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

Kuan Yu Hsieh; Hung-Yi Chen; Sung-Cheol Kim; Yun-Ju Tsai; Hsien-Yi Chiu; Guan-Yu Chen

doi:10.1016/j.compbiomed.2022.105300

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

Comput Biol Med. 2022 Apr:143:105300. doi: 10.1016/j.compbiomed.2022.105300. Epub 2022 Feb 9.

Authors

Kuan Yu Hsieh¹, Hung-Yi Chen², Sung-Cheol Kim³, Yun-Ju Tsai⁴, Hsien-Yi Chiu⁵, Guan-Yu Chen⁶

Affiliations

¹ Department of Electrical and Computer Engineering, College of Electrical and Computer Engineering, National Yang Ming Chiao Tung University, Hsinchu, 30010, Taiwan; Institute of Biomedical Engineering, College of Electrical and Computer Engineering, National Yang Ming Chiao Tung University, Hsinchu, 30010, Taiwan. Electronic address: kuanyu.ee05@nycu.edu.tw.
² Institute of Biomedical Engineering, College of Electrical and Computer Engineering, National Yang Ming Chiao Tung University, Hsinchu, 30010, Taiwan. Electronic address: nhd123456ekek@gmail.com.
³ PsychoGenics, 215 College Rd, Paramus, NJ, 07652, USA. Electronic address: skim@psychogenics.com.
⁴ Department of Dermatology, National Taiwan University Hospital Hsinchu Branch, Hsinchu, Taiwan; Department of Dermatology, National Taiwan University Hospital Taipei, Taiwan; Department of Dermatology, College of Medicine, National Taiwan University, Taipei, Taiwan; Department of Dermatology, National Taiwan University BioMedical Park Hospital, Hsinchu, Taiwan. Electronic address: Andrewtsai.derma@gmail.com.
⁵ Department of Medical Research, National Taiwan University Hospital Hsinchu Branch, Hsinchu, Taiwan; Department of Dermatology, National Taiwan University Hsin-Chu Hospital, Hsinchu, Taiwan; Department of Dermatology, National Taiwan University Hospital Hsinchu Branch, Hsinchu, Taiwan; Department of Dermatology, National Taiwan University Hospital Taipei, Taiwan; Department of Dermatology, College of Medicine, National Taiwan University, Taipei, Taiwan. Electronic address: extra.owl0430@yahoo.com.tw.
⁶ Department of Electrical and Computer Engineering, College of Electrical and Computer Engineering, National Yang Ming Chiao Tung University, Hsinchu, 30010, Taiwan; Institute of Biomedical Engineering, College of Electrical and Computer Engineering, National Yang Ming Chiao Tung University, Hsinchu, 30010, Taiwan; Department of Biological Science and Technology, National Yang Ming Chiao Tung University, Hsinchu, 30010, Taiwan. Electronic address: guanyu@nycu.edu.tw.

PMID: 35172223
DOI: 10.1016/j.compbiomed.2022.105300

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.

Keywords: Computer-aided disease assessment; MASK R-CNN; NAPSI; Nail psoriasis; Standardized data acquisition.