The usefulness of computer-aided joint space analysis in the assessment of rheumatoid arthritis

Evaluating high-resolution computed tomography derived 3-D joint space metrics of the metacarpophalangeal joints between rheumatoid arthritis and age- and sex-matched control participants

Introduction

Rheumatoid arthritis (RA) is commonly characterized by joint space narrowing. High-resolution peripheral quantitative computed tomography (HR-pQCT) provides unparalleled in vivo visualization and quantification of joint space in extremity joints commonly affected by RA, such as the 2nd and 3rd metacarpophalangeal joints. However, age, sex, and obesity can also influence joint space narrowing. Thus, this study aimed to determine whether HR-pQCT joint space metrics could distinguish between RA patients and controls, and determine the effects of age, sex and body mass index (BMI) on these joint space metrics.

Methods

HR-pQCT joint space metrics (volume, width, standard deviation of width, maximum/minimum width, and asymmetry) were acquired from RA patients and age-and sex-matched healthy control participants 2nd and 3rd MCP joints. Joint health and functionality were assessed with ultrasound (i.e., effusion and inflammation), hand function tests, and questionnaires.

Results

HR-pQCT-derived 3D joint space metrics were not significantly different between RA and control groups (p > 0.05), despite significant differences in inflammation and joint function (p < 0.05). Joint space volume, mean joint space width (JSW), maximum JSW, minimum JSW were larger in males than females (p < 0.05), while maximum JSW decreased with age. No significant association between joint space metrics and BMI were found.

Conclusion

HR-pQCT did not detect group level differences between RA and age-and sex-matched controls. Further research is necessary to determine whether this is due to a true lack of group level differences due to well-controlled RA, or the inability of HR-pQCT to detect a difference.

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.

The promise and limitations of artificial intelligence in musculoskeletal imaging

With the recent developments in deep learning and the rapid growth of convolutional neural networks, artificial intelligence has shown promise as a tool that can transform several aspects of the musculoskeletal imaging cycle. Its applications can involve both interpretive and non-interpretive tasks such as the ordering of imaging, scheduling, protocoling, image acquisition, report generation and communication of findings. However, artificial intelligence tools still face a number of challenges that can hinder effective implementation into clinical practice. The purpose of this review is to explore both the successes and limitations of artificial intelligence applications throughout the muscuskeletal imaging cycle and to highlight how these applications can help enhance the service radiologists deliver to their patients, resulting in increased efficiency as well as improved patient and provider satisfaction.

Anti-arthritic effect of chicken embryo tissue hydrolyzate against adjuvant arthritis in rats (X-ray microtomographic and histopathological analysis)

Finding new, safe strategies to prevent and control rheumatoid arthritis is an urgent task. Bioactive peptides and peptide-rich protein hydrolyzate represent a new trend in the development of functional foods and nutraceuticals. The resulting tissue hydrolyzate of the chicken embryo (CETH) has been evaluated for acute toxicity and tested against chronic arthritis induced by Freund's full adjuvant (modified Mycobacterium butyricum) in rats. The antiarthritic effect of CETH was studied on the 28th day of the experiment after 2 weeks of oral administration of CETH at doses of 60 and 120 mg/kg body weight. Arthritis was evaluated on the last day of the experiment on the injected animal paw using X-ray computerized microtomography and histopathology analysis methods. The CETH effect was compared with the non-steroidal anti-inflammatory drug diclofenac sodium (5 mg/kg). Oral administration of CETH was accompanied by effective dose-dependent correction of morphological changes caused by the adjuvant injection. CETH had relatively high recovery effects in terms of parameters for reducing inflammation, inhibition of osteolysis, reduction in the inflammatory reaction of periarticular tissues, and cartilage degeneration. This study presents for the first time that CETH may be a powerful potential nutraceutical agent or bioactive component in the treatment of rheumatoid arthritis.

Machine Learning in Rheumatic Diseases

With advances in information technology, the demand for using data science to enhance healthcare and disease management is rapidly increasing. Among these technologies, machine learning (ML) has become ubiquitous and indispensable for solving complex problems in many scientific fields, including medical science. ML allows the development of guidelines and framing of the evaluation system for complex diseases based on massive data. In the analysis of rheumatic diseases, which are chronic and remarkably heterogeneous, ML can be anticipated to be extremely helpful in deciphering and revealing the inherent interrelationships in disease development and progression, which can further enhance the overall understanding of the disease, optimize patients' stratification, calibrate therapeutic strategies, and predict prognosis and outcomes. In this review, the basics of ML, its potential clinical applications in rheumatology, together with its strengths and limitations are summarized.

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.

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.

Radiographic remission in rheumatoid arthritis quantified by computer-aided joint space analysis (CASJA): a post hoc analysis of the RAPID 1 trial

Background

The reduction of finger joint space width (JSW) in patients with rheumatoid arthritis (RA) is strongly associated with joint destruction. Treatment with certolizumab pegol (CZP), a PEGylated anti-TNF, has been proven to be effective in RA patients. The computer-aided joint space analysis (CAJSA) provides the semiautomated measurement of joint space width at the metacarpal-phalangeal joints (MCP) based on hand radiographs. The aim of this post hoc analysis of the RAPID 1 trial was to quantify MCP joint space distance (JSD-MCP) measured by CAJSA between baseline and week 52 in RA patients treated with certolizumab pegol (CZP) plus methotrexate (MTX) compared with MTX/placebo.

Methods

Three hundred twenty-eight patients were included in the post hoc analysis and received placebo plus MTX, CZP 200 mg plus MTX and CZP 400 mg plus MTX. All patients underwent X-rays of the hand at baseline and week 52 as well as assessment of finger joint space narrowing of the MCP using CAJSA (Version 1.3.6; Sectra; Sweden). The joint space width (JSW) was expressed as mean joint space distance of the MCP joints I to V (JSD-MCP_total).

Results

The MTX group showed a significant reduction of joint space of − 4.8% (JSD-MCP_total), whereas in patients treated with CZP 200 mg/MTX and CZP 400 mg/MTX a non-significant change (JSD-MCP_total + 0.6%) was observed. Over 52 weeks, participants with DAS28 remission (DAS28 ≤ 2.6) exhibited a significant joint space increase of + 3.3% (CZP 200 mg plus MTX) and + 3.9% (CZP pegol 400 mg plus MTX).

Conclusion

CZP plus MTX did not reduce JSD-MCP_total estimated by CAJSA compared with MTX/placebo. Furthermore, clinical remission (DAS28 ≤ 2.6) in patients treated with CZP plus MTX was associated with an increasing JSD, indicating radiographic remission in RA.

Quantitative Imaging in Inflammatory Arthritis: Between Tradition and Innovation

Radiologic imaging is crucial for diagnosing and monitoring rheumatic inflammatory diseases. Particularly the emerging approach of precision medicine has increased the interest in quantitative imaging. Extensive research has shown that ultrasound allows a quantification of direct signs such as bone erosions and synovial thickness. Dual-energy X-ray absorptiometry and high-resolution peripheral quantitative computed tomography (CT) contribute to the quantitative assessment of secondary signs such as osteoporosis or lean mass loss. Magnetic resonance imaging (MRI), using different techniques and sequences, permits in-depth evaluations. For instance, the perfusion of the inflamed synovium can be quantified by dynamic contrast-enhanced imaging or diffusion-weighted imaging, and cartilage injury can be assessed by mapping (T1ρ, T2). Furthermore, the increased metabolic activity characterizing the inflammatory response can be reliably assessed by hybrid imaging (positron emission tomography [PET]/CT, PET/MRI). Finally, advances in intelligent systems are pushing forward quantitative imaging. Complex mathematical algorithms of lesions' segmentation and advanced pattern recognition are showing promising results.

Applications of Artificial Intelligence in Musculoskeletal Imaging: From the Request to the Report

Artificial intelligence (AI) will transform every step in the imaging value chain, including interpretive and noninterpretive components. Radiologists should familiarize themselves with AI developments to become leaders in their clinical implementation. This article explores the impact of AI through the entire imaging cycle of musculoskeletal radiology, from the placement of the requisition to the generation of the report, with an added Canadian perspective. Noninterpretive tasks which may be assisted by AI include the ordering of appropriate imaging tests, automatic exam protocoling, optimized scheduling, shorter magnetic resonance imaging acquisition time, computed tomography imaging with reduced artifact and radiation dose, and new methods of generation and utilization of radiology reports. Applications of AI for image interpretation consist of the determination of bone age, body composition measurements, screening for osteoporosis, identification of fractures, evaluation of segmental spine pathology, detection and temporal monitoring of osseous metastases, diagnosis of primary bone and soft tissue tumors, and grading of osteoarthritis.

Computer-Based Radiographic Quantification of Joint Space Narrowing Progression Using Sequential Hand Radiographs: Validation Study in Rheumatoid Arthritis Patients from Multiple Institutions

We have developed a refined computer-based method to detect joint space narrowing (JSN) progression with the joint space narrowing progression index (JSNPI) by superimposing sequential hand radiographs. The purpose of this study is to assess the validity of a computer-based method using images obtained from multiple institutions in rheumatoid arthritis (RA) patients. Sequential hand radiographs of 42 patients (37 females and 5 males) with RA from two institutions were analyzed by a computer-based method and visual scoring systems as a standard of reference. The JSNPI above the smallest detectable difference (SDD) defined JSN progression on the joint level. The sensitivity and specificity of the computer-based method for JSN progression was calculated using the SDD and a receiver operating characteristic (ROC) curve. Out of 314 metacarpophalangeal joints, 34 joints progressed based on the SDD, while 11 joints widened. Twenty-one joints progressed in the computer-based method, 11 joints in the scoring systems, and 13 joints in both methods. Based on the SDD, we found lower sensitivity and higher specificity with 54.2 and 92.8%, respectively. At the most discriminant cutoff point according to the ROC curve, the sensitivity and specificity was 70.8 and 81.7%, respectively. The proposed computer-based method provides quantitative measurement of JSN progression using sequential hand radiographs and may be a useful tool in follow-up assessment of joint damage in RA patients.

Fully automated joint space width measurement and digital X-ray radiogrammetry in early RA

Objectives

To study fully automated digital joint space width (JSW) and bone mineral density (BMD) in relation to a conventional radiographic scoring method in early rheumatoid arthritis (eRA).

Methods

Radiographs scored by the modified Sharp van der Heijde score (SHS) in patients with eRA were acquired from the SWEdish FarmacOTherapy study. Fully automated JSW measurements of bilateral metacarpals 2, 3 and 4 were compared with the joint space narrowing (JSN) score in SHS. Multilevel mixed model statistics were applied to calculate the significance of the association between ΔJSW and ΔBMD over 1 year, and the JSW differences between damaged and undamaged joints as evaluated by the JSN.

Results

Based on 576 joints of 96 patients with eRA, a significant reduction from baseline to 1 year was observed in the JSW from 1.69 (±0.19) mm to 1.66 (±0.19) mm (p<0.01), and BMD from 0.583 (±0.068) g/cm² to 0.566 (±0.074) g/cm² (p<0.01). A significant positive association was observed between ΔJSW and ΔBMD over 1 year (p<0.0001). On an individual joint level, JSWs of undamaged (JSN=0) joints were wider than damaged (JSN>0) joints: 1.68 mm (95% CI 1.70 to 1.67) vs 1.54 mm (95% CI 1.63 to 1.46). Similarly the unadjusted multilevel model showed significant differences in JSW between undamaged (1.68 mm (95% CI 1.72 to 1.64)) and damaged joints (1.63 mm (95% CI 1.68 to 1.58)) (p=0.0048). This difference remained significant in the adjusted model: 1.66 mm (95% CI 1.70 to 1.61) vs 1.62 mm (95% CI 1.68 to 1.56) (p=0.042).

Conclusions

To measure the JSW with this fully automated digital tool may be useful as a quick and observer-independent application for evaluating cartilage damage in eRA.

Trial registration number

NCT00764725.

Computer-assisted Joint Space Area Measurement: A New Technique in Patients With Knee Osteoarthritis

Objectives

This study aims to assess the validity and reproducibility of computer-assisted joint space area measurement in knee roentgenograms of patients with knee osteoarthritis and compare it with a qualitative method in knee roentgenograms and quantitative and semi-quantitative methods in magnetic resonance imaging.

Patients and methods

The study included 40 knees of 40 patients diagnosed as osteoarthritis (14 males, 26 females; mean age 57.4±5.9 years; range 47 to 67 years). Only the patients who wrote consents for publication of their radiologic data, and with knee roentgenograms and magnetic resonance images of the same knees were selected. Computer-assisted measurements were applied to joint spaces by two blinded physicians, for two times with an interval of one week. Data were evaluated for intraobserver and interobserver consistency. Also, data were compared with qualitative (Kellgren-Lawrence classification), quantitative (joint space width, cartilage thickness, meniscal thickness in magnetic resonance images) and semi-quantitative methods (whole-organ magnetic resonance imaging score).

Results

Intraobserver consistency was evaluated for each physician, which revealed no differences. Interobserver consistency was evaluated by comparing the measurements of two blinded physicians and no differences were found (p>0.05). There was no significant correlation between the grade of Kellgren-Lawrence classification and other variables; such as grade of meniscus, meniscal thickness, cartilage thickness and computer- assisted joint space area measurements (p>0.05). While there was a positive correlation between computer-assisted joint space area measurement and other quantitative measurements, there was a negative correlation between computer-assisted joint space area measurement and whole-organ magnetic resonance imaging scores.

Conclusion

When compared with qualitative, quantitative, and semi-quantitative methods, computer-assisted joint space area measurement seems to be a useful, reproducible, and cost-effective quantitative method for evaluating knee osteoarthritis.

Validation of automatic joint space width measurements in hand radiographs in rheumatoid arthritis

Computerized methods promise quick, objective, and sensitive tools to quantify progression of radiological damage in rheumatoid arthritis (RA). Measurement of joint space width (JSW) in finger and wrist joints with these systems performed comparable to the Sharp-van der Heijde score (SHS). A next step toward clinical use, validation of precision and accuracy in hand joints with minimal damage, is described with a close scrutiny of sources of error. A recently developed system to measure metacarpophalangeal (MCP) and proximal interphalangeal (PIP) joints was validated in consecutive hand images of RA patients. To assess the impact of image acquisition, measurements on radiographs from a multicenter trial and from a recent prospective cohort in a single hospital were compared. Precision of the system was tested by comparing the joint space in mm in pairs of subsequent images with a short interval without progression of SHS. In case of incorrect measurements, the source of error was analyzed with a review by human experts. Accuracy was assessed by comparison with reported measurements with other systems. In the two series of radiographs, the system could automatically locate and measure 1003/1088 (92.2%) and 1143/1200 (95.3%) individual joints, respectively. In joints with a normal SHS, the average (SD) size of MCP joints was [Formula: see text] and [Formula: see text] in the two series of radiographs, and of PIP joints [Formula: see text] and [Formula: see text]. The difference in JSW between two serial radiographs with an interval of 6 to 12 months and unchanged SHS was [Formula: see text], indicating very good precision. Errors occurred more often in radiographs from the multicenter cohort than in a more recent series from a single hospital. Detailed analysis of the 55/1125 (4.9%) measurements that had a discrepant paired measurement revealed that variation in the process of image acquisition (exposure in 15% and repositioning in 57%) was a more frequent source of error than incorrect delineation by the software (25%). Various steps in the validation of an automated measurement system for JSW of MCP and PIP joints are described. The use of serial radiographs from different sources, with a short interval and limited damage, is helpful to detect sources of error. Image acquisition, in particular repositioning, is a dominant source of error.

A reliability study using computer-based analysis of finger joint space narrowing in rheumatoid arthritis patients

The joint space difference index (JSDI) is a newly developed radiographic index which can quantitatively assess joint space narrowing progression of rheumatoid arthritis (RA) patients by using an image subtraction method on a computer. The aim of this study was to investigate the reliability of this method by non-experts utilizing RA image evaluation. Four non-experts assessed JSDI for radiographic images of 510 metacarpophalangeal joints from 51 RA patients twice with an interval of more than 2 weeks. Two rheumatologists and one radiologist as well as the four non-experts examined the joints by using the Sharp-van der Heijde Scoring (SHS) method. The radiologist and four non-experts repeated the scoring with an interval of more than 2 weeks. We calculated intra-/inter-observer reliability using the intra-class correlation coefficients (ICC) for JSDI and SHS scoring, respectively. The intra-/inter-observer reliabilities for the computer-based method were almost perfect (inter-observer ICC, 0.966-0.983; intra-observer ICC, 0.954-0.996). Contrary to this, intra-/inter-observer reliability for SHS by experts was moderate to almost perfect (inter-observer ICC, 0.556-0.849; intra-observer ICC, 0.589-0.839). The results suggest that our computer-based method has high reliability to detect finger joint space narrowing progression in RA patients.

Radiographic quantifications of joint space narrowing progression by computer-based approach using temporal subtraction in rheumatoid wrist

OBJECTIVE

To investigate the validity of a computer-based method using temporal subtraction in carpal joints of patients with rheumatoid arthritis (RA), which can detect the difference in joint space between two images with the joint space difference index (JSDI).

METHODS

The study consisted of 43 patients with RA (39 females and 4 males) who underwent radiography at baseline and at 1-year follow-up. The joint space narrowing (JSN) of carpal joints on bilateral hand radiographs was assessed by our computer-based method, using the Sharp/van der Heijde method as the standard of reference. We compared the JSDI of joints with JSN progression in the follow-up period with that of those without JSN progression. In addition, we examined whether there is a significant difference in JSDI in terms of laterality or topology of the joint.

RESULTS

The JSDI of joints with JSN progression was significantly higher than that of those without JSN progression (Mann-Whitney U test, p < 0.001). There was no statistically significant difference in the JSDI between the left and right carpal joints, which was analysed for five different joints altogether and each joint separately (Mann-Whitney U test, p > 0.05). There was statistically significant difference in JSDI among different joints (Kruskal-Wallis test, p = 0.003).

CONCLUSION

These results suggest that our computer-based method may be useful to recognize the JSN progression on radiographs of rheumatoid wrists.

ADVANCES IN KNOWLEDGE

The computer-based temporal subtraction method can detect the JSN progression in the wrist, which is the single most commonly involved site in RA.

Performance of computer-based analysis using temporal subtraction to assess joint space narrowing progression in rheumatoid patients

Our computer-based method can detect the chronological change in joint space width between baseline and follow-up images as the joint space difference index (JSDI). The aim of this study was to verify the sensitivity and specificity of our computer-based method in assessment of joint space narrowing progression in rheumatoid patients. Twenty-seven patients (24 women and 3 men) with rheumatoid arthritis underwent radiography of the bilateral hand at baseline and at 1 year. The joint space narrowing (JSN) of a total of 252 metacarpophalangeal (MCP) joints and 229 carpal joints was assessed by our computer-based method, setting the Sharp/van der Heijde method as the gold standard. We constructed a receiver operating characteristic curve by using the Sharp/van der Heijde method as the gold standard and set the optimal cutoff on JSDI for MCP, carpal, and MCP/carpal joints. We then calculated the sensitivity and specificity for each cutoff in assessment of JSN progression. At the most discriminant cutoff, the sensitivity and specificity of the computer-based method for MCP joints was 78.6 versus 85.3 %, respectively (AUC = 0.837; P < 0.001). Carpal joints revealed a lower sensitivity and specificity with 64.7 and 86.8 % (AUC = 0.775; P < 0.001). Furthermore, the sensitivity and specificity for MCP/carpal joints was 71.0 versus 83.6 %, respectively (AUC = 0.778; P < 0.001). The computer-based method presented a reliable assessment of JSN progression with high sensitivity and specificity and may be useful in follow-up assessment of the joint damage in rheumatoid patients.