June 2015
Volume 56, Issue 7
ARVO Annual Meeting Abstract  |   June 2015
Validation of Confidence Levels for a Cell Phone-Based Refractor (NETRA-G)
Author Affiliations & Notes
  • Vitor F Pamplona
    EyeNetra Inc, Somerville, MA
  • Steven Turpin
    University of California, Berkeley, Berkeley, MA
  • Jorge Cuadros
    University of California, Berkeley, Berkeley, MA
  • Rahul Modi
    EyeNetra Inc, Somerville, MA
  • Footnotes
    Commercial Relationships Vitor Pamplona, EyeNetra Inc (E); Steven Turpin, None; Jorge Cuadros, None; Rahul Modi, EyeNetra Inc (E)
  • Footnotes
    Support None
Investigative Ophthalmology & Visual Science June 2015, Vol.56, 2211. doi:
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      Vitor F Pamplona, Steven Turpin, Jorge Cuadros, Rahul Modi; Validation of Confidence Levels for a Cell Phone-Based Refractor (NETRA-G). Invest. Ophthalmol. Vis. Sci. 2015;56(7 ):2211.

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      © ARVO (1962-2015); The Authors (2016-present)

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Purpose: To assess the performance of a cell phone based refracting device (NET). We identify the efficacy of NET confidence values by comparing refractive differences between NET and subjective refraction (SR).

Methods: NET retrofits a high-resolution mobile phone (Samsung S4) by adding a pinhole mask and a lens onto the display. The device is bi-ocular and both eyes are constantly fogged (+6D). The subject aligns red and green lines with the translation on the screen proportional to refractive error. Besides refraction, the device computes a confidence value (from 0, poor, to 1, good). The hypothesis is that results under 0.4 require a retest. 89 subjects (mean + SD age 28.41 +/- 6.95) underwent SR and NET refraction. Auto-Refractor (AR) was used as a starting point for SR. Subjects were split according to NET's confidence value and compared. Group A (n=70) included high confidence readings (>0.4) and Group B (n=19) low confidence readings (<0.4). Subject's refractive error ranged from +1.50 to -8.25D (mean + SD refraction -1.37 +/- 1.90D). Exclusion criteria included: amblyopia, non-refractory pathologies, and 4 subjects in which NET yielded a better refraction than SR.

Results: For all 89 subjects, the mean + SD Vector Power Difference (VDD) of NET-SR is 1.20 +/- 1.24D. Group A's mean + SD VDD is 0.95 +/- 0.60D (corr=95%). In comparison, the mean + SD VDD of AR-SR is 0.51 +/- 0.45D (note: SR is biased towards AR). NET, AR and SR yielded 20/25 or better vision on 92%, 96% and 98% respectively. Mean + SD visual acuity difference of NET-SR is -0.07 +/- 0.08 logMar. Group B's mean + SD VDD of NET-SR is 2.12 +/- 2.18D (corr=65%). NET, AR and SR yielded 20/25 or better for 61%, 72% and 89% respectively. Linear regression of spherical equivalent presented a slope of 0.86 and a y-intercept of -0.63D. Mean + SD of PD difference of NET-AR is -0.76 +/- 1.88mm.

Conclusions: NET's confidence value can be used as a predictor for insufficient accuracy, triggering a retest or a discarding method. The low confidence group included the only 2 subjects with high-order aberrations and 5 subjects that required translation (a known language barrier for NET, since subjects need to understand the procedures). Group A included all subjects that could not reach 20/20 vision on the SR. The results show that NET has potential to be a used as an effective tool for rapidly estimating refractive errors and interpupillary distance measurements.


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