Summary The study shows that deep machine learning can be utilized to more accurately identify erythema migrans rashes in early Lyme disease. Recognition of the EM rash is crucial to early diagnosis and treatment. Improved rash recognition using deep learning methodology to prescreen patient rash photos may help prevent later serious manifestations of Lyme disease. […]
This study uses a neuroimaging radiotracer with positron emission tomography (PET) to quantify cerebral glial activation in brains of patients with post-treatment Lyme disease. Results show elevated central nervous system immune activation in patients with PTLDS as compared to controls.