June 2020
Volume 61, Issue 7
Free
ARVO Annual Meeting Abstract  |   June 2020
Development of a novel wearable intraocular pressure monitor based on image tracking of exposed sclera
Author Affiliations & Notes
  • Matthew James Alan Rickard
    Globe Biomedical LLC, Riverside, California, United States
    California Baptist University, Riverside, California, United States
  • Joshua Park
    Globe Biomedical LLC, Riverside, California, United States
    California Baptist University, Riverside, California, United States
  • Creed Jones
    Globe Biomedical LLC, Riverside, California, United States
    Electrical and Computer Engineering, Virginia Tech, Blacksburg, Virginia, United States
  • Arthur J Sit
    Department of Ophthalmology, Mayo Clinic, Rochester, Minnesota, United States
  • Pinakin Gunvant Davey
    College of Optometry, Western University of Health Sciences, Pomona, California, United States
  • Footnotes
    Commercial Relationships   Matthew Rickard, Appl. No. 14/831,234 (P), Globe Biomedical (I); Joshua Park, Globe Biomedical (E), Globe Biomedical (I); Creed Jones, Appl. No. 14/831,234 (P), Globe Biomedical (I); Arthur Sit, Globe Biomedical (C); Pinakin Davey, Globe Biomedical (C)
  • Footnotes
    Support  NSF SBIR 1842393
Investigative Ophthalmology & Visual Science June 2020, Vol.61, 4765. doi:
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    • Get Citation

      Matthew James Alan Rickard, Joshua Park, Creed Jones, Arthur J Sit, Pinakin Gunvant Davey; Development of a novel wearable intraocular pressure monitor based on image tracking of exposed sclera. Invest. Ophthalmol. Vis. Sci. 2020;61(7):4765.

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

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Abstract

Purpose : To provide non-invasive, non-contact ambulatory IOP monitoring using image-based correlation of the mechanical strain of near-limbus sclera with IOP. Novel eyeglass frames embedded with miniature image sensors are under development.

Methods : We captured multiple images during five pressure-increasing inflation tests using porcine globes under various anatomical pressures on a benchtop. We compared the laboratory IOP values with the system measurement signal, which is based on tissue expansion observed in the imagery. The benchtop system, shown in Fig. 1, includes a 3D printed replica of a head (B1), 3D printed eyeglass frames with an embedded compact CMOS camera (A1), a water column (B6), a precision needle valve, a digital pressure sensor (B4), a liquid microflow sensor (B5) to ensure inflation completed, and an LCD Monitor displaying a gray screen to illuminate the pig eye (B2). To simulate a realistic use case, the frames rested on elastomer nose and ears created from a 3D printed prototype mold. Two eyes were used within 72 hours post-mortem and graphite powder was added to the eyes as needed for image tracking. A polynomial fit between the image-based measurement signal and the lab IOP was created for pressures in the expected calibration range (shown as solid markers in Fig. 2). The mathematical fit was then used to convert the image-based signal to expected IOP for higher pressures.

Results : For all five runs, the image-based measurement signal correlated well with polynomial curves generated using the mid-range IOP levels. Further, good prediction of elevated IOP using the polynomial equations was observed when extrapolated to higher pressures, which can be seen in Fig. 1 for one of the eyes. The IOP calculated by the image-based system when the lab IOP measured 30 mmHg was 31.1, 29.0, 28.6, 28.5, and 30.3.

Conclusions : Scleral imaging can be used to infer IOP in porcine globes using compact cameras embedded in eyeglass frames. The results support the realization of a novel, non-invasive technology for continuous IOP monitoring in glaucoma management.

This is a 2020 ARVO Annual Meeting abstract.

 

Fig. 1: Experimental setup for imaging exposed scleral strain in vitro

Fig. 1: Experimental setup for imaging exposed scleral strain in vitro

 

Fig. 2: Accuracy of predicting IOP using scleral strain imagery

Fig. 2: Accuracy of predicting IOP using scleral strain imagery

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