# Intraocular lens calculation formula developed using artificial intelligence for ultrasonic biometry

## TL;DR

An AI program was developed to calculate intraocular lenses using ultrasonic biometry, showing better accuracy than traditional methods.

## Contribution

A novel AI-based intraocular lens calculation formula that outperforms the Barrett Universal II formula in accuracy.

## Key findings

- The AI program achieved 86% and 87.5% accuracy for basic and advanced models, respectively.
- The Barrett Universal II formula had a significantly lower success rate of 69%.
- The AI system performed better for medium and long eyes but worse for short eyes.

## Abstract

We developed an artificial intelligence program for calculating intraocular
lenses and analyzed its accuracy rate via ultrasonic biometry. This endeavor
is aimed at enhancing precision and efficacy in the selection of intraocular
lenses, particularly in cases where optical biometry is unavailable.

Data was collected from the Hospital de Clínicas de Porto
Alegre, which included cases of phacoemulsification with
intraocular lens implantation, in which the lens selection was based on
ultrasonic biometry. The program, implemented in Python, Java, and PHP,
employs the ridge regression method. Two design options were developed: a
basic model, which uses only keratometry variables (K1 and K2), axial size
and final target refraction in the spherical equivalent, and an advanced
model, which incorporates preoperative refraction and the patient’s age. The
Universal Barrett II formula was used to compare both models.

The sample consisted of 486 eyes from 313 patients, with 350 eyes used for
program training and 136 for program validation. The spherical equivalent
hit rates, with a variation of ±0.5 D, were 86% and 87.5% for the
basic and advanced models, respectively, with no statistically significant
difference between them. With the Barret Universal II formula, the success
rate was 69%, which was significantly different from the values of the two
aforementioned models (p<0.0001). The system was better for medium and
long eyes but worse for short eyes (<=22.00 mm).

The developed artificial intelligence program was superior to the Barrett
formula in terms of performance, in the general context and within the
subgroup of patients with longer eyes. This innovation can considerably
contribute to the selection of intraocular lenses, particularly in cases
where optical biometry is unavailable.