Abstract
Objectives
To analyse the accuracy of artificial intelligence (AI)-driven intraocular (IOL) calculation formulae, together with established formulae using the heteroscedastic methodology and the Eyetemis Analysis Tool.
Methods
Data from 404 eyes who underwent uneventful phacoemulsification with implantation of the SN60WF IOL were retrospectively reviewed. IOL power calculations were performed using the Barrett Universal II (BUII), EVO 2.0, Hoffer QST, K6, Ladas Super Formula (LSF), Nallasamy, PEARL-DGS and RBF 3.0 formulae. The SD of the prediction error (PE), served as the primary metric for accuracy. The mean absolute deviation (MAD) and the predictability rates within intervals from ±0.25 D to ±1.50 D were also evaluated. The Eyetemis Analysis Tool was used for further validation.
Results
The SD ranged from 0.468 (Nallasamy) to 0.510 (LSF). The Nallasamy formula had a significantly lower SD than the BUII (0.505, p = 0.025) and K6 (0.489, p = 0.022) formulae. The Nallasamy formula also exhibited the lowest MAD (0.358) with a significant difference compared with the Hoffer QST formula (0.384, p < 0.001). Finally, a significantly higher percentage of eyes achieving ± 0.50 D of the target refraction was seen using the Nallasamy formula (77.19%) compared with the Hoffer QST (71.04%, p = 0.019) and Ladas Super Formula (70.79%, p = 0.030) formulae.
Conclusions
The Nallasamy formula, incorporating AI technology, demonstrated superior accuracy according to the analysis guidelines for PE statistics for non-gaussian datasets recommended by Holladay et al. and the online Eyetemis Analysis Tool.
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Data availability
The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.
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Acknowledgements
We thank Prof. Rand R. Wilcox for his professional guidance with data analysis.
Funding
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
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Conceptualization and Design: OR, RS. Data Collection: OR, NH, KD. Data Analysis and Interpretation: OR, RS. Statistical Analysis: OR. Manuscript Drafting: OR. Final Approval of Manuscript: OR, NH, KD, JHP, RJO, EL, AAB, IB, RS. Supervision: JHP, RJO, EL, AAB, IB, RS. Project Administration: JHP, RJO, AAB, RS.
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The authors declare that they have no conflict of interest. All authors do not have a financial or proprietary interest in any material or method mentioned in this work. R.O. is on the Board of Directors of Perceive Bio and the Scientific Advisory Board of Perfect Lens. J.P. is supported in part by Research to Prevent Blindness Institutional Grant and reports consulting fees from Lensar and Oertli, outside the submitted work.
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This study was performed as part of the requirements toward a Doctor of Medicine degree for Noa Heifetz at the Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
Supplementary information
41433_2024_3365_MOESM1_ESM.docx
Statistical comparison of the SD values of the prediction errors of the formulae according to the heteroscedastic method
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Reitblat, O., Heifetz, N., Durnford, K. et al. Accuracy assessment of artificial intelligence IOL calculation formulae: utilizing the heteroscedastic statistics and the Eyetemis Analysis Tool. Eye 38, 3578–3585 (2024). https://doi.org/10.1038/s41433-024-03365-x
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DOI: https://doi.org/10.1038/s41433-024-03365-x
