Predicting psoriasis using routine laboratory tests with random forest

Artificial intelligence in psoriasis: Where we are and where we are going

Artificial intelligence (AI) is a field of computer science that involves the development of programs designed to replicate human cognitive processes and the analysis of complex data. In dermatology, which is predominantly a visual-based diagnostic field, AI has become increasingly important in improving professional processes, particularly in the diagnosis of psoriasis. In this review, we summarized current AI applications in psoriasis: (i) diagnosis, including identification, classification, lesion segmentation, lesion severity and area scoring; (ii) treatment, including prediction treatment efficiency and prediction candidate drugs; (iii) management, including e-health and preventive medicine. Key challenges and future aspects of AI in psoriasis were also discussed, in hope of providing potential directions for future studies.

Enhanced Deep Learning Approach for Accurate Eczema and Psoriasis Skin Detection

This study presents an enhanced deep learning approach for the accurate detection of eczema and psoriasis skin conditions. Eczema and psoriasis are significant public health concerns that profoundly impact individuals' quality of life. Early detection and diagnosis play a crucial role in improving treatment outcomes and reducing healthcare costs. Leveraging the potential of deep learning techniques, our proposed model, named "Derma Care," addresses challenges faced by previous methods, including limited datasets and the need for the simultaneous detection of multiple skin diseases. We extensively evaluated "Derma Care" using a large and diverse dataset of skin images. Our approach achieves remarkable results with an accuracy of 96.20%, precision of 96%, recall of 95.70%, and F1-score of 95.80%. These outcomes outperform existing state-of-the-art methods, underscoring the effectiveness of our novel deep learning approach. Furthermore, our model demonstrates the capability to detect multiple skin diseases simultaneously, enhancing the efficiency and accuracy of dermatological diagnosis. To facilitate practical usage, we present a user-friendly mobile phone application based on our model. The findings of this study hold significant implications for dermatological diagnosis and the early detection of skin diseases, contributing to improved healthcare outcomes for individuals affected by eczema and psoriasis.

The association between platelet indices and presence and severity of psoriasis: a systematic review and meta-analysis

The role of platelet function indices-platelet count (PLT), mean platelet volume (MPV), platelet distribution width (PDW), plateletcrit (PCT), immature platelet fraction (IPF), and platelet mass index (PMI)-in psoriasis is uncertain. This systematic review and meta-analysis aimed to evaluate the association of these platelet biomarkers with both presence and severity of psoriasis. We searched MEDLINE (Ovid), Embase (Ovid), and the Cochrane Library from inception to November 2021. To evaluate the association of platelet function indices and psoriasis, we recorded mean differences (MD) and 95% confidence intervals (CI) as well as correlation coefficients (r) for each included study, and generated summary estimates using random-effects inverse-variance modelling. We screened 1,079 unique studies, and included 33 studies with 6724 patients in the quantitative analyses. Compared with controls, patients with psoriasis had higher PLT (MD 12.86 × 10⁹/L, 95% CI 6.34-19.39, p < 0.001), MPV (MD 0.61fL, 95% CI 0.31-0.92, p < 0.001), and PCT (MD 0.05%, 95% CI 0.01-0.09, p = 0.010), but similar PDW (MD 0.16%, 95% CI -0.46-0.79, p = 0.610). Psoriasis Area and Severity Index (PASI) was weakly correlated with PLT (r 0.17, 95% CI 0.06-0.28, p = 0.003), MPV (r 0.36, 95% CI 0.22-0.49, p < 0.001), and PDW (r 0.17, 95% CI 0.08-0.26, p < 0.001). Study numbers were insufficient to judge the relationship of IPF and PMI with psoriasis presence, or PCT, IPF, and PMI with psoriasis severity. In summary, PLT, MPV, and PCT are significantly elevated in patients with psoriasis, and PLT, MPV, and PDW are weakly correlated with PASI. Future studies are needed to evaluate the independent diagnostic and prognostic potentials of these biomarkers in patients with psoriasis.