# FunduScope: a human-centered, machine learning–based interactive tool for training junior ophthalmologists in diabetic retinopathy detection

## TL;DR

FunduScope is an ML-based interactive tool designed to train junior ophthalmologists in detecting diabetic retinopathy by addressing current training limitations.

## Contribution

The novel contribution is a human-centered ML-based learning tool for diabetic retinopathy training with insights from junior doctors' needs and experiences.

## Key findings

- FunduScope addresses the lack of variability in training materials and time constraints of experienced ophthalmologists.
- The tool showed potential in reducing cognitive load and improving usability for junior doctors.
- Future enhancements include integrating XAI to support clinical decision-making and expanding to other eye diseases.

## Abstract

Interpreting fundus images is an essential skill for detecting eye diseases, such as diabetic retinopathy (DR), one of the leading causes of visual impairment. However, the training of junior doctors relies on experienced ophthalmologists, who often lack the time for teaching, or on printed training materials that lack variability in examples. In this work, we present FunduScope, an interactive human-centered learning tool for training junior ophthalmologists, which is based on a pre-trained ML model for classifying DR. In a qualitative pre-study, we investigated the needs of junior doctors and identified gaps in recent learning procedures. In the main mixed-methods study, we examined the experience of 10 junior doctors with the tool and its impact on cognitive load, usability, and additional factors relevant to e-learning tools. Despite technical constraints our results confirm the potential of using an ML-based learning tool in medical education, addressing the time constraints of ophthalmologists, and providing learning independence for junior doctors. However, future work could extend the learning tool by using explainable artificial intelligence (XAI) to further support the clinical decision making of learners and exceeding the scope of this proof of concept to other ophthalmic diseases.