Scholarlink

This chapter examines the transformative effect of artificial intelligence (AI) on the diagnosis, surgical planning, and postoperative management of cataracts. It discusses how machine learning (ML) and deep learning (DL) models are integrated into cataract detection, grading, intraocular lens (IOL) power calculation, and optimization of surgical workflows. AI-based platforms such as convolutional neural networks (CNNs), ensemble learning systems, and hybrid predictive algorithms have proven to be more accurate in cataract screening, pediatric cataract diagnosis, and automated referral systems, greatly increasing access to care. Additionally, AI in IOL power calculations—using advanced models like Kane, Hill-RBF, and PEARL-DGS formulas—has improved refractive accuracy and personalized treatment planning. The chapter also highlights the new applications of AI during surgery and after, including recognition of surgical phases, instrument tracking, and predicting posterior capsule opacification. Overall, these innovations emphasize AI’s expanding role in making cataract surgery more precise, efficient, and tailored to individual patients.

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

Using Artificial Intelligence to Revolutionize Diagnosis and Treatment of Cataracts and IOL Calculations

  • 章节

  • 发表日期：

    2026年

查看全文
  • Alejandro Espaillat

摘要

This chapter examines the transformative effect of artificial intelligence (AI) on the diagnosis, surgical planning, and postoperative management of cataracts. It discusses how machine learning (ML) and deep learning (DL) models are integrated into cataract detection, grading, intraocular lens (IOL) power calculation, and optimization of surgical workflows. AI-based platforms such as convolutional neural networks (CNNs), ensemble learning systems, and hybrid predictive algorithms have proven to be more accurate in cataract screening, pediatric cataract diagnosis, and automated referral systems, greatly increasing access to care. Additionally, AI in IOL power calculations—using advanced models like Kane, Hill-RBF, and PEARL-DGS formulas—has improved refractive accuracy and personalized treatment planning. The chapter also highlights the new applications of AI during surgery and after, including recognition of surgical phases, instrument tracking, and predicting posterior capsule opacification. Overall, these innovations emphasize AI’s expanding role in making cataract surgery more precise, efficient, and tailored to individual patients.