A Multi Comparison of 8 Different Intraocular Lens Biometry Formulae, Including a Machine Learning Thin Lens Formula (MM) and an Inbuilt Anterior Segment Optical Coherence Tomography Ray Tracing Formula
Richard N. McNeely, Katherine McGinnity, Stephen Stewart, Emmanuel Eric Pazo, Salissou Moutari, Jonathan E. Moore

TL;DR
This study compares eight formulas for calculating intraocular lens power, finding that the MM and ray tracing methods perform similarly to traditional ones in predicting lens accuracy.
Contribution
The study introduces a machine learning-based thin lens formula (MM) and compares it with traditional and ray tracing-based IOL power calculation methods.
Findings
The MM formula achieved 96% accuracy within ±0.75D for enhanced monofocal IOLs.
Ray tracing showed high accuracy for multifocal IOLs with 90% within ±0.75D.
MM and ray tracing performed similarly to established formulas in terms of accuracy.
Abstract
A comparison of the accuracy of intraocular lens (IOL) power calculation formulae, including SRK/T, HofferQ, Holladay 1, Haigis, MM, Barrett Universal II (BUII), Emmetropia Verifying Optical (EVO), and AS-OCT ray tracing, was performed. One hundred eyes implanted with either the Rayone EMV RAO200E (Rayner Intraocular Lenses Limited, Worthing, UK) or the Artis Symbiose (Cristalens Industrie, Lannion, France) IOL were included. Biometry was obtained using IOLMaster 700 (Carl Zeiss Meditec AG, Jena, Germany) and MS-39 AS-OCT (CSO, Firenze, Italy). Mean (MAE) and median (MedAE) absolute errors and percentage of eyes within ±0.25D, ±0.50D, ±0.75D, and ±1.00D of the target were compared, with ±0.75D considered a key metric. The highest percentage within ±0.75D was found with MM (96%) followed by the Haigis (94%) for the enhanced monofocal IOL. SRK/T (94%) had the highest percentage within…
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TopicsSocial Sciences and Policies
