# Rheumatoid Arthritis Diagnosis: Deep Learning vs. Humane
## 2 RA Diagnosis Methods: Modernism vs. Traditionalism
### Deep Learning Methods
During the first stage, the model creates the mask of the hand by executing segmentation in the original radiographic image. In the second stage, the model extracts five separate fingers from the previously created hand mask image. At the final stage, the model detects three points for each finger by the use of CNN architecture and a signal processing-based joint break detection. The CNN method that is used is based on the LeNet-5 model, while the joint break method is a method during which the finger image is scanned from the end of the tip to its base in order to find peak locations where the intensity of the finger image changes dramatically. This advanced method outperformed the AdaBoost model. Another two-staged model, which is proposed in [26], combines the use of the object detection method and convolution neural networks, which can predict the joint level narrowing and erosion SvH scores [13], as well as the overall RA damage, from patients' radiographs. At the first stage, the model performs object detection using the RetinaNet object recognition models. These models were trained in order to detect finger and wrist joints from radiograph images. During the second stage, the model with the use of CNNs with attention predicts the join-wise narrowing and erosion SvH scores and the overall RA damage applying the joints that were extracted from the previous stage. The attention mechanism that is used helps the model to focus on the salient regions of the radiograph images so that the damage predictions are more accurate. Furthermore, the visualization that is added on top of the radiograph images helps with the explainability of the model predictions. In [27], they use a deep learning model to predict the RA state of the patient at his next clinic visit. As the records of the patients are digitally stored and accessed by EHR (electronic health record) platforms such as university hospitals (UN) and public safety-net hospitals (SNH), it is relatively easy to create datasets with the corresponding data. These data can be used in order to train the deep learning model so that it can forecast the clinical disease activity index (CDAI) score of the patient at his/her next visit. The author selected the ESR and CRP level, the prior CDAI score, DMARDS, oral and injected glucocorticoids, autoantibodies, and finally, the demographic data of the patients as variables for predicting the disease state. Their research showed that only the previous CDAI score was not enough for predicting the next score, and a combination of variables such as laboratory values, medication and the history of disease activity should be used in order to obtain a correct prediction for the next visit. The outcome of their work shows that deep learning models can be used on EHR data for the accurate prediction of the RA disease condition. [PERSON] et al. [28] followed the Ratingen [13] score system for annotating the X-ray images that fed their model. They used only the left hand radiograph to extract only the joint regions. Thus, ten new images showing the ten joint regions were created from one X-ray image. These images were next rated by expert raters, and then they were used to train the model. The authors used a VGG16-based deep learning model and applied transfer learning. They used the pre-trained VGG16 model as the core, and they replaced some of the fully connected layers. After the replacement, they had to fine-tune the entire model, so that it could adapt to the new domain. However, they conclude that a whole new training of the model from scratch could be the best solution as the datasets are sufficient. Because of the six categories of the Ratingen score system and the non-equal distribution of the data in the datasets, besides the global accuracy of the model, the metrics of balance accuracy and the \(\pm 1\) balanced accuracy were introduced. The authors also experimented with a weighted cross-entropy because of the pronounced imbalances in the Ratingen scores in their data, which shows better results.