Abstract
The diagnosis of knee osteoarthritis is challenging due to its complex nature and various contributing factors. With the advancement of artificial intelligence (AI) technology, some computer vision-based methods have been developed to address this task. However, when applied in practice, these methods encounter numerous challenges. Training a powerful AI model to effectively analyze a wide range of medical images is crucial. On the other hand, collecting and accurately labeling a significant number of medical images in the real world is necessary. Specifically, when dealing with knee images from specific regions like Vietnam, certain unique biological characteristics make it difficult to utilize and trust previously published studies. To effectively address these challenges, we introduce DIKOApp, an automatic diagnostic application for knee osteoarthritis based on the DIKO framework, trained on a dataset specifically built for the Vietnamese population. This framework is designed with two stages that leverage medical knowledge and computer vision techniques. The DIKO framework leverages efficient data sampling and augmentation framework to handle medical images in the real world more effectively. When evaluated using a real-world knee image dataset from Vietnamese individuals, the DIKO model demonstrates impressive performance with an accuracy of 89.34% and an F1-score of 0.88. By utilizing the capabilities of the DIKO framework, DIKOApp shows practical and promising real-world potential, enabling doctors and healthcare service providers to diagnose pathological conditions more accurately while requiring less diagnostic time, thereby improving the lives of patients.