# Dual-Modality Computational Ophthalmic Imaging with Deep Learning and Coaxial Optical Design

arXiv: https://arxiv.org/abs/2504.18549 · HTML: https://mlsanity.com/paper/2504.18549/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
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- First posted on arXiv 13 Apr 2025.

## Abstract (arXiv)

The growing burden of myopia and retinal diseases necessitates more accessible and efficient eye screening solutions. This study presents a compact, dual-function optical device that integrates fundus photography and refractive error detection into a unified platform. The system features a coaxial optical design using dichroic mirrors to separate wavelength-dependent imaging paths, enabling simultaneous alignment of fundus and refraction modules. A Dense-U-Net-based algorithm with customized loss functions is employed for accurate pupil segmentation, facilitating automated alignment and focusing. Experimental evaluations demonstrate the system's capability to achieve high-precision pupil localization (EDE = 2.8 px, mIoU = 0.931) and reliable refractive estimation with a mean absolute error below 5%. Despite limitations due to commercial lens components, the proposed framework offers a promising solution for rapid, intelligent, and scalable ophthalmic screening, particularly suitable for community health settings.