OCTA is a non-invasive imaging technique enhancing traditional OCT technology. It is vital in visualizing the retinal and choroidal vasculature without contrast dyes or invasive procedures. OCTA distinguishes itself by detecting red blood cell movement within vessels to map retinal and choroidal blood flow. The resulting 3D image offers a comprehensive view of microvasculature, invaluable for diagnosing and managing various ocular diseases.
Published in Chapter:
Computer-Aided Diagnosis in Ophthalmology: A Technical Review of Deep Learning Applications
Copyright: © 2024
|Pages: 24
DOI: 10.4018/979-8-3693-3661-8.ch006
Abstract
This chapter explores the growing applications of deep learning (DL) in the field of ophthalmology. Specifically, it examines the integration and efficacy of DL systems in enhancing patient outcomes, particularly in the diagnosis and management of conditions such as diabetic retinopathy, age-related macular degeneration, and retinopathy of prematurity. It also outlines how DL algorithms are employed to analyze complex datasets and retinal images, enabling early detection, precise diagnosis, and effective treatment strategies. This chapter also addresses the challenges inherent in integrating AI into clinical practice, including issues related to data bias, algorithmic reliability, ethical concerns, and the need for diverse, representative datasets. It proposes a roadmap for the responsible implementation of DL in ophthalmology, emphasizing the importance of continuous research, development, and ethical considerations. Overall, this chapter presents a vision where these technologies not only enhance clinical practice but also promote improved health outcomes in the field of eye care.