Machine learning helps improve diagnostic ability of subclinical keratoconus using Scheimpflug and OCT imaging modalities

Ce Shi,Yufeng Ye,Jun Jiang,Meixiao Shen,Sisi Chen,Tiantian Zhu,Fan Lu,Ying Zhang,Mengyi Wang

doi:10.1186/s40662-020-00213-3

Abstract

PurposeTo develop an automated classification system using a machine learning classifier to distinguish clinically unaffected eyes in patients with keratoconus from a normal control population based on a combination of Scheimpflug camera images and ultra-high-resolution optical coherence tomography (UHR-OCT) imaging data.MethodsA total of 121 eyes from 121 participants were classified by 2 cornea experts into 3 groups: normal (50 eyes), with keratoconus (38 eyes) or with subclinical keratoconus (33 eyes). All eyes were imaged with a Scheimpflug camera and UHR-OCT. Corneal morphological features were extracted from the imaging data. A neural network was used to train a model based on these features to distinguish the eyes with subclinical keratoconus from normal eyes. Fisher’s score was used to rank the differentiable power of each feature. The receiver operating characteristic (ROC) curves were calculated to obtain the area under the ROC curves (AUCs).ResultsThe developed classification model used to combine all features from the Scheimpflug camera and UHR-OCT dramatically improved the differentiable power to discriminate between normal eyes and eyes with subclinical keratoconus (AUC = 0.93). The variation in the thickness profile within each individual in the corneal epithelium extracted from UHR-OCT imaging ranked the highest in differentiating eyes with subclinical keratoconus from normal eyes.ConclusionThe automated classification system using machine learning based on the combination of Scheimpflug camera data and UHR-OCT imaging data showed excellent performance in discriminating eyes with subclinical keratoconus from normal eyes. The epithelial features extracted from the OCT images were the most valuable in the discrimination process. This classification system has the potential to improve the differentiable power of subclinical keratoconus and the efficiency of keratoconus screening.

Highlights

The accurate identification of keratoconus (KC) at its earliest stage is the primary concern in corneal refractive surgery preoperative screening for several reasons
Corneas with undetected KC are known to be highly associated with iatrogenic keratectasia, which is the most severe and irreversible complication after laser in situ keratomileusis (LASIK) [1, 2]
We present an automated classification system using the combination of Scheimpflug camera and ultra-high-resolution optical coherence tomography (UHR-OCT) imaging parameters based on a machine learning classifier to distinguish a population with subclinical keratoconus from a normal control population

Summary

Introduction

The accurate identification of keratoconus (KC) at its earliest stage is the primary concern in corneal refractive surgery preoperative screening for several reasons. New ophthalmic imaging modalities have been applied in the screening of KC at its earliest stage [6, 7]. Among these modalities, Scheimpflug-based camera imaging and spectral domain optical coherence tomography (SD-OCT) have been the most widely studied methods. Scheimpflug-based camera imaging and spectral domain optical coherence tomography (SD-OCT) have been the most widely studied methods Both approaches have provided unique imaging advantages in recognizing early changes in the cornea (e.g., depth information, corneal microstructures, etc.) and have been proven to provide diagnostic value in detecting subclinical KC [5]. In clinical settings, combined machine-derived parameters from these instruments are often too complicated for clinicians to interpret

Methods

Results

Discussion

Conclusion