ArticleBMC ophthalmology2025
Comparing IOL refraction prediction accuracy and A-constant optimization for cataract surgery patients across South Indian and Midwestern United States populations.
Article in BMC ophthalmology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
backgroundIOL power selection is a key determinant of refractive outcomes after cataract surgery. Numerous formulas exist to aid in this process; some are derived from geometric-optical principles (e.g., SRK/T, Barrett) while others are based on data-driven and machine learning approaches (e.g., Nallasamy, Pearl-DGS). Given differences in ocular biometry and environmental stimuli, population-specific factors may impact the generalizability of certain formulas. This study compares clinical and biometric characteristics and evaluates the prediction accuracy of seven IOL power formulas, including machine learning–based approaches, in two distinct cataract surgery populations from South India and the Midwestern United States.
methodsIn this retrospective cross-sectional comparative study, data were collected from two tertiary care eye centers: University of Michigan’s Kellogg Eye Center (Ann Arbor, MI, USA) and Aravind Eye Hospital (Chennai, Tamil Nadu, India). The dataset included demographics, biometry power of the surgically implanted intraocular lens (IOL), and 1-month postoperative refraction. Seven IOL formulas were applied to predict postoperative refraction, and performance was assessed by comparing mean absolute errors both before and after population-specific A-constant optimization.
resultsA total of 985 eyes from Aravind (mean age 60.5 ± 9.5 years) and 1003 from UMich (mean age 70.7 ± 9.5) were analyzed. Aravind patients had significantly lower age, axial length, lens thickness, and central corneal thickness, while UMich patients had lower K measurements, IOL power, and postoperative refraction. Overall, formulas performed better in Aravind for the SN60WF lens. Before A-constant optimization on the Aravind dataset, one formula (Nallasamy) achieved mean absolute error under 0.25 diopters compared to four formulas (Nallasamy, Pearl-DGS, SRK/T, Barrett) afterwards.
conclusionsSubstantial clinical and biometric differences exist between South Indian and Midwestern US cataract populations. Machine learning-based IOL refraction prediction formulas performed the best on the South Indian dataset both before and after population-specific parameter optimization. Understanding population level differences and creating methods to integrate these factors into IOL formulas may help improve refractive outcomes in cataract surgery.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.