## 2 Materials and Methods
A few studies [22; 28; 29; 30; 31; 32; 33; 34; 35; 36] have already shown the applicability of this U-Net AI model in the evaluation of healthy skin, actinic keratoses, atopic dermatitis, and also in the evaluation of plaque psoriasis. The segmentation of the skin layers and detection of hair follicles was visually supervised by an expert team to ensure validated results. All 3D stacks with a variation of more than 30% between manually and AI model-generated measurements were reviewed by an expert team to detect possible reasons for differences or wrong measurements. The number of pixels between the blue and red line (see Figures 2 and 3) divided by the number of pixels corresponding to the region of interest was used for calculating the SC thickness (SCT). The result in microns was obtained with the pixel-micron equivalence. The same procedure was used for the ET, calculating the number of pixels between the red and green line and summing up the SCT. DEJ undulation was quantified as the ratio of the DEJ contour length to the straight-line length of the analyzed area in percent. In acquisitions without visible DEJ, because of excessive acanthosis, the AI model placed the green line at the bottom of the stack. For each parameter, the area detected as a follicle by the follicle detection model was excluded from the calculation in order to prevent a bias of the skin layer measurements. One 3D stack had to be excluded from the AI-supported calculation because of a severe mismeasurement by the AI model caused by scales and dust in between the skin surface and probe, which led to a misrecognition of great parts of the skin surface. Subjectively scored calculations in this 3D stack were possible. Figure 2: The artificial intelligence (AI) model segmented line-field confocal optical coherence tomography stacks (only the vertical view within the 3D stack depicted here, LC-OCT image size: 1.2 mm \(\times\) 0.4 mm) of study plaque (left side) and control area (right side). Blue line = skin surface; red line = boundary between stratum corneum and stratum granulosum/living epidermis; green line = before dermo-epidermal junction. V0 = before therapy, V1 = after 4 weeks of therapy, V2 = after 12 weeks of therapy. For the statistical analysis, nonparametric tests were used due to the non-normal distribution of the data. Clinical scores (PASI, DLQI, GEPARD, and LPSI) as well as measurements (SCT, ET, and DEJ undulation) were compared between all visits with the Friedman test. Further, differences between the SP and CoA were calculated and compared between all visits. The distribution of all measurements at each visit is depicted with boxplots. A statistical comparison between the AI-supported and manual measurements was carried out together with specialists of the DAMAE Medical team. Pearson's and Spearman's coefficients were computed for the correlation between AI-supported and subjective measurements of ET and SCT. Additionally, the percentage deviation between both methods was provided. A linear regression analysis was performed to judge the agreement of both methods, where an intercept of 0 and a slope of 1 indicate perfect accordance. A \(p\)-value less than 0.05 was considered significant. Statistical analysis was performed using R version 4.3.1.