The accuracy of intraocular lens power calculation formulas based on artificial intelligence in highly myopic eyes: a systematic review and network meta-analysis

Front Public Health. 2023 Nov 9:11:1279718. doi: 10.3389/fpubh.2023.1279718. eCollection 2023.

Abstract

Objective: To systematically compare and rank the accuracy of AI-based intraocular lens (IOL) power calculation formulas and traditional IOL formulas in highly myopic eyes.

Methods: We screened PubMed, Web of Science, Embase, and Cochrane Library databases for studies published from inception to April 2023. The following outcome data were collected: mean absolute error (MAE), percentage of eyes with a refractive prediction error (PE) within ±0.25, ±0.50, and ±1.00 diopters (D), and median absolute error (MedAE). The network meta-analysis was conducted by R 4.3.0 and STATA 17.0.

Results: Twelve studies involving 2,430 adult myopic eyes (with axial lengths >26.0 mm) that underwent uncomplicated cataract surgery with mono-focal IOL implantation were included. The network meta-analysis of 21 formulas showed that the top three AI-based formulas, as per the surface under the cumulative ranking curve (SUCRA) values, were XGBoost, Hill-RBF, and Kane. The three formulas had the lowest MedAE and were more accurate than traditional vergence formulas, such as SRK/T, Holladay 1, Holladay 2, Haigis, and Hoffer Q regarding MAE, percentage of eyes with PE within ±0.25, ±0.50, and ±1.00 D.

Conclusions: The top AI-based formulas for calculating IOL power in highly myopic eyes were XGBoost, Hill-RBF, and Kane. They were significantly more accurate than traditional vergence formulas and ranked better than formulas with Wang-Koch AL modifications or newer generations of formulas such as Barrett and Olsen.

Systematic review registration: https://www.crd.york.ac.uk/PROSPERO/, identifier: CRD42022335969.

Keywords: artificial intelligence; formulas; high myopia; intraocular lens; prediction error.

Publication types

  • Meta-Analysis
  • Systematic Review
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Artificial Intelligence
  • Humans
  • Lenses, Intraocular*
  • Myopia*
  • Network Meta-Analysis
  • Refraction, Ocular
  • Refractive Errors* / complications
  • Retrospective Studies

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This study was financially supported by the National Key Research and Development Program of China (No. 2020YFC2008205), the National Natural Science Foundation of China (Nos. 82171058 and 81974134), and the Key R&D Plan of Hunan Province of China (No. 2020SK2076).