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

TL;DR

This paper presents an explainable, semi-automatic AI approach for grading retinal diseases related to systemic conditions, emphasizing transparency and clinician involvement over deep learning models.

Contribution

It introduces a clinician-in-the-loop workflow using retinal vessel parameters for interpretable disease staging without relying on deep CNNs.

Findings

Effective retinal vascular assessment for disease staging

Enhanced interpretability through meta-data parameters

Potential for federated, clinician-assisted AI workflows

Abstract

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.