# Novel quantitative indicators of digital ophthalmoscopy image quality

## TL;DR

This paper proposes and evaluates quantitative image quality metrics for digital ophthalmoscopy, aiming to improve teleophthalmology by automatically assessing image suitability for diagnosis.

## Contribution

It introduces and tests statistical, gradient-based, and wavelet transform features for image quality assessment in ophthalmoscopy, demonstrating their potential with machine learning on a small dataset.

## Key findings

- Features can distinguish unsharp images
- Machine learning confirms feature suitability
- Highlights need for larger, diverse datasets

## Abstract

With the advent of smartphone indirect ophthalmoscopy, teleophthalmology - the use of specialist ophthalmology assets at a distance from the patient - has experienced a breakthrough, promising enormous benefits especially for healthcare in distant, inaccessible or opthalmologically underserved areas, where specialists are either unavailable or too few in number. However, accurate teleophthalmology requires high-quality ophthalmoscopic imagery. This paper considers three feature families - statistical metrics, gradient-based metrics and wavelet transform coefficient derived indicators - as possible metrics to identify unsharp or blurry images. By using standard machine learning techniques, the suitability of these features for image quality assessment is confirmed, albeit on a rather small data set. With the increased availability and decreasing cost of digital ophthalmoscopy on one hand and the increased prevalence of diabetic retinopathy worldwide on the other, creating tools that can determine whether an image is likely to be diagnostically suitable can play a significant role in accelerating and streamlining the teleophthalmology process. This paper highlights the need for more research in this area, including the compilation of a diverse database of ophthalmoscopic imagery, annotated with quality markers, to train the Point of Acquisition error detection algorithms of the future.