A mask R-CNN based automatic assessment system for nail psoriasis severity

Abstract

Nail psoriasis significantly impacts the quality of life in patients with psoriasis, which affects approximately 2–3% of the population worldwide. Disease severity measures are essential in guiding treatment and evaluation of therapeutic efficacy. However, due to subsidy, convenience and low costs of health care in Taiwan, doctor usually needs to manage nearly hundreds of patients in single outpatient clinic, leading to difficulty in performing complex assessment tools. For instance, Nail Psoriasis Severity index (NAPSI) is used by dermatologists to measure the severity of nail psoriasis in clinical trials, but its calculation is quite time-consuming, which hampers its application in daily clinical practice. Therefore, we developed a simple, fast and automatic system for the assessment of nail psoriasis severity by constructing a standard photography capturing system combined with utilizing one of the deep learning architectures, mask R-CNN. This system not only assist doctors in capturing signs of disease and normal skin, but also able to extract features without pre-processing of image data. Expectantly, the system could help dermatologists make accurate diagnosis, assessment as well as provide precise treatment decision more efficiently.