Dual-Modality Computational Ophthalmic Imaging with Deep Learning and Coaxial Optical Design
It addresses the growing burden of myopia and retinal diseases by providing a promising solution for rapid, intelligent, and scalable ophthalmic screening, particularly in community health settings, though it is incremental due to integration of existing methods.
This study tackled the need for accessible eye screening by developing a compact dual-function device for fundus photography and refractive error detection, achieving high-precision pupil localization (EDE = 2.8 px, mIoU = 0.931) and reliable refractive estimation with a mean absolute error below 5%.
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.