## 4 Discussion  
In this study, we focused on non-invasive live imaging with the LC-OCT of PP under therapy, comparing manual and AI-supported measurements of epidermal thicknesses in lesional and healthy skin. The results of this study show a significant reduction in acanthosis and hyperkeratosis measurable by LC-OCT, correlating with the significant reduction in the clinical severity (LPSI, PASI) and improvement in quality of life (DLOI). In comparing the ET and SCT of the CoA to the corresponding values in the SP during the study period, an approximation of the SP to the CoA could be seen. The applicability of an AI model for the evaluation of epidermal alterations and the severity of actinic keratosis has already been shown [32]. In our study, an AI-supported measurement of the ET and SCT was possible, calculating reliable and highly correlating values compared to subjectively scored measurements. In the future, this is a chance for a faster and more objective evaluation of PP by LC-OCT imaging. AI-supported analysis leads to higher accuracy of the measurements, as it evaluates the thickness all over the scanned part of the lesion, calculating a mean value. Subjective quantifications of ET in different regions in one scan are time-consuming and cannot measure such exact and objective results. We verified all scans with a difference in thickness of more than 30% between both methods. If the DEJ was not visible, the AI algorithm measured until the end of the 3D stack in these parts. Careful revision is crucial when applying an AI model, since it is not capable of stating that measurements are not possible due to bad or poor visibility and always tries to segment. For the stratum corneum, AI-supported measurements showed slightly higher values, especially in CoAs and SPs after effective treatment, adding the first layers of the stratum spinosum to the stratum corneum. The AI model was trained to differentiate stratum corneum and epidermis through the detection of nuclei in the cells, thus drawing the boundary between these layers (stratum corneum: without nuclei or white nuclei for parakertosis; epidermis: regular, signal poor nuclei). For the upper parts of the stratum granulosum, this can be difficult to detect, even for a trained expert. However, results often differed by only a few micrometers; because of the thin layer, this results in high percentage variations without clinical relevance. The median percentage difference when applying a linear regression model for the difference between manual and AI model-measured SCT was 30.8%. For even more accurate findings in the future, a re-training of the AI algorithm could be aimed for. For the ET, subjective measurements were often slightly higher than the corresponding AI model measurements. This is most likely because the more elongated rete ridges were measured manually. The median percentage difference \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Alteration** & **V0 SP** & **V1 SP** & **V2 SP** & **V0 CoA** & **V1 CoA** & **V2 CoA** \\ \hline acanthosis & 12/12 & 9/12 & 3/12 & 0/12 & 0/12 & 0/12 \\ \hline hyperkeratosis & 12/12 & 9/12 & 3/12 & 0/12 & 0/12 & 0/12 \\ \hline parakertosis & 12/12 & 10/12 & 7/12 & 0/12 & 0/12 & 0/12 \\ \hline bright inflammatory cell epidermis & 10/12 & 10/12 & 9/12 & 10/12 & 9/12 & 8/12 \\ \hline elongated rete ridges & 11/12 & 10/12 & 7/12 & 0/12 & 0/12 & 0/12 \\ \hline dilated vessels & 11/12 & 11/12 & 9/12 & 7/12 & 6/12 & 5/12 \\ \hline bright inflammatory cell dermis & 2/3 & 4/9 & 8/11 & 9/12 & 8/12 & 7/12 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Further dermo-epidermal alterations in the study plaque (SP) and control area (CoA) in the course of therapy. If examination of the criterion was not possible, due to extensive acanthosis and/or hyperkeratosis, the patient was excluded for the analysis of this criterion. First number = number of patients presenting the criterion, second number = number of examinable patients.