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Precision Medicine in Glaucoma: Artificial Intelligence, Biomarkers, Genetics and Redox State.

ABSTRACT: Glaucoma is a multifactorial neurodegenerative illness requiring early diagnosis and strict monitoring of the disease progression. Current exams for diagnosis and prognosis are based on clinical examination, intraocular pressure (IOP) measurements, visual field tests, and optical coherence tomography (OCT). In this scenario, there is a critical unmet demand for glaucoma-related biomarkers to enhance clinical testing for early diagnosis and tracking of the disease's development. The introduction of validated biomarkers would allow for prompt intervention in the clinic to help with prognosis prediction and treatment response monitoring. This review aims to report the latest acquisitions on biomarkers in glaucoma, from imaging analysis to genetics and metabolic markers.

SUBMITTER: Fea AM

PROVIDER: S-EPMC9917798 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature

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Precision Medicine in Glaucoma: Artificial Intelligence, Biomarkers, Genetics and Redox State.

Fea Antonio Maria AM Ricardi Federico F Novarese Cristina C Cimorosi Francesca F Vallino Veronica V Boscia Giacomo G

International journal of molecular sciences 20230201 3

Glaucoma is a multifactorial neurodegenerative illness requiring early diagnosis and strict monitoring of the disease progression. Current exams for diagnosis and prognosis are based on clinical examination, intraocular pressure (IOP) measurements, visual field tests, and optical coherence tomography (OCT). In this scenario, there is a critical unmet demand for glaucoma-related biomarkers to enhance clinical testing for early diagnosis and tracking of the disease's development. The introduction ...[more]

PMID: 36769127

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Project description:Precision medicine has the potential to revolutionize the way cardiovascular diseases are diagnosed, predicted, and treated by tailoring treatment strategies to the individual characteristics of each patient. Artificial intelligence (AI) has recently emerged as a promising tool for improving the accuracy and efficiency of precision cardiovascular medicine. In this scoping review, we aimed to identify and summarize the current state of the literature on the use of AI in precision cardiovascular medicine. A comprehensive search of electronic databases, including Scopes, Google Scholar, and PubMed, was conducted to identify relevant studies. After applying inclusion and exclusion criteria, a total of 28 studies were included in the review. We found that AI is being increasingly applied in various areas of cardiovascular medicine, including the diagnosis, prognosis of cardiovascular diseases, risk prediction and stratification, and treatment planning. As a result, most of these studies focused on prediction (50%), followed by diagnosis (21%), phenotyping (14%), and risk stratification (14%). A variety of machine learning models were utilized in these studies, with logistic regression being the most used (36%), followed by random forest (32%), support vector machine (25%), and deep learning models such as neural networks (18%). Other models, such as hierarchical clustering (11%), Cox regression (11%), and natural language processing (4%), were also utilized. The data sources used in these studies included electronic health records (79%), imaging data (43%), and omics data (4%). We found that AI is being increasingly applied in various areas of cardiovascular medicine, including the diagnosis, prognosis of cardiovascular diseases, risk prediction and stratification, and treatment planning. The results of the review showed that AI has the potential to improve the performance of cardiovascular disease diagnosis and prognosis, as well as to identify individuals at high risk of developing cardiovascular diseases. However, further research is needed to fully evaluate the clinical utility and effectiveness of AI-based approaches in precision cardiovascular medicine. Overall, our review provided a comprehensive overview of the current state of knowledge in the field of AI-based methods for precision cardiovascular medicine and offered new insights for researchers interested in this research area.

Project description:IntroductionInflammatory Bowel Disease (IBD), encompassing Crohn's disease and ulcerative colitis, presents significant clinical challenges due to its heterogeneous nature and complex etiology. Recent advancements in biomedical research have enhanced our understanding of IBD's genetic, microbial, and biochemical aspects. However, persistent issues in clinical management, including treatment non-response, surgical interventions, and diagnostic uncertainties, underscore the need for more targeted approaches. This review examines the convergence of artificial intelligence (AI) and precision medicine (PM) in IBD management. By leveraging AI's capacity to analyze complex, multi-dimensional datasets, this emerging field offers promising applications in improving diagnostic accuracy, predicting treatment responses, and forecasting disease progression, potentially transforming IBD patient care.MethodThe systematic review (SR) was conducted by searching the following databases: PubMed, PubMed PMC, BVS, Scopus, Web of Science, Embase, Cochrane, and ProQuest up to February 2024. Studies that employed AI in IBD applied to precision medicine were included.Results139 studies on applying AI in precision medicine for IBD were identified. Most studies (>70%) were published after 2020, indicating a recent surge in interest. The AI applications primarily focused on diagnosis, treatment response prediction, and prognosis. Machine learning algorithms were predominantly used, particularly random forest, logistic regression, and support vector machines. Omics data were frequently employed as predictors, especially transcriptomics and microbiome analyses. Studies demonstrated good predictive performance across all three areas, with median AUC values ranging from 0.85 to 0.90.ConclusionAI applications in IBD show promising potential to enhance clinical practice, particularly in disease prognosis and predicting treatment response. However, clinical implementation requires further validation through prospective studies. Future research should focus on standardizing protocols, defining clinically significant outcomes, and evaluating the efficacy of these tools.

Dataset Information

Precision Medicine in Glaucoma: Artificial Intelligence, Biomarkers, Genetics and Redox State.

Publications

Precision Medicine in Glaucoma: Artificial Intelligence, Biomarkers, Genetics and Redox State.

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