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Although color vision deficiencies are very prevalent, there are no ideal methods for assessing color vision in all environments. We compared a new digital and automated method that quantifies color perception for the three protan, deutan, and tritan axes with two of the most commonly used color tests in daily practice: the Ishihara 38 plates test and the Farnsworth-Munsell 100-Hue test. One hundred patients underwent a triple examination composed of the new DIVE Color Test, the Ishihara test, and the Farnsworth-Munsell 100-Hue test. The DIVE Color Test was performed twice in forty participants to assess its repeatability. In the trichromatic group, the mean age stood at 20.57 ± 9.22 years compared with 25.99 ± 15.86 years in the dyschromatic group. The DIVE and Ishihara tests exhibited excellent agreement in identifying participants with color deficiency (Cohen's kappa = 1.00), while it was 0.81 when comparing DIVE and Farnsworth. The correlation between the global perception values of Farnsworth (TES) and DIVE (GCS) was 0.80. The repeatability of the DIVE Color Test was high according to Bland-Altman analysis with an intraclass correlation coefficient of 0.83. According to Ishihara, the DIVE Color Test proved to be an effective and reproducible tool for red-green color vision deficiency detection, capable of determining the severity of the defect in each of the three axes faster and more accurately than both Ishihara and Farnsworth.
RESUMO
INTRODUCTION: Around 70% to 80% of the 19 million visually disabled children in the world are due to a preventable or curable disease, if detected early enough. Vision screening in childhood is an evidence-based and cost-effective way to detect visual disorders. However, current screening programmes face several limitations: training required to perform them efficiently, lack of accurate screening tools and poor collaboration from young children.Some of these limitations can be overcome by new digital tools. Implementing a system based on artificial intelligence systems avoid the challenge of interpreting visual outcomes.The objective of the TrackAI Project is to develop a system to identify children with visual disorders. The system will have two main components: a novel visual test implemented in a digital device, DIVE (Device for an Integral Visual Examination); and artificial intelligence algorithms that will run on a smartphone to analyse automatically the visual data gathered by DIVE. METHODS AND ANALYSIS: This is a multicentre study, with at least five centres located in five geographically diverse study sites participating in the recruitment, covering Europe, USA and Asia.The study will include children aged between 6 months and 14 years, both with normal or abnormal visual development.The project will be divided in two consecutive phases: design and training of an artificial intelligence (AI) algorithm to identify visual problems, and system development and validation. The study protocol will consist of a comprehensive ophthalmological examination, performed by an experienced paediatric ophthalmologist, and an exam of the visual function using a DIVE.For the first part of the study, diagnostic labels will be given to each DIVE exam to train the neural network. For the validation, diagnosis provided by ophthalmologists will be compared with AI system outcomes. ETHICS AND DISSEMINATION: The study will be conducted in accordance with the principles of Good Clinical Practice. This protocol was approved by the Clinical Research Ethics Committee of Aragón, CEICA, on January 2019 (Code PI18/346).Results will be published in peer-reviewed journals and disseminated in scientific meetings. TRIAL REGISTRATION NUMBER: ISRCTN17316993.
