SpecF2M: A Spectral-Aware Multi-task Network Estimating Axial Length and Refractive Error from Pediatric Fundus Photographs

2026-08-07

AI Summary

This study addresses the challenges in pediatric myopia screening, which relies on invasive examinations that hinder widespread adoption. Fundus images exhibit low-contrast, spatially diffuse, and multiscale features associated with axial length (AL), spherical equivalent (SPH), and cylindrical power (CYL), whose anatomical correlates partially overlap yet remain inconsistent. To tackle this, the authors propose SpecF2M—the first spectrum-aware multitask network incorporating hybrid spatial–frequency domain modeling for pediatric fundus analysis—featuring an anatomy-guided enhancement module, a MixCNN-HSL hybrid backbone, and an expert-routing head to jointly estimate AL, SPH, and CYL. Evaluated on 6,966 pediatric fundus images, the method achieves mean absolute errors of 0.5347 mm for AL and 0.7062 D for SPH, significantly outperforming CNN- and ViT-based baselines, while revealing an asymmetric task coupling wherein CYL exhibits weaker association with myopic fundus patterns.

Abstract

Spherical Equivalent Refraction (SER) and Axial Length (AL) are core indicators for pediatric myopia screening, yet their measurements require dedicated biometry and cycloplegic refraction. Fundus photography offers an accessible imaging modality, as myopia-related posterior-pole changes are visible in 45$^\circ$ fundus images. However, these cues are often low-contrast, spatially diffuse, and multi-scale. Moreover, AL, Sphere (SPH), and Cylinder (CYL) share partially overlapping but non-identical anatomical correlates. We propose SpecF2M, a spectral-aware multi-task network for estimating AL and SER components from pediatric fundus photographs. SpecF2M integrates a deterministic anatomy-guided enhancement module, a hybrid spatial--spectral backbone combining MixCNN and Hybrid Spectral Learning (HSL) blocks, and an expert-routing head for component-level estimation of AL, SPH, and CYL. On a pediatric cohort of 4,359 eligible child visits and 6,966 fundus images, SpecF2M outperforms controlled CNN/ViT baselines for AL and SPH estimation, achieving MAEs of 0.5347 mm and 0.7062 D, respectively. Component-level analysis further reveals asymmetric task coupling, where CYL exhibits weaker association with fundus-derived myopic patterns than AL/SPH. These results support fundus-based, screening-oriented estimation of pediatric myopia indicators, while external validation remains necessary before deployment.

Problem

• Axial Length
• Refractive Error
• Pediatric Myopia
• Fundus Photography
• Spherical Equivalent Refraction

Innovation

• spectral-aware learning
• multi-task network
• fundus photography
• axial length estimation
• pediatric myopia