SunSpot is a mobile application that will assist with skin cancer diagnosis. The intended customers of this application will consist of hospitals, dermatologists, other medical professionals, and medical research institutions interested in purchasing software that will assist in the diagnosis and tracking of skin cancer among their patients. The user audience of this mobile application will include a broad, diverse range of individuals of all ages, ethnicities, genders, and backgrounds. Moreover, the user audience will be individuals who are willing to use a mobile application to assist with skin cancer diagnosis.
In the report Cancer Facts and Figures 2021 published by the American Cancer Society, in the U.S. more than 9,500 people are diagnosed with skin cancer every day. The mortality rate is slightly above 2 deaths every hour. The Skin Cancer Foundation states, “when detected early, the 5 year survival rate of melanoma is 99%”. The American Cancer Society reports that, “Invasive melanoma accounts for about 1% of all skin cancer cases, but the vast majority of skin cancer deaths. In 2021, an estimated 106,110 new cases of invasive melanoma and 101,280 cases of in situ melanoma will be diagnosed in the US, while 7,180 people will die from the disease”.
The goal of SunSpot is to enable early detection of various types of skin cancer. The application will be used by a patient to collect photos of themselves on a monthly basis. These photos will be securely transferred to a server for analysis. The server will consume the photos in order to create a mapping of all moles and lesions that are found on the patient’s body. With continued usage of the application, historical analysis can be performed to identify if new skin spots are found, if any spots are growing in an abnormal manner, or if any spots are growing at an abnormal rate. The shape, size, and color of the spot will also be delivered into a machine learning algorithm that will eventually lead to the application’s ability to perform a diagnosis on its own. If any abnormalities are suspected, the patient will be notified to seek a medical professional for a diagnosis. Official diagnosis will be collected into the system allowing detection accuracy to increase over the application’s lifetime.