Scholarlink

This chapter discusses how artificial intelligence (AI) technologies are transforming the diagnosis, management, and treatment of retinal diseases, including diabetic retinopathy (DR), age-related macular degeneration (AMD), retinopathy of prematurity (ROP), and inherited retinal disorders (IRD). It explains how machine learning (ML) and deep learning (DL) algorithms use multimodal imaging data, such as fundus photography, optical coherence tomography (OCT), and autofluorescence imaging, to improve diagnostic accuracy, predict disease progression, and customize treatment. FDA-approved and CE-marked AI systems, such as IDx-DR, EyeArt, and SELENA, are reviewed alongside emerging platforms, including Google ARDA and RetmarkerDR, to emphasize their real-world use in DR screening. The chapter also explores AI’s role in modeling AMD progression, classifying ROP severity, and recognizing genetic patterns in IRDs. By integrating predictive modeling, automated image analysis, and personalized medicine, AI is transforming retinal care through early detection, optimized treatment strategies, and enhanced global access to vision-saving interventions.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Enhancing Diagnosis and Treatment of Retinal Diseases Through Artificial Intelligence

  • 章节

  • 发表日期：

    2026年

查看全文
  • Alejandro Espaillat

摘要

This chapter discusses how artificial intelligence (AI) technologies are transforming the diagnosis, management, and treatment of retinal diseases, including diabetic retinopathy (DR), age-related macular degeneration (AMD), retinopathy of prematurity (ROP), and inherited retinal disorders (IRD). It explains how machine learning (ML) and deep learning (DL) algorithms use multimodal imaging data, such as fundus photography, optical coherence tomography (OCT), and autofluorescence imaging, to improve diagnostic accuracy, predict disease progression, and customize treatment. FDA-approved and CE-marked AI systems, such as IDx-DR, EyeArt, and SELENA, are reviewed alongside emerging platforms, including Google ARDA and RetmarkerDR, to emphasize their real-world use in DR screening. The chapter also explores AI’s role in modeling AMD progression, classifying ROP severity, and recognizing genetic patterns in IRDs. By integrating predictive modeling, automated image analysis, and personalized medicine, AI is transforming retinal care through early detection, optimized treatment strategies, and enhanced global access to vision-saving interventions.