ESCRS - POCOR338 - Electrical Impedance Profiling Of Corneas With Keratocunus And Post - Penetrating Keratoplasty Using A Multifrecuency Prototype Device

Electrical Impedance Profiling Of Corneas With Keratocunus And Post - Penetrating Keratoplasty Using A Multifrecuency Prototype Device

Published 2026 - 44th Congress of the ESCRS

Reference: POCOR338 | Type: Poster | DOI: 10.82333/wqt9-wr45

Authors: Ms Martina Chau* 1 , Mr Ivo Rudzinski 2 , Mr Rodrigo M Torres 3 , Mr Mario J Saravia 4

1CORNEA,FUNDACION RETINA,BUENOS AIRES,Argentina, 2Buenos Aires Macula,BUENOS AIRES,Argentina, 3ROMAT CREATOR CENTER,PARANA, ENTRE RIOS,Argentina;BAMTech Devices,BUENOS AIRES,Argentina, 4BAMTech Devices,BUENOS AIRES,Argentina;Buenos Aires Macula,BUENOS AIRES,Argentina

Purpose

To evaluate whether a multifrequency electrical impedance device can discriminate between the

corneal impedance profiles of eyes with keratoconus and those after penetrating keratoplasty (PK).

Setting

Keratoconus is a condition in which the cornea becomes thinner and weaker,

while penetrating keratoplasty (corneal transplant) replaces the damaged cornea with donor tissue. Although 

hese conditions look very different clinically, current diagnostic tools do not directly measure the electrical

properties of the cornea, which may also change with disease.

 

Methods

A pilot experimental study was conducted in volunteer patients using the second-generation

prototype Impel, a multifrequency impedance system equipped with optimized corneal-contact electrodes.

Stable impedance measurements were obtained across a frequency range of 500 Hz to 64 kHz.

Four eyes with clinically diagnosed keratoconus and three eyes with stable 

penetrating keratoplasty performed for keratoconus were evaluated.

Raw impedance data were normalized across frequencies and processed

using principal component analysis (PCA) for dimensionality reduction. Supervised machine-learning

classifiers, including Linear Discriminant Analysis (LDA), were trained using 5-fold cross-validation to assess

discrimination between groups.

Results

Keratoconus and PK corneas exhibited distinct impedance behaviors across frequencies (Figure 1).

Differences became more apparent in the phase-space representation (Figure 2), where keratoconus traces

demonstrated wider dispersion patterns compared with the more compact trajectories of PK grafts. Preliminary

machine-learning classification showed separability between groups, consistent with the visual cluster patterns

observed.

Conclusions

The Impel prototype was able to detect differential corneal impedance signatures between

keratoconus and post-PK eyes in this pilot evaluation. These findings support the feasibility of impedance-

based corneal characterization. Larger, prospective clinical studies are required to validate these preliminary

results.