Predicting psoriasis severity using machine learning: A systematic review.

In dermatology, the applications of machine learning (ML), an artificial intelligence (AI) subset that enables machines to learn from experience, have progressed past the diagnosis and classification of skin lesions. A lack of systematic reviews exists to explore the role of ML in predicting the severity of psoriasis. This systematic review aims to identify and summarize the existing literature on predicting psoriasis severity using ML algorithms and identify gaps in current clinical applications of these tools. OVID Embase, OVID MEDLINE, ACM Digital Library, Scopus, and IEEE Xplore were searched from inception to August, 2024. A total of 30 articles met our inclusion criteria and were included in this review. One article used serum biomarkers, while the remaining 29 used image-based models. The most common severity assessment score employed by these ML models was the Psoriasis Area Severity Index score, followed by Body Surface Area, with fifteen and five articles, respectively. The small size and heterogeneity of the existing literature are the primary limitations of this review. Progress in assessing skin lesion severity through ML in dermatology has advanced, but prospective clinical applications remain limited. ML and AI promise to improve psoriasis management, especially in non-image-based applications requiring further exploration. Large-scale prospective trials using diverse image datasets are necessary to evaluate and predict the clinical value of these predictive AI models.

Immune activation markers in individuals with HIV-1 disease and their correlation with HIV-1 RNA levels in individuals on antiretroviral therapy.

BACKGROUND: Currently CD4+ T lymphocyte counts and HIV-1 RNA levels are being utilized to predict outcome of human immunodeficiency virus (HIV) disease. Recently, the role of immune activation in HIV disease progression and response to treatment is being investigated. This study focused on the expression of CD38 and HLA-DR on lymphocyte subsets in various groups of HIV-infected individuals and to determine their association with HIV-1 disease progression. METHODS: Ninety-eight cases of patients with HIV/AIDS in different disease stages and twenty-four healthy HIV-negative individuals were included in the cross-sectional study. Their immune function and abnormal immune activation markers (CD38 & HLA-DR) were detected using a flowcytometer, and HIV-1 RNA levels in individuals receiving antiretroviral drugs were estimated. RESULTS: The immune activation marker levels were significantly different between patients with different disease stages (P < 0.001). A significant negative correlation was observed between peripheral blood CD4+ T cell counts and immune activation markers. Also, a significant positive correlation was observed between HIV-1 RNA levels and CD38+CD8+ T lymphocyte. CONCLUSION: Immune activation markers (CD38 & HLA-DR) increase with disease progression. CD38+ on CD8+ T lymphocyte correlates well with HIV1 RNA levels in individuals failing on antiretroviral therapy.

Can machine learning assist in systemic sclerosis diagnosis and management? A scoping review.

This scoping review aims to summarize the existing literature on how machine learning can be used to impact systemic sclerosis diagnosis, management, and treatment. Following Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) reporting guidelines, Embase, Web of Science, Medline (PubMed), IEEE Xplore, and ACM Digital Library were searched from inception to 3 March 2024, for primary literature reporting on machine learning models in any capacity regarding scleroderma. Following robust triaging, 11 retrospective studies were included in this scoping review. Three studies focused on the diagnosis of scleroderma to influence preferred management and nine studies on treatment and predicting treatment response to scleroderma. Nine studies used supervision in their machine learning model training; two used supervised and unsupervised training and one used solely unsupervised training. A total of 817 patients were included in the data sets. Seven of the included articles used patients from the United States, one from Belgium, two from Japan, and two from China. Although currently limited to retrospective studies, the results indicate that machine learning modeling may have a role in early diagnosis, management, therapeutic decision-making, and in the development of future therapies for systemic sclerosis. Prospective studies examining the use of machine learning in clinical practice are recommended to confirm the utility of machine learning in patients with systemic sclerosis.