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

arXiv: https://arxiv.org/abs/2212.07058 · HTML: https://mlsanity.com/paper/2212.07058/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 48 references that are in the map.
- First posted on arXiv 14 Dec 2022.

## Builds on (most cited first, 4 of 4)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Deep Learning based Computer-Aided Diagnosis Systems for Diabetic Retinopathy: A Survey](https://mlsanity.com/paper/1811.01238/) (2018)
- [Uncertainty aware and explainable diagnosis of retinal disease](https://mlsanity.com/paper/2101.12041/) (2021)

## Abstract (arXiv)

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.