Improving clinical refractive results of cataract surgery by machine learning.

ABSTRACT:

Aim

To evaluate the potential of the Support Vector Machine Regression model (SVM-RM) and Multilayer Neural Network Ensemble model (MLNN-EM) to improve the intraocular lens (IOL) power calculation for clinical workflow.

Background

Current IOL power calculation methods are limited in their accuracy with the possibility of decreased accuracy especially in eyes with an unusual ocular dimension. In case of an improperly calculated power of the IOL in cataract or refractive lens replacement surgery there is a risk of re-operation or further refractive correction. This may create potential complications and discomfort for the patient.

Methods

A dataset containing information about 2,194 eyes was obtained using data mining process from the Electronic Health Record (EHR) syst

SUBMITTER: Sramka M 

PROVIDER: S-EPMC6611496 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature