Quality Control in the Corneal Bank with Artificial Intelligence: Comparison of a New Deep Learning-based Approach with Conventional Endothelial Cell Counting by the “Rhine-Tec Endothelial Analysis System”

Abstract

Endothelial cell density (ECD) is a crucial parameter for the release of corneal grafts for transplantation. The Lions Eye Bank of Baden-Württemberg uses the “Rhine-Tec Endothelial Analysis System” for ECD quantification, which is based on a fixed counting frame method considering only a small sample of 15 to 40 endothelial cells. The measurement result therefore depends on the frame placement and manual correction of the cells counted within the frame. To increase the sample size and create higher objectivity, we developed a new method based on “deep learning” that automatically detects all visible endothelial cells in the image. This study aims to compare this new method with the conventional Rhine-Tec system. 9375 archived phase-contrast microscopic images of consecutive grafts from the Lions Eye Bank were evaluated with the deep learning method and compared with the corresponding archived analyses of the Rhine-Tec system. Means, Bland-Altman and correlation analyses were compared. Comparable results were obtained for both methods. The mean difference between the Rhine-Tec system and the deep learning method was only − 23 cells/mm² (95% confidence interval − 29 to − 17). There was a statistically significant positive correlation between the two methods, with a correlation coefficient of 0.748. What was striking in the Bland-Altman analysis were clustered deviations in the cell density range between 2000 and 2500 cells/mm² – with higher values in the Rhine-Tec system. The comparable results for cell density measurement values underline the validity of the deep learning-based method. The deviations around the formal threshold for graft release of 2000 cells/mm² are most likely explained by the higher objectivity of the deep learning method and the fact that measurement frames and manual corrections were specifically selected to reach the formal threshold of 2000 cells/mm² when the full area endothelial quality was good. This full area assessment of the graft endothelium cannot currently be replaced by deep learning methods and remains the most important basis for graft release for keratoplasty.

Fazitbox

Bereits bekannt:

• Die Bestimmung der Endothelzelldichte ist entscheidend für die Qualitätskontrolle von Hornhauttransplantaten.

• Aktuell wird hierfür häufig das halbautomatische Rhine-Tec-System eingesetzt.

• Dieses basiert auf einem festen Zählrahmen mit nur 15 – 40 Zellen.

Neu beschrieben:

• Es wurde eine vollautomatische Deep-Learning-Methode zur Endothelzelldichtemessung entwickelt.

• Die Studie zeigt eine gute Korrelation dieser Methode mit dem Rhine-Tec-System.

• Die neue Methode könnte objektiver sein, ersetzt aber nicht die ärztliche Gesamtbeurteilung.

Conclusion Box

Already known:

• Measuring endothelial cell density plays a key role in quality control at cornea banks

• The semi-automatic Rhine-Tec system is currently often used for the purpose

• This method uses a fixed-frame system that only includes fifteen to forty cells

New:

• A fully automated deep learning method for measuring endothelial cell density has been developed

• The study shows a high correlation between this method and the Rhine-Tec system

• The novel method could be more objective, but does not replace overall assessment by a physician

Keywords

corneal transplantation - deep learning - corneal endothelium - corneal bank - fully automated

Publication History

Received: 29 November 2023

Accepted: 01 April 2024

Accepted Manuscript online:
04 April 2024

Article published online:
28 June 2024

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