Smartphone-based multispectral imaging and machine-learning based analysis for discrimination between seborrheic dermatitis and psoriasis on the scalp.

For appropriate treatment, accurate discrimination between seborrheic dermatitis and psoriasis in a timely manner is crucial to avoid complications. However, when they occur on the scalp, differential diagnosis can be challenging using conventional dermascopes. Thus, we employed smartphone-based multispectral imaging and analysis to discriminate between them with high accuracy. A smartphone-based multispectral imaging system, suited for scalp disease diagnosis, was redesigned. We compared the outcomes obtained using machine learning-based and conventional spectral classification methods to achieve better discrimination. The results demonstrated that smartphone-based multispectral imaging and analysis has great potential for discriminating between these diseases.

Smartphone-based multispectral imaging: system development and potential for mobile skin diagnosis.

We investigate the potential of mobile smartphone-based multispectral imaging for the quantitative diagnosis and management of skin lesions. Recently, various mobile devices such as a smartphone have emerged as healthcare tools. They have been applied for the early diagnosis of nonmalignant and malignant skin diseases. Particularly, when they are combined with an advanced optical imaging technique such as multispectral imaging and analysis, it would be beneficial for the early diagnosis of such skin diseases and for further quantitative prognosis monitoring after treatment at home. Thus, we demonstrate here the development of a smartphone-based multispectral imaging system with high portability and its potential for mobile skin diagnosis. The results suggest that smartphone-based multispectral imaging and analysis has great potential as a healthcare tool for quantitative mobile skin diagnosis.