AI Article Synopsis
- Psoriasis is a chronic skin disorder affecting 2-3% of people worldwide, with scaliness being a key factor in measuring its severity using the Psoriasis Area and Severity Index (PASI).
- Traditional PASI assessments are subjective, leading to variability in scores among dermatologists due to their reliance on visual and tactile evaluations.
- This study introduces an objective assessment method utilizing 3D surface roughness and clustering techniques, achieving 94.12% accuracy and high reliability, with kappa coefficients greater than 0.84, indicating almost perfect agreement on scaliness scoring.
Article Abstract
Psoriasis is an incurable skin disorder affecting 2-3% of the world population. The scaliness of psoriasis is a key assessment parameter of the Psoriasis Area and Severity Index (PASI). Dermatologists typically use visual and tactile senses in PASI scaliness assessment. However, the assessment can be subjective resulting in inter- and intra-rater variability in the scores. This paper proposes an assessment method that incorporates 3D surface roughness with standard clustering techniques to objectively determine the PASI scaliness score for psoriasis lesions. A surface roughness algorithm using structured light projection has been applied to 1999 3D psoriasis lesion surfaces. The algorithm has been validated with an accuracy of 94.12%. Clustering algorithms were used to classify the surface roughness measured using the proposed assessment method for PASI scaliness scoring. The reliability of the developed PASI scaliness algorithm was high with kappa coefficients>0.84 (almost perfect agreement).
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| http://dx.doi.org/10.1016/j.compbiomed.2013.08.009 | DOI Listing |
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