Suitability of multifunction devices Myah and Myopia Master for monitoring myopia progression in children and adults.

PURPOSE: To assess the feasibility of using multifunction instruments to measure axial length for monitoring myopia progression in children and adults. METHODS: Axial length was measured in 60 children (aged 6-18 years) and 60 adults (aged 19-50 years) with multifunction instruments (Myah and Myopia Master) and stand-alone biometers (Lenstar LS900 and IOLMaster 700). Repeatability (measurements by the same examiner) and reproducibility (measurements by different examiners) were computed as the within-subject standard deviation (Sw) and 95% limits of agreement (LoA). Inter-instrument agreement was computed as intraclass correlation coefficients. The threshold for detecting myopic progression was taken as 0.1 mm. Measures were repeated only in children following the administration of 1% tropicamide to determine the impact of cycloplegia on axial length. RESULTS: Overall, the IOLMaster 700 had the best repeatability in children (0.014 mm) and adults (0.009 mm). Repeatability Sw values for all devices ranged from 0.005 to 0.021 mm (children) and 0.003 to 0.016 mm (adults). In children, reproducibility fell within 0.1 mm 95% of the time for the Myah, Myopia Master and IOLMaster 700. Agreement among all devices was classified as excellent (ICC 0.999; 95% CI 0.998-0.999), but the 95% LoA among the Myah, Myopia Master and Lenstar LS900 was ≥0.1 mm. Cycloplegia had no statistically significant effect on axial length (all p > 0.13). CONCLUSIONS: The Myah and Myopia Master multifunction instruments demonstrated good repeatability and reproducibility, and their accuracy was comparable to stand-alone biometers. Axial length measurements using different instruments can be considered interchangeable but should be compared with some caution. Accurate axial length measurements can be obtained without cycloplegia. The multifunction instruments Myah and Myopia Master are as well suited for monitoring myopia progression in children as the stand-alone biometers IOLMaster 700 and Lenstar LS900.

Multimodal Genotype and Phenotype Data Integration to Improve Partial Data-Based Longitudinal Prediction.

Multimodal data analysis has attracted ever-increasing attention in computational biology and bioinformatics community recently. However, existing multimodal learning approaches need all data modalities available at both training and prediction stages, thus they cannot be applied to many real-world biomedical applications, which often have a missing modality problem as the collection of all modalities is prohibitively costly. Meanwhile, two diagnosis-related pieces of information are of main interest during the examination of a subject regarding a chronic disease (with longitudinal progression): their current status (diagnosis) and how it will change before next visit (longitudinal outcome). Correct responses to these queries can identify susceptible individuals and provide the means of early interventions for them. In this article, we develop a novel adversarial mutual learning framework for longitudinal disease progression prediction, allowing us to leverage multiple data modalities available for training to train a performant model that uses a single modality for prediction. Specifically, in our framework, a single-modal model (which utilizes the main modality) learns from a pretrained multimodal model (which accepts both main and auxiliary modalities as input) in a mutual learning manner to (1) infer outcome-related representations of the auxiliary modalities based on its own representations for the main modality during adversarial training and (2) successfully combine them to predict the longitudinal outcome. We apply our method to analyze the retinal imaging genetics for the early diagnosis of age-related macular degeneration (AMD) disease, that is, simultaneous assessment of the severity of AMD at the time of the current visit and the prognosis of the condition at the subsequent visit. Our experiments using the Age-Related Eye Disease Study dataset show that our method is more effective than baselines at classifying patients' current and forecasting their future AMD severity.