Observer‐independent assessment of psoriasis‐affected area using machine learning

Abstract

Background Assessment of psoriasis severity is strongly observer-dependent, and objective assessment tools are largely missing. The increasing number of patients receiving highly expensive therapies that are reimbursed only for moderate-to-severe psoriasis motivates the development of higher quality assessment tools. Objective To establish an accurate and objective psoriasis assessment method based on segmenting images by machine learning technology. Methods In this retrospective, non-interventional, single-centred, interdisciplinary study of diagnostic accuracy, 259 standardized photographs of Caucasian patients were assessed and typical psoriatic lesions were labelled. Two hundred and three of those were used to train and validate an assessment algorithm which was then tested on the remaining 56 photographs. The results of the algorithm assessment were compared with manually marked area, as well as with the affected area determined by trained dermatologists. Results Algorithm assessment achieved accuracy of more than 90% in 77% of the images and differed on average 5.9% from manually marked areas. The difference between algorithm-predicted and photograph-based estimated areas by physicians was 8.1% on average. Conclusion The study shows the potential of the evaluated technology. In contrast to the Psoriasis Area and Severity Index (PASI), it allows for objective evaluation and should therefore be developed further as an alternative method to human assessment.

Quantification of Efflorescences in Pustular Psoriasis Using Deep Learning

“…Some studies [ 21 – 23 ] chose to rely on classification DLMs, thus capping the achievable precision to discrete scores in contrast to our DLM, which predicts continuous metrics. Various segmentation approaches have also been applied to ulcers [ 24 ], skin cancer [ 25 , 26 ], eczema [ 27 ], and psoriasis [ 7 , 28 ], and therefore could also be used to produce metrics similar to our study. However, they all targeted diseases with plaques, single lesions, or lesions larger than PP efflorescences.…”

“…They have repeatedly achieved superhuman performance in image recognition tasks, progressing to general images today. Successful applications to medical image analysis include skin cancer classification [ 6 ], psoriasis or brain tumor segmentation [ 7 , 8 ] and even synthetic medical data generation [ 9 ].…”

Machine Learning in Dermatology: Current Applications, Opportunities, and Limitations

“…For example, for psoriasis, dermatologists use the Psoriasis Area and Severity Index (PASI) as the gold standard to assess severity based on body surface area involved, erythema, induration, and scaling. ML models have been developed to singly assess body surface area [61,62], scaling [63], induration/color [64][65][66][67], and erythema only [68] in patients with psoriasis.…”

Artificial Intelligence in Translational Medicine

“…Following this trend, other studies have employed data from dermoscopic images sometimes combined with macroscopic images for training supervised or unsupervised ML models, based principally on CNN algorithms to detect and/or classify cutaneous malignancies, including melanoma and basal cell carcinoma [330][331][332][333][334][335][336][337]. Notably, CNN algorithms showed interesting performances also in classifying and detecting other relevant dermatological disorders, including onychomycosis, rosacea, atopic dermatitis, and psoriasis [338][339][340][341][342][343][344]. Considering the Asan dataset, the area under the ROC curve concerning the diagnosis of basal cell carcinoma, squamous cell carcinoma, intraepithelial carcinoma, and melanoma was 0.96, 0.83, 0.82, and 0.96, respectively.…”