Skincare AI and COVID-19 Rashes
In the past decade, the world has witnessed a plethora of viral disease outbreaks. The most notable ones include the H1N1 pandemic, MERS, SARS, Ebola, and Zika virus outbreaks. Amid this COVID-19 pandemic, scientists and researchers have been pointed in that outbreaks can only be managed through efficient testing and tracing of the affected. The challenge this presents has shown itself to be getting the right tools in the hands of the right people at the right time.
Unfortunately, widespread testing becomes an unachievable feat, especially for novel infectious outbreaks. Nations are struggling to manage overwhelmed hospitals and treat patients whilst simultaneously slowing the spread of COVID-19. In regards to positive results, only full lockdowns and restriction of human contact in public places has been seen as effective in controlling outbreaks. However, even in the strictest quarantine situations, there have been outbreaks of COVID-19 in to the public where community transmission has occurred.
Diagnosis of the symptoms of the novel have been identified as being unique to COVID-19. Amongst flu symptoms, cough, and fever typically found is the development of widespread skin rashes. Confirmed by the American Academy of Dermatology and The European Academy of Dermatology and Venerology, respectively, approximately 20 percent of the individuals develop rashes before any other symptoms like shortness of breath, fever, or cough appear.
There is a dire need for an automated image-based screening system that will benefit the user to detect the newly found COVID-19 symptom of skin rashes (maculopapular rash) and reduce the burden on testing resources. Moreover, this at-home screening information will encourage users to share this information with their doctor and help them to provide a correct diagnosis of the specific skin condition. For instance, if the newly found COVID-19 skin rash is identified, further tests can be ordered and extra precautions can be promptly placed for self-quarantine.
Lockdowns can do a lot to stem to tide of a COVID outbreak, but they only work in countries that are organized enough to manage the process tightly. In most countries the major challenge will be the early detection of the virus. In order to achieve this goal, getting tools that can assess the likelihood that there may be a medical reason to conduct further tests on a patient start to become more crucial. The way COVID infiltrates its way in to a community is through easy transmission, long incubation period, and asymptomatic carriers distributing the virus through communities. Simply put, the sooner a patient can be identified the sooner appropriate action can be taken by the patients, doctors, and other people fighting the disease.
At Opu Labs we have already shown the first proof-of-concept that proves creating such a system is achievable. Based on the analysis of COVID-19 images shared with us by doctors from around the world combined with the image data we have collected from our users, Opu Labs recently released the first version of AI enabled with a sophisticated neural network that is being trained to identify viral induced dermatological skin conditions. As the database grows, the aim is to improve the AI’s performance well enough so that the novel COVID-19 symptom of skin rashes can be precisely identified and for users to take this information to their care provider.
What our new tool highlights is the true gap in modern medicine when it comes to using all the tools available in the diagnosis of medical conditions in general. There is so much progress being made through private research and development that struggles to see the light of day in the community of traditional institutionalized medicine regardless of its efficacy.
With rapidly developing challenges with managing modern diseases, a paradigm shift is needed to ensure new technologies can be adopted in to mainstream medical practices. Any early diagnosis of people is critical given that COVID especially progresses and transmits readily. The most immediate need we have to address is an early warning system that encourages users to see a doctor for a diagnosis.
Photo by William Iven on Unsplash