Development and Validation of a Smartphone-Attached Slit-Lamp Microscope and Fundoscopy Device for Integrated Digital Ophthalmic Imaging (Smart Eye Camera)

2025 0 citations

Abstract

<title>Abstract</title> Background Smartphone-based ophthalmic imaging is gaining importance, but most devices assess only anterior or posterior segments. The Smart Eye Camera (SEC) is a novel smartphone-attached device capable of evaluating both, with potential for AI-assisted diagnosis and telemedicine. This study investigated its safety, diagnostic accuracy, and clinical feasibility compared with conventional slit-lamp and fundus imaging. Methods In this prospective, single-center study, 40 participants (80 eyes) across four age groups were enrolled. SEC findings were compared with conventional devices. Safety was assessed by corneal fluorescein and lissamine green staining, central corneal thickness, intraocular pressure, ganglion cell complex, and critical flicker fusion frequency. Diagnostic accuracy was evaluated for tear film breakup time, meibomian gland dysfunction, hyperemia, cup-to-disc ratio, and glaucoma classifications. Results One participant was excluded. No significant safety changes or adverse events were observed. Diagnostic measurements obtained with SEC showed strong correlations (r = 0.72–1.00) and high agreement (κ = 0.63–1.00) with conventional devices. Examination time was significantly shorter with SEC (20–61 vs 32–143 seconds, P &lt; 0.01). Conclusions SEC demonstrated safety, diagnostic accuracy, and time efficiency comparable to conventional devices. Its portability and smartphone integration support applications in teleophthalmology and AI-assisted diagnostics. Larger multicenter studies are warranted.

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Year
2025
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Eisuke Shimizu, Shigeto Shimmura, Yoko Ogawa et al. (2025). Development and Validation of a Smartphone-Attached Slit-Lamp Microscope and Fundoscopy Device for Integrated Digital Ophthalmic Imaging (Smart Eye Camera). . https://doi.org/10.21203/rs.3.rs-8074316/v1

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DOI
10.21203/rs.3.rs-8074316/v1

Data Quality

Data completeness: 70%