July 2019
Volume 60, Issue 9
Open Access
ARVO Annual Meeting Abstract  |   July 2019
Mapping contrast sensitivity of visual field with Bayesian adaptive qVFM method
Author Affiliations & Notes
  • Pengjing Xu
    Psychology, The Ohio State University, Columbus, Ohio, United States
  • Luis Andres Lesmes
    Adaptive Sensory Technology, California, United States
  • Deyue Yu
    College of Optometry, The Ohio State University, COLUMBUS, Ohio, United States
  • Zhong-Lin Lu
    Psychology, The Ohio State University, Columbus, Ohio, United States
  • Footnotes
    Commercial Relationships   Pengjing Xu, The Ohio State University (P); Luis Lesmes, Adaptive Sensory Technology (I), Adaptive Sensory Technology (E), Adaptive Sensory Technology (P); Deyue Yu, The Ohio State University (P); Zhong-Lin Lu, Adaptive Sensory Technology (I), The Ohio State University (P)
  • Footnotes
    Support  National Eye Institute (EY021553, EY025658)
Investigative Ophthalmology & Visual Science July 2019, Vol.60, 4377. doi:
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    • Get Citation

      Pengjing Xu, Luis Andres Lesmes, Deyue Yu, Zhong-Lin Lu; Mapping contrast sensitivity of visual field with Bayesian adaptive qVFM method. Invest. Ophthalmol. Vis. Sci. 2019;60(9):4377.

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

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Purpose : Current clinical evaluation,which focuses on central vision,could improve characterization of residual vision with peripheral testing of visual acuity,contrast sensitivity,color vision,crowding,and reading speed.Assessing more than light sensitivity,a comprehensive visual field map(VFM) of functional vision would be valuable for detecting and managing eye diseases.

Methods : We previously developed a Bayesian adaptive qVFM method that combines a global approach for preliminary assessment of the VFM's shape,and a local approach for assessment at individual retinal locations.The method was validated in measuring the light sensitivity map.In this study,we extended qVFM to measure contrast sensitivity across visual field.In both simulations and psychophysics,we sampled 64 visual field locations(48x48 deg) and compared qVFM with a procedure testing locations independently(qFC;Lesmes et al.,2015).Subjects were identified a single optotype (size: 2.5x2.5deg),one of 10 Sloan alternatives,filtered with a raised cosine filter and octave bandwidth.On each trial,contrast and location was adaptively selected.Three eyes were simulated to compare the accuracy and precision of VFMs measured with 1280 trials of each method.In addition,data were collected from eight eyes(4 OS,4 OD) of four normal observers.

Results : For simulations,the average bias of qVFM and qFC contrast threshold estimates(in log10 units) were 0.021 and 0.072 after 320 trials,0.0079 and 0.0080 after 1280 trials.The average standard deviation(SD) of qVFM and qFC estimates were 0.053 and 0.089 after 320 trials, 0.031 and 0.049 after 1280 trials.The estimated within-run variability(68.2% HWCIs) were comparable to the estimated cross-run variability(SD).For psychophysics,the average HWCI of qVFM and qFC estimates across the visual field decreased from 0.28 on the first trial to 0.083 and 0.15 after 160,to 0.061 and 0.092 after 320 trials.The root mean squared error(RMSE) of thresholds estimated with qVFM and qFC started at 0.21, decreased to 0.12 after 160 and to 0.10 after 320 trials.

Conclusions : The qVFM provides an accurate,precise,efficient mapping of contrast sensitivity across the entire visual field.The method could find potential clinical applications in monitoring vision loss,evaluating therapeutic interventions,and developing effective rehabilitation for low vision.

This abstract was presented at the 2019 ARVO Annual Meeting, held in Vancouver, Canada, April 28 - May 2, 2019.


Fig1: Ten filtered Sloan letters.

Fig1: Ten filtered Sloan letters.


Fig2: Estimated VFMs (OD and OS) from one human observer.

Fig2: Estimated VFMs (OD and OS) from one human observer.


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