Improvement of multiple generations of intraocular lens calculation formulae with a novel approach using artificial intelligence

John Ladas, Donna Ladas, Shawn R. Lin, Uday Devgan, Aazim A. Siddiqui, Albert S. Jun

Research output: Contribution to journalArticlepeer-review

Abstract

Purpose: Cataract surgery is the most common eye surgery. Appropriate optimization of intraocular lens (IOL) calculation formulae can result in improved patient outcomes. The purpose of this article is to describe a methodology of optimizing existing IOL formulae and develop hybrid formulae based on artificial intelligence (AI). Methods: Preoperative biometric and postoperative outcomes data were obtained from medical records at a single institution. A numeric computing environment was used to analyze these data and refine IOL formulae using supervised learning AI. The mean absolute error of each IOL formulae with and without AI enhancement was deter-mined, as well as the number of eyes within 0.5 diopter of the predicted refraction. Results: AI algorithms improved the mean absolute error as well as number of eyes within 0.5 diopters of predicted refraction for each of the formulae tested (P < 0.05). Conclusions: A novel methodology is described that uses AI to improve existing IOL formulae. This methodology has the potential to improve clinical outcomes for cataract surgery patients. Translational Relevance: Artificial intelligence can be used to improve existing IOL formulae.

Original languageEnglish (US)
Article number7
JournalTranslational Vision Science and Technology
Volume10
Issue number3
DOIs
StatePublished - 2021

Keywords

  • Artificial intelligence
  • Cataract refractive outcomes
  • Cataract surgery
  • IOL power calculation
  • Machine learning

ASJC Scopus subject areas

  • Biomedical Engineering
  • Ophthalmology

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