Knee osteoarthritis presents a significant health challenge for many adults globally. At present, there are no pharmacological treatments that can cure this medical condition. The primary method for managing the progress of knee osteoarthritis is through early identification. Currently, X-ray imaging serves as a key modality for predicting the onset of osteoarthritis. Nevertheless, the traditional manual interpretation of X-rays is susceptible to inaccuracies, largely due to the varying levels of expertise among radiologists. In this paper, we propose a multimodal model based on pre-trained vision and language models for the identification of the knee osteoarthritis severity Kellgren-Lawrence (KL) grading. Using Vision transformer and Pre-training of deep bidirectional transformers for language understanding (BERT) for images and texts embeddings extraction helps Transformer encoders extracts more distinctive hidden-states that facilitates the learning process of the neural network classifier. The multimodal model was trained and tested on the OAI dataset, and the results showed remarkable performance compared to the related works. Experimentally, the evaluation of the model on the test set comprising X-ray images demonstrated an overall accuracy of 82.85%, alongside a precision of 84.54% and a recall of 82.89%.

Language is fundamental to social interaction and communication, and assistive technologies can help reduce barriers between Deaf and hard-of-hearing individuals and hearing communities. This study presents a standalone edge-AI device based on Raspberry Pi 5 for detecting selected Arabic Sign Language (ArSL) gestures and displaying corresponding written text. The system integrates an autofocus camera, YOLO-based gesture detection, text buffering, and a 128 × 64 OLED display capable of presenting Arabic text. YOLOv5m, YOLOv8m, and YOLOv9m were compared as candidate detectors, and a custom dataset of 1881 images was developed for nine gesture classes and background examples. The dataset was partitioned by signer, with three signers used for training, one for validation, and one held out for testing. Across three YOLOv8m training runs, the mean mAP@0.5 was 0.9583. On Raspberry Pi 5, the completed pipeline achieved a measured throughput of 3.52 frames per second (FPS). Live-camera experiments assessed performance across distance, illumination, background, viewpoint, hand orientation, and signing speed, identifying conditions that most affected detection. The results demonstrate the feasibility of a self-contained, interactive edge system for detecting predefined ArSL gestures and displaying buffered text, providing a practical foundation for expanding the vocabulary and developing continuous-sign recognition.