Diagnosing erythema migrans (EM) skin lesion, the most common early symptom of Lyme disease using deep learning techniques can be effective to prevent long-term complications. Existing works on deep learning based EM recognition only utilizes lesion image due to the lack of a dataset of Lyme disease related images with associated patient data. Physicians rely on patient information about the background of the skin lesion to confirm their diagnosis. In order to assist the deep learning model with a probability score calculated from patient data, this study elicited opinion from fifteen doctors. For the elicitation process, a questionnaire with questions and possible answers related to EM was prepared. Doctors provided relative weights to different answers to the questions. We converted doctors evaluations to probability scores using Gaussian mixture based density estimation. For elicited probability model validation, we exploited formal concept analysis and decision tree. The elicited probability scores can be utilized to make image based deep learning Lyme disease pre-scanners robust.

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.