Supplements for manuscript: Automated classification of hidradenitis suppurativa disease severity by convolutional neural network analyses using clinical images – a prospective study.

Published: 21 November 2022| Version 1 | DOI: 10.17632/kgng2kb8y6.1
Antonia Wiala,


Figure legends: Supplemental Fig 1. Study workflow and CNN architecture: The dynamics of the disease were estimated using method A (mixed neural network) and method B with its segmentation variants were implemented to predict the severity scores and localization of lesion area, respectively. Supplemental Fig 2. Individual Typology Angle (ITA°) map. According to their ITA°, individuals were assigned to one of seven skin classification categories. To see if the difference between actual Fitzpatrick (Expert) and ITA phototype (Scarletred®Vision) are significant, a Mann Whitney U Test was conducted (n=149, significance level 5%). The null hypothesis for this test states that there will be no significant difference between the two groups. The test resulted in a p-value of ≈ 0.09 (> 0.05). Therefore, there is no significant difference between the two groups which supports Scarletred®Vision robustness to compute the ITA° related phototype. Supplemental Fig 3. Images were signal augmented and analyzed by measuring SEV*, L*, +a* and +b* values. Clear trends were observed in the SEV*_meandt-refdt and +a*_meandt-refdt (dark green squares, p<0.001), indicating the importance of the SEV* for classification and lesion detection also in this study 13. Supplemental Fig 4. The CNN predicted score aligned with changes of the IHS4, reflecting HS specific dynamics.



Artificial Intelligence, Clinical Assessment, Convolutional Neural Network, Hidradenitis Suppurativa