Code-Free Machine Learning for the Detection of Common Ophthalmic Diseases.

Abstract

Purpose: We explore a code-free method enabling physicians without programming experience to develop machine learning (ML) models for detecting diabetic retinopathy (DR), age-related macular degeneration (AMD), and glaucoma from fundus photographs.

Methods: Two classification models were developed using Google Vertex AI's no-code AutoML Vision platform: a binary model detecting any pathology and a multi-class model classifying specific diseases. The development dataset consisted of 800 fundus photography images (200 each of DR, AMD, glaucoma, and normal) from the publicly available Fundus Image dataset for Vessel Segmentation. Ten percent of the dataset was saved for testing and 10% for internal validation. External validation was performed using the Eye Disease Diagnosis and Fundus Synthesis dataset, from which 100 single-diagnosis images per class were randomly selected (total N = 400). Model performances were evaluated using area under the precision-recall curve (AUPRC), precision, recall, accuracy, F1 score, and confidence score analysis.

Results: Internally, the binary model yielded an AUPRC of 0.967, with 95.0% precision and recall. The multi-class model had an AUPRC of 0.906, with 91.0% precision and 90.0% recall. On external validation, the binary model reached 92.3% accuracy, whereas the multi-class model achieved 90% overall accuracy.

Conclusions: Code-free ML approaches can enable physicians to create ML models for retinal disease detection without requiring programming expertise, supporting early detection of eye diseases.

Translational relevance: This work bridges the gap between AI research and clinical deployment by demonstrating that physicians can independently build ML models using accessible, no-code tools.