Development of a Mobile Terminal-Based Intelligent Detection System for Multiple Anterior Segment Diseases of the Eye

Обзор

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.

Вмешательства

• Устройство 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.

Первичные конечные точки

• Case-level diagnostic accuracy of the AI system compared with board-certified ophthalmologists [Срок оценки: Day 1]
• Diagnostic accuracy of the AI system when operated by non-medical users [Срок оценки: Day 1]
• Sensitivity of the on-device AI system in population-level community screening [Срок оценки: Day 1]
• Incremental cost-effectiveness ratio of on-device versus cloud-based screening [Срок оценки: Day 1]
• Per-inference electricity consumption of on-device versus cloud-based deployment [Срок оценки: Day 1]

Вторичные конечные точки (6)

• Total image evaluation time of the AI system compared with board-certified ophthalmologists [Срок оценки: Day 1]
• Per-screening time learning curve among non-medical operators [Срок оценки: Day 1]
• Screening duration comparing on-device and cloud-based deployment in resource-limited field settings [Срок оценки: Day 1]
• User acceptability comparing on-device and cloud-based deployment in resource-limited field settings [Срок оценки: Day 1]
• Diagnostic accuracy of the on-device AI system in population-level community screening [Срок оценки: Day 1]
• Specificity of the on-device AI system in population-level community screening [Срок оценки: Day 1]

Критерии участия

Критерии включения

• Adults aged 18 years or older;
• Willing to participate and able to provide written informed consent prior to enrollment.

Критерии исключения

• Unable to cooperate with anterior segment image capture (including smartphone-based photography or slit-lamp biomicroscopy).

Критерии приведены из реестра в оригинале (на английском). Окончательную оценку соответствия проводит исследовательский центр.

Здоровые добровольцы: Да

Дизайн исследования

Модель наблюдения

Другое

Центры проведения

Китай · 1 центр

• Zhongshan Ophthalmic Center, Sun Yat-sen University — Гуанчжоу

Идентификаторы

NCT: NCT07634913 · 2023KYPJ274

Первоисточники (государственные реестры)

• ClinicalTrials.gov (NIH, США)

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