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Start date: 2020End date: 2026Type: search.filters.itemtype.ReviewSubject: search.filters.subject.HumanSubject: search.filters.subject.Artificial IntelligenceHas files: NoAuthor: search.filters.author.Uwishema, Olivier

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    Environmental Factors and Cardiovascular Susceptibility: Toward Personalized Prevention Mediated by the Role of Artificial Intelligence–A Narrative Review
    (John Wiley and Sons Inc, 2025) Soufan, Fatima; Tukur, Hajar Nasir; Tamir, Ruth Girum; Muhirwa, Ernest; Wojtara, Magda Sara; Uwishema, Olivier; Soufan, Fatima, Department of Research and Education, Oli Health Magazine Organization, Kigali, Rwanda, Faculty of Medicine, Beirut Arab University, Beirut, Lebanon; Tukur, Hajar Nasir, Department of Research and Education, Oli Health Magazine Organization, Kigali, Rwanda, Faculty of Medicine, Bahçeşehir Üniversitesi, Istanbul, Turkey; Tamir, Ruth Girum, Department of Research and Education, Oli Health Magazine Organization, Kigali, Rwanda, Addis Ababa Health Bureau, Addis Ababa, Ethiopia; Muhirwa, Ernest, Department of Research and Education, Oli Health Magazine Organization, Kigali, Rwanda, World Vision International, Monrovia, United States; Wojtara, Magda Sara, Department of Research and Education, Oli Health Magazine Organization, Kigali, Rwanda, University of Michigan Medical School, Ann Arbor, United States; Uwishema, Olivier, Department of Research and Education, Oli Health Magazine Organization, Kigali, Rwanda
    Background and Purpose: Cardiovascular diseases (CVD) represent a significant global health challenge due to high morbidity and mortality rates, that necessitate approaching the intricate relation between cardiovascular susceptibility and environmental factors, highlighting the importance of creating personalized cardiovascular prevention plans. Furthermore, as it is becoming integrated with the various aspects of healthcare, the role of artificial intelligence in cardiovascular and precision medicine is driving innovations towards personalized care. This review dives into the complex connection between cardiovascular susceptibility and environmental risk highlighting the importance of creating personalized cardiovascular preventive strategies in light of the upcoming artificial intelligence. Methods: An in-depth review was conducted using PubMed and ScienceDirect, to collect data from all articles that handled environmental factors and cardiovascular susceptibility with special emphasis on the up-to-date emerging role of artificial intelligence in preventive strategies. Results: The review revealed high heritability estimates and highlighted the significance of modifiable risk factors which are pivotal determinants affecting CVD susceptibility. The integration of artificial intelligence is implementing the power of precision preventive medicine that can be directed toward specific environmental factors, shifting the whole healthcare system to superior outcomes. Conclusion: Recognizing the preventability of CVD through personalized environmental modifications, this review advocates tailored prevention plans that account for individual characteristics. Despite its proven efficacy in managing modifiable risk factors, achieving optimal cardiovascular health remains challenging, necessitating innovative strategies and the integration of artificial intelligence in personalized healthcare. © 2025 Elsevier B.V., All rights reserved.
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    AI-assisted ophthalmic imaging for early detection of neurodegenerative diseases
    (BioMed Central Ltd, 2025) Tukur, Hajar Nasir; Uwishema, Olivier; Akbay, Hatice; Sheikhah, Dalal; Silva Correia, Inês F.; Tukur, Hajar Nasir, Research and Education, Oli Health Magazine Organization, Kigali, Rwanda, Faculty of Medicine, Bahçeşehir Üniversitesi, Istanbul, Turkey; Uwishema, Olivier, Research and Education, Oli Health Magazine Organization, Kigali, Rwanda, Oli Health Magazine Organization, Kigali, Rwanda; Akbay, Hatice, Research and Education, Oli Health Magazine Organization, Kigali, Rwanda, Marmara Üniversitesi Tip Fakültesi, Istanbul, Turkey; Sheikhah, Dalal, Research and Education, Oli Health Magazine Organization, Kigali, Rwanda, Faculty of Medicine, Bahçeşehir Üniversitesi, Istanbul, Turkey; Silva Correia, Inês F., Research and Education, Oli Health Magazine Organization, Kigali, Rwanda, School of Medicine, Chelmsford, United Kingdom
    Background: Artificial intelligence (AI) plays a promising role in ophthalmic imaging by providing innovative, non-invasive tools for the early detection of neurodegenerative diseases such as Alzheimer’s disease (AD) and Parkinson’s disease (PD). Since early diagnosis is crucial for slowing disease progression and improving patient outcomes, leveraging AI-assisted ophthalmic imaging retinal imaging can enhance detection accuracy and clinical decision-making. Methods: This review examines clinical applications of AI in identifying retinal biomarkers associated with neurodegenerative diseases. Relevant data was gathered through a comprehensive literature review using PubMed, ScienceDirect, and Google Scholar to evaluate studies utilizing AI algorithms for retinal imaging analysis, focusing on diagnostic performance, sensitivity, specificity, and clinical relevance. Results: AI-assisted ophthalmic imaging retinal imaging enhances the early identification of neurodegenerative diseases by detecting microscopic structural and vascular changes in the retina. Studies have demonstrated that AI models analyzing Optical Coherence Tomography (OCT) and fundus images achieve high diagnostic accuracy. Studies have reported an area under the curve (AUC) of up to 0.918 in PD detection, with sensitivity ranging from 80 to 100% and specificity up to 85%. Similarly, AI-assisted OCT angiography (OCT-A) analysis has successfully identified retinal vascular alterations in AD patients, correlating with cognitive decline and an AUC of 0.73–0.91. These findings highlight AI’s potential to detect preclinical disease stages before significant neurological symptoms manifest. Discussion: The integration of AI technologies into ophthalmic imaging holds the potential to improve early diagnosis and transform patient outcomes. However, challenges such as model interpretability, dataset biases, and ethical considerations must be addressed to ensure the responsible integration of AI into clinical practice. Future research should focus on refining AI algorithms, integrating multimodal imaging techniques, and developing predictive biomarkers to optimize early intervention strategies for neurodegenerative diseases. Clinical trial number: Not applicable. © 2025 Elsevier B.V., All rights reserved.