A computational method to differentiate rheumatoid arthritis patients using thermography data

Abstract

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.

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

“…Pauk et al used temperature, demographic, and clinical information as input features to the ANN model 28 . They obtained an accuracy of 92.8% in detecting the RA from the healthy subjects.…”

“…Several studies on automated detection of hand thermograms were based on ML algorithms 26 – 28 Majority of the studies have used temperature values as the input to the ML classifier. Limited literature used convolution neural network (CNN) models to classify control subjects and RA patients in the hand thermal images.…”

Dorsal Finger Fold Recognition by Convolutional Neural Networks for the Detection and Monitoring of Joint Swelling in Patients with Rheumatoid Arthritis

“…In rheumatology, digital biomarkers most commonly include information obtained from wearables or smartphones, such as data from gyroscopes, accelerometers, tab speed, or geolocation [3]. Thermography (where temperature is recorded by a camera), in combination with artificial neural networks, has recently been used to differentiate between RA patients and healthy individuals, but so far has not been shown to monitor disease activity sufficiently [4]. The core problem of biomarkers for RA is the insufficient use of real-world data and the low specificity of patient-reported outcomes, respectively.…”