An AI-Based Decision Support System for Quality Control Applied to the Use Case Donor Cornea

In recent years, more and more AI models and algorithms get used in previously uncharted domains. The medical domain is one of them and already shows a significant usage of AI methods, for example computer vision algorithms for the analysis of medical imagery. The field of ophthalmology studies medical conditions relating to the eye. One of those conditions, Cornea guttata, can be identified by analysing post mortem microscope images of the donor's cornea endothelium, which needs to be done manually by a skilled professional. To help facilitate this analysis, this paper proposes a hybrid Decision Support System that combines computer vision methods and AI classifiers to guide the decision of the clinicians. By conducting a UX-driven study with professionals from an eye bank, we show that our Decision Support System is able to help users with the classification of Cornea guttata in microscope images. Moreover, the system was able to boost the agreement between two professionals classifying the same cases. The implemented classifiers showed a higher performance compared to the human baseline and the combination of human expertise and AI classifiers detected most of the guttata cases.