  Explainable Artificial Intelligence (AI) in the form of an interpretable and
semiautomatic approach to stage grading ocular pathologies such as Diabetic
retinopathy, Hypertensive retinopathy, and other retinopathies on the backdrop
of major systemic diseases. The experimental study aims to evaluate an
explainable staged grading process without using deep Convolutional Neural
Networks (CNNs) directly. Many current CNN-based deep neural networks used for
diagnosing retinal disorders might have appreciable performance but fail to
pinpoint the basis driving their decisions. To improve these decisions'
transparency, we have proposed a clinician-in-the-loop assisted intelligent
workflow that performs a retinal vascular assessment on the fundus images to
derive quantifiable and descriptive parameters. The retinal vessel parameters
meta-data serve as hyper-parameters for better interpretation and
explainability of decisions. The semiautomatic methodology aims to have a
federated approach to AI in healthcare applications with more inputs and
interpretations from clinicians. The baseline process involved in the machine
learning pipeline through image processing techniques for optic disc detection,
vessel segmentation, and arteriole/venule identification.
