The aging population is expected to create a significant burden on society and healthcare systems, particularly concerning chronic diseases. Knee osteoarthritis (OA) leads to pain and dysfunction and is a contributing factor to falls. The medical costs associated with prolonged medication use, joint replacement, and post-operative rehabilitation are substantial. Maintaining healthy knees is essential for quality aging and effective medical care.
Screening for knee OA aims to shift the focus from reactive treatment to proactive prevention, fostering a healthier population. The goal is to identify individuals at risk before symptoms appear, enabling preventive strategies. The targeted population is large, necessitating a simple, fast, and easy-to-use tool that is both valid and reliable. Digital screening, empowered by AI, can stratify older adults in the community who are at higher risk of knee pain or OA, facilitating cost-effective care.
Current digital screening tools include online risk calculators, wearable devices, and smartphone apps. Our team has developed a mobile app that is easy and convenient to operate in community settings, capable of identifying older adults at risk based on influencing factors. A mobile phone is used to capture walking performance along an 8-meter line and the ability to perform sit-to-stand movements in 30 seconds. Pose estimation is employed to identify joint positions, allowing for the computation of walking speed and the number of sit-to-stand movements in 30 seconds. The outcomes are compared with age- and gender-reference scores. The screening can be conducted by anyone, anywhere, at any age, in 10-15 minutes. The mobile app can screen older adults with and without knee pain and provide risk stratification for further management.
Preventing disease progression through precision management is another role that digital screening can fulfill. The use of digital screening in managing knee OA subtypes will be further discussed.
