## 4 Discussion  
In this literature review, 60 research articles were selected based on the above criteria. As shown in Figure 3, 15 of these articles were published in 2022, while the number of selected articles published in 2024 was five, as the survey was closed on 15 May 2024. The research articles reviewed dealt with the classification of knee osteoarthritis according to KL grade, with a total of 56 articles out of 60, while only 4 articles focused on monitoring the disease progression. In response to the first research question (RQ1), the main methodologies developed in the reviewed papers are based on the transfer of supervised learning techniques using a variety of traditional machine learning algorithms and deep learning models. The result is the optimal performance obtained by the most efficient model or a combination of a set of models. In the proposed approaches, ResNet models were used in 24 articles, while DenseNet and Visual Geometry Group (VGG) models were applied in 14 and 13 articles, respectively. In terms of conventional machine learning approaches, Logistic Regression (LR) and Random Forest (RF) are the main methods used in the literature. Furthermore, we find that 50 articles used Deep Learning models (83%), and only 10 articles preferred traditional machine learning algorithms (17%). In addition, we find that Grad-CAM is the most widely used visualization method in the literature, followed by the saliency map (Figure 3). The developed methods are dominated by supervised transfer learning with 58 articles, compared to only two papers that used a semi-supervised learning technique [13; 45]. From Table 3, it can be concluded that the OAI dataset is the most used database, followed by the MOST dataset, since these two databases are the only tagged images available with a large number of images. Figure 3: Coverage of all articles included in the survey: (**a**) Number of research articles by year of publication; (**b**) Breakdown of articles by methods adopted; (**c**) Distribution of visualization methods employed. With regard to the second question (RQ2), as shown in Table 6, the traditional machine learning model developed in [43] achieved the highest accuracy, with an average accuracy of 99.80% using an active contour algorithm for ROI segmentation and KNN for knee OA classification. However, among the methods developed without ROI detection, the most accurate method developed in [28] achieved a weighted kappa coefficient of 0.99 and a mean absolute error (MAE) of 0.0256. A brief analysis indicates that ROI segmentation has a minimal impact on the accuracy of the methods developed. It is evident that while ROI segmentation enhances the detection of JSN, it also conceals certain symptoms of knee osteoarthritis, such as osteophytes and sclerosis. This, in turn, diminishes the improvement in the accuracy of the model employed for feature extraction and classification. Conversely, the utilization of demographic variables or clinical data (gender, age, serum biomarkers, age, body mass index (BMI), inflammatory factors, gait analysis data, University of Western Ontario and McMaster Arthritis Index (WOMAC) total score) did not yield satisfactory results, with an accuracy rate of approximately 80% [7; 26; 27; 41]. However, the analysis of this study indicates that hyperparameters, feature extraction, and optimal weights are critical factors that have demonstrated the potential to enhance model accuracy. It is important to note that the validation of models developed in internal databases is consistently superior to that obtained in an external validation using entirely different image datasets. Furthermore, Table 3 reveals that studies comparing binary and multi-class classification generally achieved slightly higher accuracies with binary classification [24; 29; 30; 33]. It can be noted that researchers tend to employ a binary classification (OA, non-OA) or a multi-class classification of only four KL grades instead of five, combining the first two grades, KL0 and KL1.