AI/ML

The Stanford Acuity Test: A Probabilistic Approach for Precise Visual Acuity Testing. (arXiv:1906.01811v1 [cs.AI])

Chart-based visual acuity measurements are used by billions of people to
diagnose and guide treatment of vision impairment. However, the ubiquitous eye
exam has no mechanism for reasoning about uncertainty and as such, suffers from
a well-documented reproducibility problem. In this paper we uncover a new
parametric probabilistic model of visual acuity response based on measurements
of patients with eye disease. We present a state of the art eye exam which (1)
reduces acuity exam error by 75% without increasing exam length, (2) knows how
confident it should be, (3) can trace predictions over time and incorporate
prior beliefs and (4) provides insight for educational Item Response Theory.
For patients with more serious eye disease, the novel ability to finely measure
acuity from home could be a crucial part in early diagnosis. We provide a web
implementation of our algorithm for anyone in the world to use.

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