# Rheumatoid Arthritis Diagnosis: Deep Learning vs. Humane
## 2 RA Diagnosis Methods: Modernism vs. Traditionalism  
### Deep Learning Methods  
In this review, we proceed with reference to the state-of-the-art methods that utilize algorithms from the field of deep learning in order to counter the RA disease. To the best of our knowledge, the 14 works that are reviewed in our work constitute the main efforts of the researchers in order to combine the deep learning method and medical data for diagnosing RA disease. The authors in [11] present a multi-task deep learning model that can learn to detect joints on X-ray images and concurrently diagnose two kinds of joint damage, narrowing damage and erosion damage. Moreover, they propose an alternation for label smoothing. Their way combines cues from classification and regression into a single loss. This way achieves a reduction of 5% regarding the relative error compared to other standard functions. In order to approach the standard metric SvH [13] for hand and foot images, they execute segmentation and classification tasks at the same time. For the training procedure, they utilize four images per patient, with all the scores for narrowing and erosion damage, annotated for each image. In order to make the procedure more robust, they use access to annotations of the center of all joints for better training signals. In [10], there is a proposal for a method based on deep learning that realizes at the same time recognition and classification of the RA using X-ray images. As it is difficult for a not experienced RA doctor to evaluate the state of the condition, as this diagnosis relies on implicit knowledge, the authors believe that there is a need for the implementation of a system that can evaluate the RA condition automatically. Furthermore, they developed their system in a way so it can be improved by evaluations and modification of the doctor who performs the diagnosis. Their system consists of four specific procedures. At first, there is the generation of the training data by the user. The user in this state detects the position of the joints and measures the size of the image and reads the SvH [13] score that is written on the X-ray image. Therefore, there is a five-dimensional (position x, y, width, height, score) data record that trains their model. At the next step, when there is an input of a new image, the trained model predicts the position of the joints and the damage score (DS) as well. This info is drawn on the image. Next, the medical doctor has the task of checking the result. At this stage, the outcome can be modified (score, position). Finally, this checked data can be fed into the training procedure of the model in order to make the system more robust. A deep learning model for calculating the radiographic finger joint SvH score in RA using X-ray images was developed in [21]. The model executes two tasks in two steps: the joint detection step and the joint evaluation step. Regarding the first step, a classifier was trained to detect the finger joints using Haar-like features. For the second step, meaning the joint space narrowing (JSN) score and the erosion score assignment for each detected joint, a CNN model was used. This model consisted of seven layers (two convolution layers, two pooling layers and three fully connected layers). Data augmentation (horizontal flipping, rotation) took place during both steps to increase the training data sets. In training sets, the images were manually clipped and scored for JSN and bone erosion by clinicians. The performance of the model was examined using a test dataset by comparing the score assigned by the clinicians and the score assigned by the model. In [15], they used grayscale ultrasound images as an input for the DesnseNet-121 model in order to automatically classify the conditions of RA. To standardize the use of the ultrasound, the OMERACT-EULER Synovitis scoring (OESS) [17] was used. The dataset of the ultrasound images was created from hospital data after the approval of the corresponding patients.