Artificial intelligence can be used to evaluate smartphone photos of suspicious rashes and detect Lyme disease earlier, according to a new study. Lyme disease affects roughly 300,000 people in the US every year and is transmitted through the bite of an infected deer tick. A painless rash, called Erythema migrans (EM), usually appears a week or so later, followed by more serious symptoms including fever, headache, chills, joint pain and swollen lymph glands. Lyme disease is most effectively treated if caught early. Untreated, it can cause cognitive impairment, chronic fatigue, heart palpitations and painful swelling that can last from months to years.

Artificial intelligence can be used to evaluate smartphone photos of suspicious rashes and detect Lyme disease earlier, according to a new study. Lyme disease affects roughly 300,000 people in the US every year and is transmitted through the bite of an infected deer tick. A painless rash, called Erythema migrans (EM), usually appears a week or so later, followed by more serious symptoms including fever, headache, chills, joint pain and swollen lymph glands. Lyme disease is most effectively treated if caught early. Untreated, it can cause cognitive impairment, chronic fatigue, heart palpitations and painful swelling that can last from months to years.

Examples of correct and incorrect visual identifications of the erythema migrans (EM) rash commonly seen in patients with Lyme disease. The images in the top right quadrant actually are EM (true positives). The upper right photos are false negatives, the lower left are false positives and the lower right were correctly ruled out as EM (true negatives). A new AI/deep learning technique from Johns Hopkins Medicine and the Johns Hopkins Applied Research Laboratory greatly increases the chances of correctly identifying EM in photographs. Johns Hopkins Medicine and Johns Hopkins Applied Research Laboratory (APL) researchers have shown that cell phone images of rashes taken by patients can be evaluated using artificial intelligence (AI) and deep learning (DL) technologies to more accurately detect and identify the erythema migrans (EM) skin redness associated with acute Lyme disease.

A report on the findings was published in the October 2020 issue of the journal Computers in Biology and Medicine. APL scientists developed and tested several deep learning computer models to accurately pick out EM from other dermatological conditions and normal skin. The DL models were "trained" to discern the appearance of EM using images of non-EM rashes and normal skin available in the public domain, and clinical photos of patients with EM provided by the Johns Hopkins University Lyme Disease Research Center and the Lyme Disease Biobank, part of the Johns Hopkins University School of Medicine's Division of Rheumatology. There are more than 300,000 new cases of Lyme disease annually in the United States and treatment is most effective if it is caught early. Misdiagnosis, especially in the disease's initial stages, is common because of several challenges.