Applications of Artificial Intelligence in Cataract Surgery: A Review

Area of Science:

• Ophthalmology
• Medical Artificial Intelligence
• Microsurgery

Background:

• Cataract surgery is a prevalent global procedure, with increasing incidence due to an aging population.
• Artificial intelligence (AI) is rapidly advancing and being integrated into various medical fields, including ophthalmology.

Purpose of the Study:

• To review the current applications of AI in ophthalmic microsurgery, specifically focusing on cataract surgery.
• To identify the mechanisms and stages of cataract surgery where AI is being utilized.
• To explore the challenges and future potential of AI in this surgical domain.

Main Methods:

• A comprehensive literature search was conducted on PubMed and Google Scholar.
• Keywords included "artificial intelligence," "AI," "machine learning," "deep learning," "convolutional neural networks," and "cataract surgery."
• Relevant articles published since 2010 were included in the review.

Main Results:

• AI, including machine learning (ML), deep learning (DL), and convolutional neural networks (CNN), is applied across pre-operative, intraoperative, and post-operative phases.
• Pre-operative applications include intraocular lens (IOL) power calculation and cataract diagnosis using imaging.
• Intraoperative uses involve risk assessment, workflow tracking, and instrument localization with smart instruments.
• Post-operative applications focus on predicting complications like posterior capsular opacification and managing patient follow-up.

Conclusions:

• Current AI integration aids in IOL calculation, diagnosis, surgical workflow, and complication prediction.
• Challenges include limited datasets, unstandardized metrics, and generalizability issues for AI models.
• Future research addressing these barriers can improve AI's role in cataract screening, physician training, and complication identification.