Oftalmolojide Yapay Zeka Uygulamaları
Özet
Yapay zeka (YZ), makine öğrenimi (MÖ) ve derin öğrenme (DÖ) teknolojileri, modern tıp ve oftalmoloji uygulamalarında devrimsel bir dönüşüm yaratmaktadır. Tıbbi görüntüleme verilerinin yoğunluğu nedeniyle özellikle DÖ, fundus fotoğrafları ve optik koherens tomografi (OCT) gibi oküler görüntüleme yöntemlerinde güçlü bir tanısal performans sergilemektedir. Bu sistemler; diyabetik retinopati (DR), yaşa bağlı makula dejenerasyonu (SMD), glokom ve prematüre retinopatisi (ROP) gibi küresel körlük nedenlerinin erken teşhisinde, takibinde ve teletıp entegrasyonuyla tarama programlarında etkin şekilde kullanılmaktadır. Ayrıca katarakt sınıflandırması, göz içi lens gücü hesaplamaları ve yüksek miyopi ilerlemesinin tahmininde de YZ algoritmalarından yararlanılmaktadır. Yapay zekanın klinisyenlerin iş yükünü azaltma, tanı hatalarını en aza indirme ve yeni biyobelirteçler keşfetme gibi önemli avantajları bulunmaktadır. Buna karşın, eğitim veri setlerinin homojenliği, nadir hastalıklara ait veri kısıtlılığı, algoritmaların "kara kutu" doğasından kaynaklanan güven ve açıklanabilirliğe dair endişeler ile adli tıp sorunları klinik uygulamada aşılması gereken temel zorlukları oluşturmaktadır. Sonuç olarak YZ, hekimlerin yerini almak için değil, teşhis doğruluğunu artıran ve süreçleri hızlandıran destekleyici bir araç olarak geleceğin oftalmoloji pratiğine yön vermektedir.
Artificial intelligence (AI), machine learning (ML), and deep learning (DL) technologies are driving a revolutionary transformation in modern medicine and ophthalmology practices. Due to the high volume and complexity of medical imaging data, DL in particular demonstrates strong diagnostic performance in ocular imaging modalities such as fundus photography and optical coherence tomography (OCT). These systems are effectively utilized in the early detection, monitoring, and screening programs via telemedicine integration for global causes of blindness, including diabetic retinopathy (DR), age-related macular degeneration (AMD), glaucoma, and retinopathy of prematurity (ROP). Furthermore, AI algorithms are leveraged in cataract classification, intraocular lens power calculations, and predicting high myopia progression. Artificial intelligence offers significant advantages, such as reducing clinicians' workloads, minimizing diagnostic errors, and enabling the discovery of new biomarkers. Conversely, key challenges that must be overcome in clinical practice include the homogeneity of training datasets, limited availability of data for rare diseases, concerns regarding trust and explainability stemming from the "black box" nature of algorithms, and forensic legal issues. In conclusion, rather than replacing physicians, AI shapes the future of ophthalmology practice as a supportive tool that enhances diagnostic accuracy and accelerates processes.
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