Background: Knee osteoarthritis (KOA) affects 37% of individuals aged ≥ 60 years in the national health survey, causing pain, discomfort, and reduced functional independence. Methods: This study aims to automate the assessment of KOA severity by training deep learning models using the Kellgren–Lawrence grading system (class 0~4). A total of 15,000 images were used, with 3000 images collected for each grade. The learning models utilized were DenseNet201, ResNet101, and EfficientNetV2, and their performance in lesion classification was evaluated and compared. Statistical metrics, including accuracy, precision, recall, and F1-score, were employed to assess the feasibility of applying deep learning models for KOA classification. Results: Among these four metrics, DenseNet201 achieved the highest performance, while the ResNet101 model recorded the lowest. DenseNet201 demonstrated the best performance with an overall accuracy of 73%. The model’s accuracy by K-L grade was 80.7% for K-L Grade 0, 53.7% for K-L Grade 1, 72.7% for K-L Grade 2, 75.3% for K-L Grade 3, and 82.7% for K-L Grade 4. The model achieved a precision of 73.2%, a recall of 73%, and an F1-score of 72.7%. Conclusions: These results highlight the potential of deep learning models for assisting specialists in diagnosing the severity of KOA by automatically assigning K-L grades to patient data.

Modern robotic systems are evolving toward conducting missions based on semantic knowledge. Such systems require environmental modeling as essential for successful mission execution. However, there is an inefficiency in that manual modeling is required whenever a new environment is given, and adaptive modeling that can adapt to the environment is needed. In this paper, we propose an integrated framework that enables autonomous environmental modeling for service robots by fusing domain knowledge with open-vocabulary-based Vision-Language Models (VLMs). When a robot is deployed in a new environment, it builds occupancy maps through autonomous exploration and extracts semantic information about objects and places. Furthermore, we introduce human–robot collaborative modeling beyond robot-only environmental modeling. The collected semantic information is stored in a structured database and utilized on demand. To verify the applicability of the proposed framework to service robots, experiments are conducted in a simulated home environment and a real-world indoor corridor. Through the experiments, the proposed framework achieved over 80% accuracy in semantic information extraction in both environments. Semantic information about various types of objects and places was extracted and stored in the database, demonstrating the effectiveness of DK-SMF for service robots.