A system and methods for the detection of the LD rash and other skin conditions

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Unmet Need
The direct medical costs associated with Lyme disease in the United States are estimated at 2.5 billion dollars annually. Precise diagnosis from laboratory results are only most accurate if taken within a few weeks of being infected. The problem is the symptoms are wide varying and come and go. In addition, one test ELISA (enzyme-linked immunosorbent assay) test. This test can’t check for the bacteria that causes Lyme disease. It can only look for your immune system’s response to it. This test addresses this problem through developing an automated referral of individuals with Lyme disease erythema rash (EM) image. This automated method solves the issue above as it allows for a computer automated algorithm to refer someone with the risk of Lyme disease, based on their skin symptoms that are common, to the correct diagnosis. There is also a need for early diagnosis along with an ability to differentiate and classify different rashes that are Lyme disease compared to those that are not. Early treatment is necessary to prevent long term morbidity from later stages of untreated Lyme disease.
Technology Overview
The technology disclosed by the inventors has shown that their automated referral algorithm is able to use computerized screening based on the photos patients take with their own cameras. The algorithm is capable of distinguishing and classifying EM rashes when compared to look alike skin lesions that are not actually from Lyme disease. This allows for a prescreening that will triage individuals with a high likelihood of Lyme EM for physician diagnosis and treatment, as early treatment for Lyme disease is key to the survival rates of patients. This solves the problem of later stage diagnosis as it allows for patients to just take pictures of their rash and put it in the algorithm, which accurately tells them if they have Lyme disease and what their next steps for receiving help should be. It allows for a more accurate and efficient prescreening process for physicians.

Stage of Development
The inventors have currently developed a prototype of the algorithm and are beginning tests.  
Patent Information:
Title App Type Country Serial No. Patent No. File Date Issued Date Expire Date Patent Status
DEEP LEARNING LYME DISEASE DIAGNOSIS PCT: Patent Cooperation Treaty PCT PCT/US2019/052724   9/24/2019     Expired
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Lisa Schwier
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