Psychotic disorders such as schizophrenia (SCH) and bipolar affective disorder (BD) are associated with lipid metabolism abnormalities and inflammatory dysregulation. The niacin skin flush test (NSFT) has long been investigated as a non-invasive indicator of these disturbances. This study used deep learning models to assess the diagnostic utility of SKINREMS, a computerized system for automated temporal analysis of skin flush responses. The study included a total of 188 participants, comprising individuals with psychotic disorders and healthy controls. Sequential skin images were recorded after topical application of methyl nicotinate. Five convolutional neural network architectures—ResNet50, ResNet101, DenseNet121, InceptionV3, and EfficientNetB0—were evaluated for their performance in analyzing these time-dependent dermatological responses in a binary classification task. Accuracy, precision, recall, F1-score, and AUC were calculated at four time points (frames 1, 10, 20, 30). The models demonstrated distinct temporal performance profiles. ResNet50 showed consistent high performance across all time points, making it suitable for clinical environments requiring stable predictions. DenseNet121 achieved the highest AUC (up to 0.99) after 15 min, indicating its potential in extended monitoring. EfficientNetB0 offered gradual performance improvement with lower computational demands, while InceptionV3 was most effective at intermediate time points. ResNet101 showed initial high performance but declined mid-phase. AUC remained stable across all models, suggesting robust discriminative capability over time. This study highlights the importance of selecting appropriate deep learning architectures based on the temporal dynamics of biological responses. The findings demonstrate potential for future clinical application of AI in non-invasive diagnostics of psychotic spectrum disorders. Keywords: CNN; deep learning; schizophrenia; niacin skin flush test; fatty acids metabolism; biomarkers