Diagnosis and Management of Hand Arthritis Using a Mobile Medical Application Fartash Vasefi a* , Nicholas MacKinnon a , Timothy Horita a , Randy Hsu a , Daniel Herdman a , Kouhyar Tavakolian b , and Reza Fazel-Rezai b a eTreat Medical Diagnostics Inc., 1055 W Hastings St, Vancouver, BC, V6E 2E9 Canada b Biomedical Engineering Research Complex (BERC), Department of Electrical Engineering, University of North Dakota, Grand Forks, North Dakota, 58202 USA. *(Corresponding author: fvasefi@etreatmd.com) ABSTRACT A deployable mobile medical application is presented that employs a smartphone camera, patient input, internet connectivity, and cloud-based image processing techniques to document and analyze physiological characteristics of hands in osteoarthritis (OA) patients. The application performs digital image processing that spatially calibrates the image, locates hand fiduciary features, and quantifies hand features to identify abnormal distal and proximal interphalangeal joints. The algorithm determines the finger centerlines and joint coordinates. From these anatomical fiduciary points, it measures the width of fingers, location and size of joints, and finger joint angulation. The diagnostically relevant features measured by the mobile application can be applied to current diagnostic protocols such as the American College of Rheumatology (ACR) criteria for OA. Based on the results from a pilot study, the mobile application was modified to include interactive user guidance built into the smartphone. This app makes improvements on the algorithm that validate the image quality and makes the algorithm less dependent on precise capture conditions. Based on clinical feedback, a web-based portal and dashboard for advanced analysis was developed and presented. Clinicians, researchers, and patients can use this to explore relationships between pain, treatment, environmental parameters, and lifestyle factors. Keywords: osteoarthritis, hand joints, articular cartilage, image processing, computer-assisted validation studies 1. INTRODUCTION Many chronic diseases including muscular-skeletal disorders like arthritis and chronic back pain, skin conditions like acne, psoriasis, rosacea, and eczema can slip through the cracks of an overburdened health system, and the onus for managing the disease then falls to the patient. Ongoing discomfort and the desire for better treatment motivate these patients to seek an alternative to the conventional health care system. Mobile health applications, including telemedicine, may help reconnect these patients to the health care system in a cost-effective way. These apps can guide the patients in recording and analyzing the impact of environmental conditions, lifestyle and treatment on their condition so they and their clinicians to determine what will work best for them. In developed regions, mobile health (mHealth) can help patients to reduce fees, often charged against their medical insurance deductibles, as well as the time and expense of commuting to clinics. In emerging regions, mHealth can provide low-cost alternatives to, or guidance on, contacting a physician. Mobile is the most widespread communication infrastructure in the world. Much of the global population has access to some form of mobile communication, even in the most remote areas of Africa, Asia, and Latin America. This infrastructure offers societies the opportunity to transform their healthcare services. By applying modern algorithms that can calibrate and control smartphone cameras we can quantitatively measure size, shape and other characteristics of human anatomy. An example of a chronic disease condition which affects human anatomy and also has a symptomatic effect on patient is arthritis. Osteoarthritis (OA) is one of the most common joint disorders in the United States [1]. OA prevalence varies depending on which definition of OA is applied, which specific joints are being considered, and population demographics. After