Awardee Organization(s): University of Georgia
Principal Investigator(s): Soheyla Amirian, PhD
Official Project Title: AI-Powered Web Application to Analyze Knee Joint Space for Aging Population
AITC Partner: PennAITech
Website(s): https://engineering.uga.edu/team_member/soheyla-amirian/
Osteoarthritis (OA) stands as a prevailing chronic joint affliction, affecting millions of individuals globally, particularly those aged 65 or older. We propose the development of an innovative AI-powered web application, tailored to facilitate the monitoring and analysis of knee joint space, and by extension, the progression of knee OA. This web application will serve as a beacon of hope for aging individuals suffering from the burden of knee OA. By building, training, and validating deep learning computer vision algorithm, we aim to empower patients, their caregivers, and healthcare providers with an intuitive and cost-effective solution. Our overarching goal is thus to provide a platform that allows for the quantitative and longitudinal assessment of knee joint space, thereby enhancing our understanding of the degeneration process in the aging knee. Beyond the technical intricacies, our mission is deeply rooted in delivering a solution that is accessible to those who need it the most. This technology will bridge geographical distances, transcending traditional healthcare limitations and opening new avenues for remote patient care. As we delve into the specific aims of this project, it is vital to underscore the potential impact it holds for individuals afflicted by knee OA, the individuals who care for them, and the healthcare professionals committed to their well-being. The specific aims are: (1) To develop an AI-powered web application to quantitatively assess and analyze knee joint space using only plain radiographs.Osteoarthritis (OA) stands as a prevailing chronic joint affliction, affecting millions of individuals globally, particularly those aged 65 or older. We propose the development of an innovative AI-powered web application, tailored to facilitate the monitoring and analysis of knee joint space, and by extension, the progression of knee OA. This web application will serve as a beacon of hope for aging individuals suffering from the burden of knee OA. By building, training, and validating deep learning computer vision algorithm, we aim to empower patients, their caregivers, and healthcare providers with an intuitive and cost-effective solution. Our overarching goal is thus to provide a platform that allows for the quantitative and longitudinal assessment of knee joint space, thereby enhancing our understanding of the degeneration process in the aging knee. Beyond the technical intricacies, our mission is deeply rooted in delivering a solution that is accessible to those who need it the most. This technology will bridge geographical distances, transcending traditional healthcare limitations and opening new avenues for remote patient care. As we delve into the specific aims of this project, it is vital to underscore the potential impact it holds for individuals afflicted by knee OA, the individuals who care for them, and the healthcare professionals committed to their well-being. The specific aims are: (1) To develop an AI-powered web application to quantitatively assess and analyze knee joint space using only plain radiographs. (2) To establish a prospective adult cohort with knee OA to clinically validate the AI-powered web application.
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