Aktaş, Zeynep

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Profile URL
https://hdl.handle.net/20.500.14411/7265
Name Variants
Aktaş Z.
Z., Aktaş
A., Zeynep
Aktaş,Z.
Aktas Z.
Aktaş, Zeynep
Zeynep, Aktaş
Z.,Aktas
Z.,Aktaş
Aktas, Z.
A.,Zeynep
Zeynep, Aktas
Z., Aktas
Aktas, Zeynep
Aktas,Z.
Job Title
Profesor Doktor
Email Address
zeynep.aktas@atilim.edu.tr
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Surgical Sciences
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Scopus Author ID
20435411000
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WoS Researcher ID
KGW-7180-2024

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Documents

93

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1017

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78

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743

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Scholarly Output

16

Articles

13

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84/0

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WoS Citation Count

74

Scopus Citation Count

98

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WoS Citations per Publication

4.63

Scopus Citations per Publication

6.13

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9

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0

JournalCount
Journal of Glaucoma3
Turkish Journal of Ophthalmology2
Graefe's Archive for Clinical and Experimental Ophthalmology2
Turkish Journal of Clinical and Experimental Ophthalmology2
Frontiers in Ophthalmology1
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Author: Aktaş, Z.Subject: Deep Learning

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    Article
    Revolutionizing Glaucoma Care: Harnessing Artificial Intelligence for Precise Diagnosis and Management
    (Gazi Eye Foundation, 2025) Ucgul, A.Y.; Aktaş, Z.
    Glaucoma is a leading cause of irreversible blindness worldwide, necessitating early detection and effective management to prevent vision loss. Recent advancements in artificial intelligence (AI) have revolutionized glaucoma care by enhancing diagnostic accuracy, monitoring disease progression, and personalizing treatment strategies. AI models, including machine learning and deep learning algorithms, have demonstrated exceptional performance in analyzing fundus photography, optical coherence tomography, and visual field data, surpassing traditional diagnostic methods. Convolutional neural networks have shown high sensitivity and specificity in detecting glaucomatous changes, while vision transformers and hybrid AI models further refine risk assessment and prognosis. Additionally, AI-powered monitoring systems utilizing multi-modal data integration allow for more precise prediction of disease progression and the need for surgical intervention. The incorporation of AI into telemedicine and wearable intraocular pressure sensors extends glaucoma management to remote and underserved populations. Despite these advancements, challenges remain, including issues related to algorithm generalizability, data standardization, bias, and ethical concerns regarding AI-driven clinical decision-making. To maximize AI’s potential in glaucoma care, further interdisciplinary research, regulatory oversight, and multi-center validation studies are needed. By addressing these challenges, AI can be effectively integrated into clinical practice, leading to improved early detection, enhanced treatment strategies, and more personalized patient care. The future of AI in glaucoma management holds great promise, paving the way for a more data-driven and patient-centered approach to combating this sight-threatening disease. © 2024 The author(s).