Introduction: Early eye screening and treatment can reduce the incidence of blindness by detecting and addressing eye diseases at an early stage. The Ophthalmologist Robot is an automated device that can simultaneously capture ocular surface and fundus images without the need for ophthalmologists, making it highly suitable for primary application. However, the accuracy of the device's screening capabilities requires further validation. This study aims to evaluate and compare the screening accuracies of ophthalmologists and deep learning models using images captured by the Ophthalmologist Robot, in order to identify a screening method that is both highly accurate and cost-effective. Our findings may provide valuable insights into the potential applications of remote eye screening.
Methods and analysis: This is a multicentre, prospective study that will recruit approximately 1578 participants from 3 hospitals. All participants will undergo ocular surface and fundus images taken by the Ophthalmologist Robot. Additionally, 695 participants will have their ocular surface imaged with a slit lamp. Relevant information from outpatient medical records will be collected. The primary objective is to evaluate the accuracy of ophthalmologists' screening for multiple blindness-causing eye diseases using device images through receiver operating characteristic curve analysis. The targeted diseases include keratitis, corneal scar, cataract, diabetic retinopathy, age-related macular degeneration, glaucomatous optic neuropathy and pathological myopia. The secondary objective is to assess the accuracy of deep learning models in disease screening. Furthermore, the study aims to compare the consistency between the Ophthalmologist Robot and the slit lamp in screening for keratitis and corneal scar using the Kappa test. Additionally, the cost-effectiveness of three eye screening methods, based on non-telemedicine screening, ophthalmologist-telemedicine screening and artificial intelligence-telemedicine screening, will be assessed by constructing Markov models.
Ethics and dissemination: The study has obtained approval from the ethics committee of the Ophthalmology and Optometry Hospital of Wenzhou Medical University (reference: 2023-026 K-21-01). This work will be disseminated by peer-review publications, abstract presentations at national and international conferences and data sharing with other researchers.
Trial registration number: ChiCTR2300070082.
Keywords: Diagnostic Imaging; OPHTHALMOLOGY; PUBLIC HEALTH.
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