AI and deep learning can analyze ‘rash selfies’ for better Lyme disease detection

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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. Credit: Johns Hopkins Medicine

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. This can enable more reliable screening, more accurate diagnosis and earlier treatment, helping avoid the serious potential neurologic, rheumatologic and cardiac complications of advanced 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 …

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