Deep Learning Optimisation Strategies for Uveal Melanoma Detection Using Ultra-Widefield Photography

Objectives: Uveal melanoma (UM) is the most common primary intraocular malignancy in adults and carries significant metastatic risk. Early and accurate diagnosis is essential, but challenging due to overlapping clinical features with benign choroidal nevi. Deep learning (DL) offers potential to support early and accessible detection, but model performance is limited by dataset size and quality. This study evaluates data-centric optimisation strategies for DL classification of UM using ultra-widefield (UWF) fundus photography. Methods: This retrospective study analysed UWF fundus photographs from 784 patients (864 images) seen at the University of Illinois Chicago eye clinic. A baseline binary classification model (UM vs. choroidal nevus) was compared with seven models incorporating optimisation strategies across three categories: class addition, dataset augmentation, and enhanced feature selection. Performance was assessed using AUC, F1 score, precision, and recall, with calibration evaluated via expected calibration error. Results: The baseline model achieved an AUC of 0.906 ± 0.032. The top-performing model incorporated healthy retinal controls as an additional class, achieving an AUC of 0.987 ± 0.011 and F1 scores of 0.966 (nevus) and 0.941 (UM). Dataset augmentation approaches yielded minimal performance gain, and Multi-strategy models showed no additive benefit. Conclusions: Data-centric optimisation significantly influences DL performance for UM detection. Three principles emerge: healthy class addition improves specificity and anatomical feature learning; data quality may outweigh quantity; and contextual input tuning is a key model parameter. These findings offer a practical framework for developing clinically robust, physician-supervised AI tools to support early UM triage and reduce diagnostic variability.