Development of a system based on artificial intelligence to identify visual problems in children: study protocol of the TrackAI project

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

Defining Natural History: Assessment of the Ability of College Students to Aid in Characterizing Clinical Progression of Niemann-Pick Disease, Type C

Niemann-Pick Disease, type C (NPC) is a fatal, neurodegenerative, lysosomal storage disorder. It is a rare disease with broad phenotypic spectrum and variable age of onset. These issues make it difficult to develop a universally accepted clinical outcome measure to assess urgently needed therapies. To this end, clinical investigators have defined emerging, disease severity scales. The average time from initial symptom to diagnosis is approximately 4 years. Further, some patients may not travel to specialized clinical centers even after diagnosis. We were therefore interested in investigating whether appropriately trained, community-based assessment of patient records could assist in defining disease progression using clinical severity scores. In this study we evolved a secure, step wise process to show that pre-existing medical records may be correctly assessed by non-clinical practitioners trained to quantify disease progression. Sixty-four undergraduate students at the University of Notre Dame were expertly trained in clinical disease assessment and recognition of major and minor symptoms of NPC. Seven clinical records, randomly selected from a total of thirty seven used to establish a leading clinical severity scale, were correctly assessed to show expected characteristics of linear disease progression. Student assessment of two new records donated by NPC families to our study also revealed linear progression of disease, but both showed accelerated disease progression, relative to the current severity scale, especially at the later stages. Together, these data suggest that college students may be trained in assessment of patient records, and thus provide insight into the natural history of a disease

Colour perception develops throughout childhood with increased risk of deficiencies in children born prematurely

AbstractAimTo quantify the impact of prematurity on chromatic discrimination throughout childhood, from 2 to 15 years of age.MethodsWe recruited two cohorts of children, as part of the TrackAI Project, an international project with seven different study sites: a control group of full‐term children with normal visual development and a group of children born prematurely. All children underwent a complete ophthalmological exam and an assessment of colour discrimination along the three colour axes: deutan, protan and trytan using a DIVE device with eye tracking technology.ResultsWe enrolled a total of 1872 children (928 females and 944 males) with a mean age of 6.64 years. Out of them, 374 were children born prematurely and 1498 were full‐term controls. Using data from all the children born at term, reference normative curves were plotted for colour discrimination in every colour axis. Pre‐term children presented worse colour discrimination than full‐term in the three colour axes (p < 0.001). Even after removing from the comparison, all pre‐term children with any visual disorder colour discrimination outcomes remained significantly worse than those from full‐term children.ConclusionWhile colour perception develops throughout the first years of life, children born pre‐term face an increased risk for colour vision deficiencies

Changes in concentrations of glucose in gastric juice (a) and hemolymph (b), amino acid in gastric juice (c) and hemolymph (d), and triglyceride in gastric juice (e) and hemolymph (f) of <i>Panulirus argus</i> after feeding diets with different carbohydrate sources.

<p>Diets were named according to the carbohydrate source they contained (wheat flour, rice starch, maize starch). Each value is the mean ± SEM (N  = 5 lobsters per diet). For each dietary treatment, the time at which the first statistically difference was found with respect to 2 h is marked by an asterisk. Differences among diets throughout the 30-h experiments are marked by different superscript letters in the legend (Two-way ANOVA, Tukey test, P≤0.05).</p

Changes in concentration of lactate in hemolymph of <i>Panulirus argus</i> after feeding diets with different carbohydrate sources.

<p>Diets were named according to the carbohydrate source they contained (wheat flour, rice starch, maize starch). Each value is the mean ± SEM (N = 5 lobsters per diet). For each dietary treatment, the time at which the first statistically difference was found with respect to 2 h is marked by an asterisk. Differences among diets throughout the 30-h experiments are marked by different superscript letters in the legend (Two-way ANOVA, Tukey test, P≤0.05).</p

Summary of the canonical discrimination analysis for discriminant functions used to identify the experimental diet ingested by lobster, <i>Panulirus argus</i>.

<p>Summary of the canonical discrimination analysis for discriminant functions used to identify the experimental diet ingested by lobster, <i>Panulirus argus</i>.</p

<i>In vitro</i> hydrolysis rates of different carbohydrate substrates by digestive gland extracts of the spiny lobsters <i>Panulirus argus</i>.

<p>Each value is the mean ± SEM (N = 30 lobsters per diet) Significant differences are marked by different superscript letters (one-way ANOVA, Tukey test, P≤0.05).</p

Carbohydrate sources used for <i>in vitro</i> digestion.

<p>^*Marked carbohydrates were also assayed after gelatinisation at 80°C for 20 min (100 g L⁻¹ deionizer water) and dried at 50°C for 48 h <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0108875#pone.0108875-Simon1" target="_blank">[2]</a>.</p><p>Carbohydrate sources used for <i>in vitro</i> digestion.</p

Standardised function coefficients for each of the significant discriminant functions used to identify the experimental diet ingested by lobster, <i>Panulirus argus</i>.

<p>Coefficients with more weight inside the discriminant function are in bold. DG, digestive gland; M, muscle; Hph, hemolymph; PK, pyruvate kinase; LDH, L-lactate dehydrogenase; FBPase, fructose 1,6-biphosphatase; G6PDH, glucose-6-phosphate dehydrogenase; AST, aspartate transaminase; HOAD, 3-hydroxyacyl-CoA dehydrogenase; G3PDH, glycerol-3-phosphate dehydrogenase; GPase, glycogen phosphorylase.</p><p>Standardised function coefficients for each of the significant discriminant functions used to identify the experimental diet ingested by lobster, <i>Panulirus argus</i>.</p

Plots of the first two axes from the forward stepwise discriminant function analysis of metabolic enzymes (a) and metabolites (b) in lobster, <i>Panulirus argus</i>, ingesting diets with different carbohydrate sources or left unfed under the same experimental conditions.

<p>Several metabolic enzymes and metabolites from the digestive gland, the muscle and the hemolymph were included in the analysis (see text for details). Diets were named according to the carbohydrate source they contained (wheat flour, rice starch, maize starch).</p