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Explainable Artificial Intelligence (AI) in the form of an interpretable andsemiautomatic approach to stage grading ocular pathologies such as Diabeticretinopathy, Hypertensive retinopathy, and other retinopathies on the backdropof major systemic diseases. The experimental study aims to evaluate anexplainable staged grading process without using deep Convolutional NeuralNetworks (CNNs) directly. Many current CNN-based deep neural networks used fordiagnosing retinal disorders might have appreciable performance but fail topinpoint the basis driving their decisions. To improve these decisions'transparency, we have proposed a clinician-in-the-loop assisted intelligentworkflow that performs a retinal vascular assessment on the fundus images toderive quantifiable and descriptive parameters. The retinal vessel parametersmeta-data serve as hyper-parameters for better interpretation andexplainability of decisions. The semiautomatic methodology aims to have afederated approach to AI in healthcare applications with more inputs andinterpretations from clinicians. The baseline process involved in the machinelearning pipeline through image processing techniques for optic disc detection,vessel segmentation, and arteriole/venule identification.

Explainable Artificial Intelligence in Retinal Imaging for the detection of Systemic Diseases