Development of a Mobile Terminal-Based Intelligent Detection System for Multiple Anterior Segment Diseases of the Eye
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: Smartphone-based on-device artificial intelligence system for anterior segment eye disease screening.
- Who it may be relevant to
- Registry conditions: Artifical Intelligence, Cataract, Pterygium, Keratopathy. Basic parameters: from 18 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- China
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Overview
This is a multi-center, cross-sectional study evaluating a smartphone-based artificial intelligence (AI) system for anterior segment eye disease screening. The system is designed to identify 16 clinically important anterior segment conditions from images captured using a standard Android smartphone. A core design feature of the system is that all image analysis is performed entirely on the smartphone itself, without requiring internet connectivity or cloud-based server infrastructure. The study is motivated by a structural challenge in the deployment of medical AI: systems that depend on cloud infrastructure for inference are non-functional in settings without reliable internet access, which disproportionately excludes populations in low-resource regions where the burden of preventable eye disease is highest. This study evaluates whether an on-device AI system, designed with operational constraints as a primary engineering objective, can deliver clinically acceptable diagnostic performance while remaining operable under real-world connectivity limitations. The study comprises five evaluation components. First, the diagnostic performance of the AI system is benchmarked against board-certified ophthalmologists of varying seniority on a standardized set of smartphone-captured anterior segment images. Second, the usability of the system is evaluated among non-medical users who perform self-administered screening with minimal instruction, with per-screening time recorded across consecutive attempts to characterize the learning curve. Third, a head-to-head field trial directly compares the on-device AI system against a functionally equivalent cloud-based deployment of the same model architecture across key operational dimensions including screening duration, diagnostic performance, and user acceptability. Fourth, population-level screening is conducted among consecutively enrolled community residents at two low-resource sites, with per-disease sensitivity and specificity calculated against reference-standard slit-lamp examinations. Fifth, pre-specified health-economic and environmental analyses compare the two deployment modalities in terms of per-person screening cost, cost-effectiveness, per-inference electricity consumption, and projected carbon emissions at scale. The reference standard for all diagnostic comparisons is slit-lamp biomicroscopic examination performed by board-certified ophthalmologists. The study is designed and reported in accordance with the DECIDE-AI reporting guideline for early-stage clinical evaluation of AI-driven decision-support systems.
Interventions
- Device Smartphone-based on-device artificial intelligence system for anterior segment eye disease screening
A structured-pruned one-stage object-detection model deployed as a standalone Android application, performing all image inference on-device without internet connectivity, designed to detect 16 anterior segment eye diseases from smartphone-captured images.
Primary outcome measures
- Case-level diagnostic accuracy of the AI system compared with board-certified ophthalmologists [Time frame: Day 1]
- Diagnostic accuracy of the AI system when operated by non-medical users [Time frame: Day 1]
- Sensitivity of the on-device AI system in population-level community screening [Time frame: Day 1]
- Incremental cost-effectiveness ratio of on-device versus cloud-based screening [Time frame: Day 1]
- Per-inference electricity consumption of on-device versus cloud-based deployment [Time frame: Day 1]
Secondary outcome measures (6)
- Total image evaluation time of the AI system compared with board-certified ophthalmologists [Time frame: Day 1]
- Per-screening time learning curve among non-medical operators [Time frame: Day 1]
- Screening duration comparing on-device and cloud-based deployment in resource-limited field settings [Time frame: Day 1]
- User acceptability comparing on-device and cloud-based deployment in resource-limited field settings [Time frame: Day 1]
- Diagnostic accuracy of the on-device AI system in population-level community screening [Time frame: Day 1]
- Specificity of the on-device AI system in population-level community screening [Time frame: Day 1]
Eligibility criteria
Inclusion criteria
- Adults aged 18 years or older;
- Willing to participate and able to provide written informed consent prior to enrollment.
Exclusion criteria
- Unable to cooperate with anterior segment image capture (including smartphone-based photography or slit-lamp biomicroscopy).
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: Yes
Study design
- Observational model
- Other
Study locations
China · 1 center
- Zhongshan Ophthalmic Center, Sun Yat-sen University — Guangzhou
Identifiers
NCT: NCT07634913 · 2023KYPJ274