The model can help AD patients detect complications from bacterial or viral infections and distinguish between eczema and skin lesions caused by mycosis fungoides.

A new artificial intelligence (AI) model can empower atopic dermatitis (AD) patients to detect complications from bacterial or viral infections and distinguish between eczema and skin lesions caused by mycosis fungoides.

The AI model is detailed in a paper published in the Journal of Dermatological Science. 

The team trained their convolutional neural network (CNN) model on non-standard images of AD, impetigo, mycosis fungoides, herpes simplex, and Kaposi varicelliform eruption. They then compared the AI's diagnosis accuracy to a set of non-standard images manually cropped and diagnostically annotated by dermatologists. They found that their system achieved a diagnostic accuracy almost equal to the manually assessed image set.

The team is currently developing an AI-powered smartphone app to translate their system, enabling patients to manage their skin diseases remotely with just their phone's camera. They are also experimenting with AD patients to improve the app's usability.

"A dermatologist would, of course, be able to spot the difference, but it is incredibly impractical for an AD patient to visit a dermatologist every day,” says Yuta Yanagisawa, a researcher with the Tohoku University School of Medicine and co-author of the paper, in a news release. “If only there were some handy, low-cost mechanism that replicated that dermatologist's knowledge and could be used during a patient's daily regimen of checking their skin."

PHOTO CAPTION: Images of skin lesions can vary significantly depending on the distance between the lesion and the imaging device. By minimizing the imaging bias caused by distance, the accuracy of skin lesion differentiation can be enhanced.

PHOTO CREDIT: Yuta Yanagisawa et al.