A comparison of thermographic characteristics of the hands and wrists of rheumatoid arthritis patients and healthy controls

External Validation of the Machine Learning-Based Thermographic Indices for Rheumatoid Arthritis: A Prospective Longitudinal Study

External validation is crucial in developing reliable machine learning models. This study aimed to validate three novel indices-Thermographic Joint Inflammation Score (ThermoJIS), Thermographic Disease Activity Index (ThermoDAI), and Thermographic Disease Activity Index-C-reactive protein (ThermoDAI-CRP)-based on hand thermography and machine learning to assess joint inflammation and disease activity in rheumatoid arthritis (RA) patients. A 12-week prospective observational study was conducted with 77 RA patients recruited from rheumatology departments of three hospitals. During routine care visits, indices were obtained at baseline and week 12 visits using a pre-trained machine learning model. The performance of these indices was assessed cross-sectionally and longitudinally using correlation coefficients, the area under the receiver operating curve (AUROC), sensitivity, specificity, and positive and negative predictive values. ThermoDAI and ThermoDAI-CRP correlated with CDAI, SDAI, and DAS28-CRP cross-sectionally (ρ = 0.81; ρ = 0.83; ρ = 0.78) and longitudinally (ρ = 0.55; ρ = 0.61; ρ = 0.60), all p < 0.001. ThermoDAI and ThermoDAI-CRP also outperformed Patient Global Assessment (PGA) and PGA + C-reactive protein (CRP) in detecting changes in 28-swollen joint counts (SJC28). ThermoJIS had an AUROC of 0.67 (95% CI, 0.58 to 0.76) for detecting patients with swollen joints and effectively identified patients transitioning from SJC28 > 1 at baseline visit to SJC28 ≤ 1 at week 12 visit. These results support the effectiveness of ThermoJIS in assessing joint inflammation, as well as ThermoDAI and ThermoDAI-CRP in evaluating disease activity in RA patients.

Subclinical joint inflammation in rheumatoid arthritis: comparing thermal and ultrasound imaging at the metacarpophalangeal joint

BACKGROUND

While ultrasound and MRI are both superior to clinical examination in the detection of joint inflammation, there is presently a lack of data whether thermography may be similarly useful in the assessment of joint inflammation in patients with RA. Our study aims to evaluate the use of thermography in detecting subclinical joint inflammation at clinically quiescent (non-tender and non-swollen) metacarpophalangeal joints (MCPJs) in patients with rheumatoid arthritis (RA). The outcomes from thermography in our study will be compared with ultrasonography (which is a more established imaging tool used for joint inflammation assessment in RA).

METHODS

The minimum (Tmin), average (Tavg) and maximum (Tmax) temperatures at the 10 MCPJs of each patient were summed to obtain the Total Tmin, Total Tavg and Total Tmax, respectively. Ultrasound grey-scale (GS) and power Doppler (PD) joint inflammation (scored semi-quantitatively, 0-3) at the 10 MCPJs were summed up to derive the respective TGS and TPD scores per patient. Pearson's correlation and simple linear regression were respectively used to assess correlation and characterize relationships between thermographic parameters (Total Tmin, Total Tavg and Total Tmax) and ultrasound imaging parameters (TGS, TPD and the number of joint(s) with PD ≥ 1 or GS ≥ 2).

RESULTS

In this cross-sectional study, 420 clinically non-swollen and non-tender MCPJs from 42 RA patients were examined. All thermographic parameters (Total Tmin, Total Tavg and Total Tmax) correlated significantly (P-values ranging from 0.001 to 0.0012) with TGS score (correlation coefficient ranging from 0.421 to 0.430), TPD score (correlation coefficient ranging from 0.383 to 0.424), and the number of joint(s) with PD ≥ 1 or GS ≥ 2 (correlation coefficient ranging from 0.447 to 0.465). Similarly, simple linear regression demonstrated a statistically significant relationship (P-values ranging from 0.001 to 0.005) between all thermographic parameters (Total Tmin, Total Tavg and Total Tmax) and ultrasound imaging parameters (TPD and TGS).

CONCLUSION

For the first time, thermographic temperatures were shown to correlate with ultrasound-detected joint inflammation at clinically quiescent MCPJs. The use of thermography in the detection of subclinical joint inflammation in RA appears promising and warrants further investigation.

Thermography at the Elbow Among Patients with Rheumatoid Arthritis: A Comparison with Ultrasound-Detected Joint Inflammation Findings

INTRODUCTION

There is a lack of data on the use of thermography for elbow joint inflammation assessment among patients with rheumatoid arthritis (RA). Hence, we aimed to compare thermography with ultrasonography (a more established imaging modality for joint inflammation assessment) in the assessment of inflammation in the elbows of patients with RA.

METHODS

Standardised minimum (Tmin), maximum (Tmax) and average (Tavg) temperatures at each elbow (medial, lateral, posterior and anterior aspects) were summed to obtain the thermographic parameters MIN, MAX and AVG, respectively. Ultrasound parameters of elbow joint inflammation included total greyscale (TGS) and total power Doppler (TPD) scores. Pearson's correlation coefficient was utilized for correlation analysis between parameters. The relationship between parameters was characterized using simple linear regression.

RESULTS

Sixty elbows were evaluated from 30 patients with RA in this cross-sectional study. Thermographic parameters (MIN, MAX and AVG) showed significant correlation (P < 0.05) with (1) TPD scores at both elbows (correlation coefficient ranging 0.40 to 0.55) and (2) TGS scores at the right elbow (correlation coefficient ranging 0.39 to 0.42). A statistically significant relationship (P values ranging from 0.002 to 0.033) between parameters was demonstrable as follows: (1) MIN, MAX and AVG versus TPD scores (bilateral elbows) and (2) MIN, MAX and AVG versus TGS scores (right elbow).

CONCLUSION

Thermographic temperatures have been demonstrated to correlate with ultrasound-detected joint inflammation at the elbow in patients with RA. The association is more consistently observed with ultrasound PD joint inflammation than its GS counterpart.

RANet: a custom CNN model and quanvolutional neural network for the automated detection of rheumatoid arthritis in hand thermal images

Rheumatoid arthritis is an autoimmune disease which affects the small joints. Early prediction of RA is necessary for the treatment and management of the disease. The current work presents a deep learning and quantum computing-based automated diagnostic approach for RA in hand thermal imaging. The study's goals are (i) to develop a custom RANet model and compare its performance with the pretrained models and quanvolutional neural network (QNN) to distinguish between the healthy subjects and RA patients, (ii) To validate the performance of the custom model using feature selection method and classification using machine learning (ML) classifiers. The present study developed a custom RANet model and employed pre-trained models such as ResNet101V2, InceptionResNetV2, and DenseNet201 to classify the RA patients and normal subjects. The deep features extracted from the RA Net model are fed into the ML classifiers after the feature selection process. The RANet model, RA Net+ SVM, and QNN model produced an accuracy of 95%, 97% and 93.33% respectively in the classification of healthy groups and RA patients. The developed RANet and QNN models based on thermal imaging could be employed as an accurate automated diagnostic tool to differentiate between the RA and control groups.

Validating within-limb calibrated algorithm using a smartphone attached infrared thermal camera for detection of arthritis in children

OBJECTIVES

To determine the impact of physical activity on temperature after within-limb calibration (TAWiC) measures and their reproducibility. To determine if thermal imaging from a smartphone attached thermal camera is comparable to thermal imaging using a handheld thermal camera for detection of arthritis in children.

METHODS

Children without symptoms were enrolled to the "asymptomatic exercise cohort", and received infrared imaging, using a standard handheld camera, after initial resting period, after activity, and after second resting period. Children seen in the rheumatology clinic with knee pain were enrolled into the "symptomatic knee pain cohort" and received imaging with both the smartphone-attached and handheld cameras before a routine clinical exam. TAWiC was defined as the temperature differences between joint and ipsilateral mid-tibia as the main readout for arthritis detection.

RESULTS

The asymptomatic exercise cohort demonstrated notable changes in absolute and TAWiC temperatures collected by thermal imaging after physical activity, and temperatures did not consistently return to pre-activity levels after a second period of rest. The 95th TAWiC from anterior view were, resting one -0.1 C (0.5), activity -0.7 C (0.5), resting two -0.2 C (0.6) (resting 1 vs resting 2, p-value = 0.13). In the symptomatic knee pain cohort, the smartphone attached and handheld thermal cameras performed similarly in regards to detection of joint inflammation and evaluation of joint temperature using the TAWiC algorithm, with high sensitivity of 80% (55.2-100.0%) and specificity of 84.2% (76.0-92.4%) in the anterior knee view when compared with the gold standard joint exam performed by a pediatric rheumatologist. The mean 95th TAWiC temperature difference between the two cameras was -0.1 C (-0.1 to 0.0) (p = 0.0004).

CONCLUSIONS

This study showed continued validity of the TAWiC algorithm across two distinct thermal camera platforms and demonstrates promise for improved accessibility and utility of this technology for arthritis detection.

Automated segmentation and classification of hand thermal images in rheumatoid arthritis using machine learning algorithms: A comparison with quantum machine learning technique

The aims and objectives of the study were to i) perform image segmentation using a color-based k-means clustering algorithm and feature extraction using binary robust invariant scalable key points (BRISK), maximum stable extremal regions (MSER), features from accelerated segment test (FAST), Harris, and orientated FAST and rotated BRIEF (ORB); ii) compare the performance of classical machine learning techniques such as LogitBoost, Bagging, and SVM with a quantum machine learning technique. For the proposed study, 240 hand thermal images were acquired in the dorsal view and ventral view of both the right and left-hand regions of RA and normal subjects. The hot spot regions from the thermograms were segmented using a color-based k-means clustering technique. The features from the segmented hot spot region were extracted using different feature extraction methods. Finally, normal and RA groups were categorized using LogitBoost, Bagging, and support vector machine (SVM) classifiers. The proposed study used two testing methods, such as 10-fold cross-validation and a percentage split of 80-20%. The LogitBoost classifier outperformed with an accuracy of 93.75% using the 10-fold cross-validation technique compared to other classifiers. Also, the quantum support vector machine (QSVM) obtained a prediction accuracy of 92.7%. Furthermore, the QSVM model reduces the computational cost and training time of the model to classify the RA and normal subjects. Thus, thermograms with classical machine learning and quantum machine learning algorithms could be considered a feasible technique for classifying normal and RA groups.

Receiver operating characteristic analysis using a novel combined thermal and ultrasound imaging for assessment of disease activity in rheumatoid arthritis

We aim to determine whether combined thermal and ultrasound (CTUS) imaging can identify rheumatoid arthritis (RA) patients with at least moderate disease activity (DAS28 > 3.2). Temperature differences of maximum (T_max), average (T_avg) and minimum (T_min) temperatures from a control temperature at 22 joints (bilateral hands) were summed up to derive the respective MAX, AVG and MIN per patient. MAX (PD), AVG (PD) and MIN (PD) are CTUS results derived by multiplying MAX, AVG and MIN by a factor of 2 when a patient's total ultrasound power Doppler (PD) joint inflammation score > median score, which otherwise remained unchanged. Receiver operating characteristic (ROC) analysis was used to determine whether CTUS imaging can identify patients with DAS28 > 3.2. In this cross-sectional study, 814 joints were imaged among 37 RA patients (mean disease duration, 31 months). CTUS (but not single modality) imaging parameters were all significantly greater comparing patients with DAS28 > 3.2 versus those with DAS28 ≤ 3.2 (all P < 0.01). Area under the ROC curves (AUCs) using cut-off levels of ≥ 94.5, ≥ 64.6 and ≥ 42.3 in identifying patients with DAS28 > 3.2 were 0.73 , 0.76 and 0.76 for MAX (PD), AVG (PD) and MIN (PD), respectively (with sensitivity ranging from 58 to 61% and specificity all 100%). The use of CTUS in detecting a greater severity of joint inflammation among patients with at least moderate disease activity (DAS28 > 3.2) appears promising and will require further validation in independent RA cohorts.

An update on thermal imaging in rheumatoid arthritis

This review aims to summarise the recent literature concerning the usage of thermal imaging in the study of rheumatoid arthritis (RA). Most RA studies have applied thermal imaging as a static process alone although thermal imaging has been conducted with an additional dynamic/functional component. Algorithms to automate the analysis of thermal imaging in RA have also been described. Several RA thermal imaging studies have demonstrated differences in thermographic findings between RA patients and healthy controls and/or compared thermographic parameters with other clinical/functional/imaging parameters; while fewer studies have assessed the role of thermal imaging in discriminating disease severity in RA. Thermal imaging is a relatively low cost, non-invasive imaging technique offering an objective measurement of joint surface temperature in RA joint inflammation assessment. Although there has been an increasing literature build up on the use of thermography in RA, more validation work is still necessary to delineate the potential role(s) of its use among patients with RA. This timely review focusses on the recent literature concerning thermal imaging, and provides clinicians with an update on its recent development in RA.

The Value of Thermal Imaging for Knee Arthritis: A Single-Center Observational Study

PURPOSE

To compare (non-contact) thermal imaging with power Doppler (PD) for the evaluation of knee arthritis with joint effusion.

MATERIALS AND METHODS

We enrolled patients with knee arthritis who were scheduled to undergo an arthrocentesis of the knee from April to December 2020 at a single tertiary hospital. A thermography camera, FLIR ONE Pro, was used to obtain both thermographic and digital images on subjects. For each subject, thermography, ultrasonography, arthrocentesis, and blood tests were conducted at the same study visit. Thermal imaging findings and clinical characteristics were compared by dividing the subjects into PD-positive and PD-negative groups on ultrasound. The receiver operating characteristic (ROC) curve analysis was used to determine the accuracy of PD positivity.

RESULTS

A total of 30 knee arthritis patients were enrolled in this study. Knee temperature was significantly higher in PD-positive group compared to PD-negative group [maximum temperature (T max): 33.2℃ vs. 30.5℃, p=0.025; minimum temperature (T min): 30.7℃ vs. 27.0℃, p=0.015; average temperature (T ave): 32.1℃ vs. 29.1℃, p=0.016]. Also, the joint fluid white blood cell count was considerably higher in PD-positive group than in PD-negative group (24556 cells/mm³ vs. 7840 cells/mm³, p=0.010). The area under the ROC curve of the point measurement of T max, T min, and T ave ranged between 0.764 and 0.790.

CONCLUSION

In this study, we found that high thermographic temperatures of the knee suggest a positive PD signal. Thus, thermography might be used as an adjuvant tool of PD for non-invasive evaluation of knee arthritis.

Characterization of Baseline Temperature Characteristics Using Thermography in The Clinical Setting

BACKGROUND

Thermography is a diagnostic method based on the ability to record infrared radiation emitted by the skin and is unique in its ability to accurately show physiological and/or pathological cutaneous temperature changes in a non-invasive way. This method can be used to indirectly assess changes or impairments in cutaneous perfusion. Significant technological advancements have allowed thermography to be more commonly utilized by clinicians, yet a basic consensus of patient characteristics that may affect temperature recordings is not established.

MATERIALS AND METHODS

We evaluated cutaneous temperature in a cohort of outpatients to understand what factors, including tobacco use and other high-risk characteristics, contribute to cutaneous tissue perfusion as measured by thermography. Participants were prospectively enrolled if they were a combustible cigarette smoker, an electronic cigarette (e-cigarette) user, or a never smoker. Standardized thermographic images of the subject's facial profiles, forearms, and calves were taken and demographic characteristics, medical comorbidities, and tobacco product use were assessed. These variables were statistically tested for associations with temperature at each anatomic site.

RESULTS

We found that gender had a significant effect on thermographic temperature that differed by anatomic site, and we found a lack of significant difference in thermographic temperature by race. Our regression analysis did not support significant differences in thermographic temperatures across smoking groups, while there was a trend for decreased perfusion in smokers relative to non-smokers and e-cigarette users relative to non-smokers.

CONCLUSION

Thermographic imaging is a useful tool for clinical and research use with consideration of sex and other perfusion-affecting characteristics.

A computational method to differentiate rheumatoid arthritis patients using thermography data

BACKGROUND

The traditional rheumatoid arthritis (RA) diagnosis is very complicated because it uses many clinical and image data. Therefore, there is a need to develop a new method for diagnosing RA using a consolidated set of blood analysis and thermography data.

OBJECTIVE

The following issues related to RA are discussed: 1) Which clinical data are significant in the primary diagnosis of RA? 2) What parameters from thermograms should be used to differentiate patients with RA from the healthy? 3) Can artificial neural networks (ANN) differentiate patients with RA from the healthy?

METHODS

The dataset was composed of clinical and thermal data from 65 randomly selected patients with RA and 104 healthy subjects. Firstly, the univariate logistic regression model was proposed in order to find significant predictors. Next, the feedforward neural network model was used. The dataset was divided into the training set (75% of data) and the test set (25% of data). The Broyden-Fletcher-Goldfarb-Shanno (BFGS) and non-linear logistic function to transformation nodes in the output layer were used for training. Finally, the 10 fold Cross-Validation was used to assess the predictive performance of the ANN model and to judge how it performs.

RESULT

The training set consisted of the temperature of all fingers, patient age, BMI, erythrocyte sedimentation rate, C-reactive protein and White Blood Cells (10 parameters in total). High level of sensitivity and specificity was obtained at 81.25% and 100%, respectively. The accuracy was 92.86%.

CONCLUSIONS

This methodology suggests that the thermography data can be considered in addition to the currently available tools for screening, diagnosis, monitoring of disease progression.

Role of thermography in the assessment of temporomandibular disorders and other musculoskeletal conditions: A systematic review

The aim of this review was to evaluate whether thermal imaging may constitute a reliable assessment method of musculoskeletal and temporomandibular disorders (TMD/MSD). A systematic review was conducted in the Pubmed, Scopus, Cochrane library, Web of Science, and Lilacs databases. The search terms were "musculoskeletal disorders,""temporomandibular disorders,""infrared thermography,""thermography," and "infrared imaging." The inclusion criteria were: studies published between January 1985 and January 2021, performed in humans, with sample size equal or greater than 20 patients, written in English, Portuguese, French and/or Spanish, and full text available. The exclusion criteria were: systematic reviews, case studies, and/or studies focused on pathologies beyond the review's domain. The risk of bias was evaluated using CASP 2018. A total of 2032 articles were retrieved. Of these, 25 studies met the inclusion criteria and were included to withdraw the following information: title, type of study, first author and year of publication, objective, number of participants, comparisons, and principal conclusions. No RCT were found. Despite some disparity, points of convergence among the majority of authors could be found. In general, healthy individuals show subtle thermal differences between contralateral homolog areas. Concerning orofacial structures, unilateral symptomatic individuals may show thermal differences equal or greater than 0.4°C. Infrared thermography accuracy in diagnosing TMD/MSD is still considered low to moderate. Despite some limitations, IRT might constitute a valuable supporting diagnostic tool in the medical field of TMD and MSD.

Assessment of blood distribution in response to post-surgical steal syndrome: A novel technique based on Thermo-Anatomical Segmentation

The distal ischemic steal syndrome (ISS) is a complication following the construction of an arteriovenous (A-V) access for hemodialysis. The ability to non-invasively monitor changes in skin microcirculation improves both the diagnosis and treatment of vascular diseases. In this study, we propose a novel technique for evaluating the palms' blood distribution following arteriovenous access, based on thermal imaging. Furthermore, we utilize the thermal images to identify typical recovery patterns of patients that underwent this surgery and show that thermal images taken post-surgery reflect the patient's follow-up status. Thermal photographs were taken by a portable thermal camera from both hands before and after the A-V access surgery, and one month following the surgery, from ten dialysis patients. A novel term "Thermo-Anatomical Segmentation", which enables a functional assessment of palm blood distribution was defined. Based on this segmentation it was shown that the greatest change after surgery was in the most distal region, the fingertips (p < 0.05). In addition, the changes in palm blood distribution in both hands were synchronized, which indicates a bilateral effect. An unsupervised machine learning model revealed two variables that determine the recovery pattern following the surgery: the palms' temperature difference pre- and post-surgery and the post-surgery difference between the treated and untreated hand. Our proposed framework provides a new technique for quantitative assessment of the palm's blood distribution. This technique may improve the clinical treatment of patients with vascular disease, particularly the patient-specific follow-up, in clinics as well as in homecare.

Accuracy of infrared thermography for perforator mapping: A systematic review and meta-analysis of diagnostic studies

INTRODUCTION

Infrared thermography allows the detection of infrared radiation which can be reliably associated with skin temperature. Modern portable thermography devices have been used to identify the location of skin perforators by detecting subtle differences in skin temperature. The aim of this study is to conduct a diagnostic accuracy systematic review to determine the specificity and sensitivity of infrared thermography.

MATERIALS AND METHODS

A PRISMA-compliant systematic review and meta-analysis was conducted, scrutinising PUBMED and EMBASE databases for diagnostic studies measuring the accuracy of infrared thermography for perforator identification. Article screening, review and data gathering was conducted in parallel by two independent authors. Eligible studies were subject to a formal risk of bias was assessment using the QUADAS2 instrument.

RESULTS

A total of 254 entries were obtained, of which 7 satisfied our pre-established inclusion criteria. These studies reported a total of 435 perforators in 133 individuals. The most commonly investigated locations were the antero-lateral thigh and abdominal wall. Reported sensitivity values ranged from 73.7% to 100%. A meta-analysis demonstrated a cumulative sensitivity of 95%. Specificity was not routinely reported. All studies presented a moderate to high risk of bias according to QUADAS2.

DISCUSSION

Affordable infrared thermography devices are an interesting alternative to traditional preoperative investigations for perforator mapping. They are sensitive enough to reliably identify a large proportion of perforators as "hot-spots". However, there is limited evidence to estimate the specificity of this technology, as studies have failed to report true negative values associated with "cold-spots".

Thermal characteristics of rheumatoid feet in remission: Baseline data

Objectives

Studies have shown conflicting characteristic thermographic patterns of the feet in patients with active rheumatoid arthritis (RA). However, to date no studies have compared thermographic patterns of patients with RA in remission and healthy controls. Thus this study aimed to investigate whether the thermal characteristics of the feet of RA patients, in clinical and radiological remission differ to those of healthy controls.

Methods

Using convenience sampling, RA patients were recruited upon confirmed absence of synovitis by clinical examination and musculoskeletal ultrasound. Thermal images of the feet were taken. Each foot was subdivided into medial, central, lateral, forefoot and heel regions. Subsequently, temperatures in the different regions were analyzed and compared to a cohort of healthy adults.

Results

Data from 32 RA patients were compared to that of 51 healthy controls. The Independent samples T-Test demonstrated a significant difference in temperatures in all the regions of the forefoot between RA participants versus healthy subjects (Table 1). Using the One-Way ANOVA test, no significant difference was found between all the forefoot regions (p = 0.189) of RA patients. Independent sample T-test found significant differences in all heel regions between the two groups (Table 2). One-Way ANOVA demonstrated no significant differences (p = 0.983) between the different foot regions (n = 192) of RA patients.

Conclusion

These findings suggest that RA patients in clinical and radiological remission exhibit significantly different feet thermographic patterns compared to healthy controls. This data will provide the basis for future studies to assess whether thermographic patterns change with disease activity.

A novel use of combined thermal and ultrasound imaging in detecting joint inflammation in rheumatoid arthritis

PURPOSE

To evaluate the use of combined thermal and ultrasound imaging to assess joint inflammation in rheumatoid arthritis (RA).

METHOD

22-joint (bilateral hands) thermography and ultrasonography were performed. For each patient, the MAX, MIN and AVG represent the sum of the temperature differences with a control temperature, for the respective maximum (Tmax), minimum (Tmin) and average (Tavg) temperatures at the joints. MAX (PD), MIN (PD) and AVG (PD) represent the results of combined thermal imaging with a patient's total ultrasound power Doppler (PD) joint inflammation score (Total PD) (when Total PD > median score, MAX, MIN and AVG was multiplied by a factor of 2, otherwise MAX (PD), MIN (PD) and AVG (PD) remained the same as the MAX, MIN and AVG). Pearson correlation and linear regression were used to assess correlation and characterize relationships of imaging parameters with the 28-joint disease activity score (DAS28).

RESULTS

In this cross-sectional study, 814 joints were examined in 37 adult RA patients (75.7 % female, 75.7 % Chinese; mean DAS28, 4.43). Among the imaging parameters, only MAX (PD) and AVG (PD) correlated significantly with DAS28 (correlation coefficient (95 % CI): MAX (PD), 0.393 (0.079, 0.636), P = 0.016; AVG (PD): 0.376 (0.060, 0.624), P = 0.022). Similarly, only MAX (PD) and AVG (PD) demonstrated a statistically significant relationship with DAS28 (regression coefficient (95 % CI): MAX (PD), 0.009 (0.002, 0.015), P = 0.016; AVG (PD), 0.011 (0.002, 0.020), P = 0.022).

CONCLUSIONS

Novel use of combined thermal and ultrasound imaging in RA shows superiority to either imaging alone in terms of correlation with DAS28.