An Autonomous AI Framework for Knee Osteoarthritis Diagnosis via Semi-Supervised Learning and Dual Knowledge Distillation

In the diagnosis of knee osteoarthritis, imaging analysis relies on accurate classification models to assess the severity of the disease. Traditional methods often require large amounts of labeled data, which is challenging in many developing countries, especially in resource-limited areas where the scarcity of labeled data becomes a bottleneck due to a lack of medical resources and qualified annotators. Privacy concerns also arise when using high-quality datasets from developed countries. This paper proposes a semi-supervised dual-knowledge distillation framework, PADistillation, that leverages autonomous AI to expand the reach of telemedicine and remote diagnostics while addressing data scarcity and privacy problems. To overcome the challenge of insufficient labeled data, the framework uses attention-guided distillation, employing high-attention pixels and channels to guide the student model's learning, thereby enhancing classification performance with limited labeled data. To ensure patient privacy during training, a personalized pixel shuffling method is proposed, dynamically determining the privacy protection priority of different regions by measuring the visual disorder of image areas. Through autonomous optimization and real-time decision making, PADistillation operates efficiently in resourceconstrained environments and supports telemedicine and remote diagnostic needs. Even with limited labeled data, the experimental results show that PADistillation achieves an accuracy rate of 88.19%, a precision rate of 86.28%, and an F1 score of 86.94%. Compared with the mainstream semi-supervised methods, its accuracy rate is increased by more than 2%, the training efficiency is improved by 30%, and the privacy protection mechanism only leads to a performance loss of 1.2%.