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Start date: 2020End date: 2025Type: search.filters.itemtype.ReviewSubject: search.filters.subject.HumanAuthor: search.filters.author.Uwishema, OlivierAuthor: search.filters.author.Akbay, Hatice

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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.
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    Neuro-ophthalmology and migraine: visual aura and its neural basis
    (BioMed Central Ltd, 2025) Tukur, Hajar Nasir; Uwishema, Olivier; Sheikhah, Dalal; Akbay, Hatice; 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; Sheikhah, Dalal, Research and Education, Oli Health Magazine Organization, Kigali, Rwanda, Faculty of Medicine, Bahçeşehir Üniversitesi, Istanbul, Turkey; Akbay, Hatice, Research and Education, Oli Health Magazine Organization, Kigali, Rwanda, Faculty of Medicine, Marmara University, Istanbul, Rwanda
    Background: Migraine, a chronic neurological condition often accompanied by visual aura, which affects 15–33% of migraineurs, often presents as transient visual disturbances such as scintillating scotoma and teichopsia. These symptoms arise primarily from cortical spreading depression (CSD) within the occipital cortex and significantly impacts the quality of life, with chronic and episodic migraineurs consistently scoring lower scores in vision-specific quality of life questionnaires. Therefore, this narrative review explores the pathophysiology pertaining to visual aura in migraines, focusing on the role of CSD while evaluating current diagnostic and therapeutic approaches employed. Methods: A narrative literature review was conducted using PubMed and ScienceDirect, focusing on studies published between 2000 and 2025. Search terms related to migraine, visual aura, and cortical spreading depression were used to identify relevant original research, reviews, and clinical studies addressing the pathophysiology, diagnosis, and treatment of migraine-related visual aura. Results: Findings indicate that CSD drove transient visual symptoms by triggering waves of neuronal depolarization and hypoperfusion in the visual cortex. Contemporaneous treatment modalities target the headache phase of migraine, with limited alternatives for aura-specific intervention. Conclusion: Advancement in neuroimaging and genetic research offer promising avenues for early diagnosis alongside focused therapeutics for migraine with aura. However, current treatment strategies remain largely focused on the headache phase, with limited efficacy for aura-specific symptoms. Future therapeutic approaches targeting cortical spreading depression may offer more precise interventions for managing visual aura in migraine. © 2025 Elsevier B.V., All rights reserved.