Lyme disease is a chronic, inflammatory condition with devastating, long-term effects. Spotting tick-bite rashes early on skin is the best way to prevent misdiagnosis and patients going untreated for long periods of time. But there is little documentation in textbooks of what these rashes look like on dark skin.

This left us asking ourselves: How can we improve identification and, ultimately diagnosis rates, of skin rash conditions (like Lyme) on Black and Brown skin?

The Lyme Bomb Detector is the first smartphone tool allowing for earlier detection and diagnosis of Lyme rashes on people of color.

The incidence of Lyme disease is on the rise around the country, with a historic spike underway in urban areas, where many Black and Brown patients reside.

Managing dark-skinned patients with Lyme disease presents multiple challenges. Racial health disparities in the US healthcare system undermine ethnic patient outcomes in most diseases. Making matters worse, most medical training related to skin conditions has little or no representation of Black or Brown skin.

In fact, a recent study found that 1 in 3 Black patients who were newly diagnosed with Lyme disease already had related neurological complications—a sign that the disease may have been ignored initially and not caught early enough to intervene effectively.

Our goal is to help close this significant racial gap in Lyme disease diagnosis and care by utilizing widely available smartphone camera tech (something many already have access to) to identify Lyme rashes quicker in people of color.

Working with the Global Lyme Alliance, we learned that Lyme disease is surprisingly increasing in our urban communities.

We also discovered that tell-tale Lyme rashes, which are normally seen easily on white skin, are much more difficult to identify on Black and Brown skin.

Given these two facts, we designed a tool that utilizes smartphone imaging and camera technology to turn an almost invisible tick bite rash on dark skin into something much easier to detect.

In essence, we're using computer vision to see what human vision cannot.

1/2021 to 5/2021

Ideation and Conceptualization

We began conceiving and designing the solution to utilize smartphone camera tech for analyzing and diagnosing invisible rash on dark skin.

5/2021 to 7/2021

Prototyping Research and Development

We had an initial proof-of-concept with a working algorithm that would scour the web for available rashes and match them up to the photo in question via a neural network model and our own proprietary "Cognitive Core" platform.

7/2021 to 9/2021

Obtained Expert Input

We spent time speaking with Lyme specialists. Dr. Crystal Barnwell, MD acted as our lead consultant for understanding the history, the needs, and the overall experience of dark-skinned patients struggling to manage conditions like Lyme disease (that begin with accurately detecting a rash).

9/2021 to present

Refinement and Testing

We've been working to progress use of smartphone image editing abilities, applying them to real-world Black skin rash examples. Although funding has become a barrier, our goal remains to educate city-dwellers about the unseen culprits and casualties of Lyme disease, and to help physicians overcome their own biases by letting a computer algorithm contrast and evaluate the skin that is affected. Our hope is that this tech will give physicians (and patients) a sharper eye and lead to more accurate diagnosis of Lyme disease earlier (when interventions are most successful).

Target Launch Date: Late 2022

Smartphone imaging technology has seen leaps in innovation at staggering rates. Tech once reserved for commercial and feature film is now available on mobile devices. Powerful image editing is mere taps away. Our goal was to harness this power to level the rash identification and diagnosis playing field for Black and Brown people.

Working with the Global Lyme Alliance and leading Lyme specialists, we designed a tool powered by artificial intelligence using a proprietary Cognitive Core platform. It enables people to check their skin for rashes and assess their risk. All they have to do is take a picture and the tool takes it from there by analyzing the image using a neural network model which has been trained with hundreds of thousands of photos with different variations of visual symptoms.

The Lyme Bomb Detector is a must-have for everyone with Black or Brown skin. The greatest power of this tool is that it can help physicians overcome their own biases and let a computer algorithm contrast and evaluate the skin that is affected to hopefully give physicians a sharper eye—and patients a more accurate Lyme disease diagnosis earlier (when interventions are most successful).

In partnership with the Global Lyme Alliance and Dr. Crystal Barnwell, MD, we are working rapidly with internal agency technologists and creatives to bring the tool to Apple and Google stores by late 2022.

Although we plan to pilot the project in New York City first, we hope for it to spread to all major cities where Lyme disease is unknowingly on the rise.