AI model grades knee osteoarthritis severity on limited devices

Researchers have developed a novel approach for grading knee osteoarthritis severity using a combination of deep learning and a large language model. The system utilizes a ResNet-18 convolutional neural network, optimized and converted to TensorFlow Lite for deployment on devices with limited computational resources. This on-device model achieved a test accuracy of 94.48% and can function without continuous internet connectivity. An auxiliary LLM, Gemini-2.0-flash, provides interpretive findings such as potential symptoms and preventive measures, enhancing the tool's utility as an accessible decision-support system. AI

影响 Enables on-device AI diagnostics for musculoskeletal disorders, improving accessibility in resource-constrained environments.

排序理由 Academic paper detailing a new AI-driven diagnostic approach for knee osteoarthritis. [lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

• Gemini-2.0-flash
• Kellgren-Lawrence grades
• Knee Osteoarthritis
• ResNet-18
• TensorFlow Lite

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