Aims To create and assess the performance of an artificial intelligence-based image analysis tool for the measurement and quantification of the corneal neovascularisation (CoNV) area.
Methods Slit lamp images of patients with CoNV were exported from the electronic medical records and included in the study. An experienced ophthalmologist made manual annotations of the CoNV areas, which were then used to create, train and evaluate an automated image analysis tool that uses deep learning to segment and detect CoNV areas. A pretrained neural network (U-Net) was used and fine-tuned on the annotated images. Sixfold cross-validation was used to evaluate the performance of the algorithm on each subset of 20 images. The main metric for our evaluation was intersection over union (IoU).
Results The slit lamp images of 120 eyes of 120 patients with CoNV were included in the analysis. Detections of the total corneal area achieved IoU between 90.0% and 95.5% in each fold and those of the non-vascularised area achieved IoU between 76.6% and 82.2%. The specificity for the detection was between 96.4% and 98.6% for the total corneal area and 96.6% and 98.0% for the non-vascularised area.
Conclusion The proposed algorithm showed a high accuracy compared with the measurement made by an ophthalmologist. The study suggests that an automated tool using artificial intelligence may be used for the calculation of the CoNV area from the slit-lamp images of patients with CoNV.
- Diagnostic tests/Investigation
- Ocular surface
Data availability statement
Data are available on reasonable request.
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BM and TS contributed equally.
Contributors BM had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis, BM is the guarantor of the study. BM and TS contributed equally to this paper and have thus joint first authorship. All authors meet the criteria for authorship. Data collection: BM and TS. Analysis and interpretation of the data: MN, GB and JA. Writing and revising the manuscript: BM, TS, MN, LD, JA and BS. Supervision: LD, JA and BS.
Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.
Competing interests None declared.
Provenance and peer review Not commissioned; externally peer reviewed.
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