Article Text

Download PDFPDF

OP-3 The effectiveness of artificial intelligence in annotating and measuring corneal pathology on OCT
  1. Colby Hart1,
  2. Xu Chen1,
  3. Mahmoud Ahmed1,
  4. Keri McLean1,
  5. Tobi Somerville1,
  6. Adela Hulpus1,
  7. Gibran Butt2,
  8. Giulia Coco1,
  9. Vito Romano1,
  10. Saaeha Rauz2,
  11. Sam Zhao1,
  12. Yalin Zheng1,
  13. Stephen Kaye1
  1. 1University of Liverpool, Liverpool, UK
  2. 2University of Birmingham, Edgbaston, UK


*Correspondence, Colby Hart:

Objective To determine if corneal OCT images can be characterised and measured using artificial intelligence (AI) and how this compares to manual subjective assessment.

Methods Phase one. Casia OCT images of patients with corneal disease were included from Birmingham and Liverpool. Individual images were annotated by expert clinicians after concordance training sessions. Two annotations were made: high and low confidence lesion borders. Images were split into training and testing sets. Training data were used to train a DeepLabV3 deep learning model. Testing sets were used to evaluate performance. Lesions were independently evaluated by three masked experts. Phase two. OCT images from patients with microbial keratitis (MK) on days 0, 3, 7 and 28 were annotated by AI after training on normal corneal OCTs. Nonparametric analysis was undertaken using SPSS v25.

Results Phase one. 456 images from patients with primary cornea disease were used to train the AI model and 43 were used for testing the model. Comparing manual and automated annotation, there was a significant difference between expert clinicians (p=0.03, p=0.001) in deciding whether the AI or subjective annotation was a better representation. This may reflect the variety of lesions included. Phase two. Images of 102 patients with MK were selected from days 0, 3, 7 and 28 and subjected to automated annotation. Data analysis on AI annotation of improvement in MK is due March 2022.

Conclusion The usefulness of AI for annotating corneal OCT lesions depends on the homogeneity and quality of the image. OCT systems which provide higher resolution images enable better automated annotation.

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: .

Statistics from

Request Permissions

If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.