Comparison of Actual Psoriasis Surface Area and the Psoriasis Area and Severity Index by the Human Eye and Machine Vision Methods in Following the Treatment of Psoriasis

Abstract

The lack of a quantitative method for assessing psoriasis severity poses a problem for quality control in dermatology. Quantitative estimation of involved surface area is important, as in the psoriasis area and severity index (PASI), but the reliability of many methods is poor. The purpose of this study was to assess the involved surface area of 15 psoriasis patients before and after different anti-psoriasis treatments using the human eye method and a computer image analysis (CIA) system based on colour segmentation. The human eye assessments were compared with the results of the CIA system and the resulting effects on the PASI score were also compared. The human eye estimates were higher than those obtained by the CIA method and, as a consequence, the values of the PASI by the human eye method were also higher than those by CIA. The human eye estimates differed most in cases where the PASI was under 15. The changes in the PASI by the human eye method before and after treatments differed significantly from those by CIA. The CIA system offers a possibility to quantify actual surface in patients with psoriasis, and will be an alternative for developing quality control when evaluating different treatment efficacies.

Targeting of interleukin-17 in the treatment of psoriasis

“…The Psoriasis Area and Severity Index (PASI) is useful for selection of a treatment strategy 16. A “PASI 75” response refers to a 75% or greater reduction in baseline PASI.…”

Machine Learning Applications in the Evaluation and Management of Psoriasis: A Systematic Review

“…We identified 12 articles related to severity and area grading of psoriasis using skin images. 37 -48…”

“…We identified 12 articles related to severity and area grading of psoriasis using skin images. [37][38][39][40][41][42][43][44][45][46][47][48] Dermatologists grade psoriasis severity according to the Psoriasis Area and Severity Index (PASI) and Physician Global Assessment (PGA) systems. 49 These severity grading systems involve clinical assessment of lesion erythema, scaliness, and induration by a dermatologist.…”

Therapeutic application of machine learning in psoriasis: A Prisma systematic review

“…Many computer vision methods are developed like which automatically assesses area for PASI scoring 7 . With actual comparison done between machine learning methods and human eye by Savolainen et al [8] Meienberger et al compared psoriasis lesion detection by different types of neural networks. One type was trained with unweighted objective function and the other one with penalty factor on false predictions of diseased regions to the manually marked psoriasis lesions using accuracy, F1 score, and difference in the area 9 …”