Predicting contrast sensitivity functions with digital twins.

ABSTRACT: We developed and validated digital twins (DTs) for contrast sensitivity function (CSF) across 12 prediction tasks using a data-driven, generative model approach based on a hierarchical Bayesian model (HBM). For each prediction task, we utilized the HBM to compute the joint distribution of CSF hyperparameters and parameters at the population, subject, and test levels. This computation was based on a combination of historical data (N = 56), any new data from additional subjects (N = 56), and "missing data" from unmeasured conditions. The posterior distributions of the parameters in the unmeasured conditions were used as input for the CSF generative model to generate predicted CSFs. In addition to their accuracy and precision, these predictions were evaluated for their potential as informativ

SUBMITTER: Zhao Y 

PROVIDER: S-EPMC11480470 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature