Explainable Artificial Intelligence in Retinal Imaging for the detection of Systemic Diseases
This addresses the need for explainable AI in healthcare diagnostics, particularly for clinicians interpreting retinal images, though it is incremental by building on existing image processing methods.
The paper tackled the problem of opaque AI decisions in retinal imaging for systemic disease detection by proposing a clinician-in-the-loop workflow using retinal vessel parameters, achieving improved transparency without deep CNNs.
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.