ArticleTranslational vision science & technology2026
Optimizing Field-of-View for Type-1 Retinopathy of Prematurity via Multiple Instance Learning.
Article in Translational vision science & technology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Corrections and comments
- Erratum issued
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: The purpose of this study was to cut down on screening time and costs, this study optimizes field-of-view (FOV) combinations for classifying type-1 retinopathy of prematurity (ROP) through multiple-instance learning (MIL) that models clinical decision making. Methods: The dataset included 1420 photographs of 284 eyes (204 eyes from Chang Gung Memorial Hospital [CGMH] and 80 eyes from Osaka University), each eye containing five FOVs (temporal, nasal, central, superior, and inferior). We evaluated various FOV combinations and compared two MIL fusion strategies, feature-level and outcome-level, to identify the most effective approach. Feature-level learns a joint representation across views, whereas outcome-level aggregates the predicted probabilities from each view at the decision stage. Model performance was evaluated using five-fold cross-validation, with metrics including accuracy, precision, recall, F1-score, and area under the curve (AUC). Results: The feature-level MIL significantly outperformed the outcome-level MIL in all combinations and metrics except the superior view (P < 0.05). Among FOV combinations, the temporal, nasal, and central set achieved the highest performance (accuracy = 0.892 ± 0.043, F1-score = 0.831 ± 0.097). Horizontal (temporal and nasal) and central views individually showed stronger diagnostic power, comparable to multi-view settings, whereas vertical (superior and inferior) views performed significantly worse. Conclusions: The feature-level MIL model effectively simulates clinical decision making for type-1 ROP classification. Temporal, nasal, and central views, as well as their combination, achieved superior performance over vertical views or their combination. Notably, feature-level MIL outperformed outcome-level MIL, underscoring the critical role of information fusion strategy. Translational Relevance: Clinically aligned MIL with optimized FOV selection enables more efficient artificial intelligence (AI)-assisted telemedicine screening for retinopathy of prematurity.
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