analysis was performed with multivariate analysis of covariance analysis, using Hotelling T(2) metric based on covariance matrices, and Pearson correlation.

RESULTS

The OA condylar morphology was statistically significantly different from the asymptomatic condyles (P < .05). Three-dimensional morphologic variation of the OA condyles was significantly correlated with pain intensity and duration.

CONCLUSION

Three-dimensional quantification of condylar morphology revealed profound differences between OA and asymptomatic condyles, and the extent of the resorptive changes paralleled pain severity and duration.

Computational radiology in skeletal radiography

Recent years have brought rapid developments in computational image analysis in musculo-skeletal radiology. Meanwhile the algorithms have reached a maturity that makes initial clinical use feasible. Applications range from joint space measurement to erosion quantification, and from fracture detection to the assessment of alignment angles. Current results of computational image analysis in radiography are very promising, but some fundamental issues remain to be clarified, among which the definition of the optimal trade off between automatization and operator-dependency, the integration of these tools into clinical work flow and last not least the proof of incremental clinical benefit of these methods.

Assessement of rheumatic diseases with computational radiology: current status and future potential

In recent years, several computational image analysis methods to assess disease progression in rheumatic diseases were presented. This review article explains the basics of these methods as well as their potential application in rheumatic disease monitoring, it covers radiography, sonography as well as magnetic resonance imaging in quantitative analysis frameworks.