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Restoring vision to the blind Part II: What will the patients see?. Gislin Dagnelie, Ph.D. Lions Vision Research & Rehabilitation Ctr Wilmer Eye Institute Johns Hopkins Univ Sch of Medicine Department of Veterans Affairs Rehabilitation Center Augusta, GA April 15, 2005. Lines of attack.

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Restoring vision to the blind Part II: What will the patients see?

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Restoring vision to the blindPart II: What will the patients see?

Gislin Dagnelie, Ph.D.

Lions Vision Research & Rehabilitation Ctr

Wilmer Eye Institute

Johns Hopkins Univ Sch of Medicine

Department of Veterans Affairs Rehabilitation Center

Augusta, GA

April 15, 2005


Lines of attack

  • Systems engineering (“brute force” or maybe just pragmatic)

  • Electrode/tissue engineering (“remodeling the interface”)

  • Likely limitations (space and time)

  • (Low) vision science/rehab


Spatial limits: retinal rewiringRobert Marc

  • Ultrastructural evidence from donor RP/AMD retinas:

    • Extensive rewiring of inner retinal cells

    • Neurite processes spread over long distances (~300 μm)

    • Glial cells migrate into choroid

  • Injected electrical current may spread through neurite tangle

Marc RE, Progr in Retin Eye Res 22:607-655 (2003)


Spatial limits: implications of retinal rewiring

  • Stimulating degenerated retina may be like writing on tissue paper with a fountain pen:

    • Charge diffusion over distances up to 1o

    • Phosphenes likely to be blurry (Gaussian blobs), not sharp

    • Minor effect if electrodes are widely spaced (>= 2o)

    • Phosphenes from closely spaced electrodes may overlap/fuse

      Retinal prosthetic vision may be pretty blurry…


Temporal limits: persistenceHumayun et al.

  • Single electrode, acute testing:

    • Flicker fusion occurs at 25-40 Hz

  • Multi-electrode implant testing:

    • Rapid changes are hard to detect

    • Flicker fusion at lower frequency?

      Maybe prosthetic vision will be not just blurry, but also streaky…


And then there is background noise:Many blind RP patients see “flashes” like this…


…or even this…


so reading with a (high-resolution, retinal) prosthesis may look like this…


…or even this !


Caution

It is naïve to expect that we will

implant a retinal prosthesis,

turn on the camera,

and just send the patient home to practice


Lines of attack

  • Systems engineering (“brute force” or maybe just pragmatic)

  • Electrode/tissue engineering (“remodeling the interface”)

  • Likely limitations (space and time)

  • (Low) vision science/rehab


Daily activities:How many dots do they take?…


Developing an implantable prosthesis

  • How does it work?

  • Why should it work?

  • What did blind patients see in the OR?

  • What do the first implant recipients tell us?

  • What could the future look like?

  • What’s up next?


Simulation techniques

  • “Pixelized” images shown to normally-sighted and low vision observers wearing video headset

  • Images are gray-scale only, no color

  • Layout of dots in crude raster, similar to (current and anticipated) retinal implants

  • Subject scans raster across underlying image through:

    • Mouse/cursor movement, or

    • Head movement (camera or head tracker)


Performance under “idealized” conditions

Subjects performed the following tasks:

  • Use live video images to perform “daily activities”

  • Walk around an office floor

  • Discriminate a face in 4 alternative forced choice

  • Read meaningful text


Live test: candy pour, 16x16


Live test: Mobility


Live test: Mobility, 6x10


Live test: spoon in 4x4 view


Face Identification Procedure


Face identification: Methods

  • 4 groups (M/F, B/W) of 15 models (Y/M/O, 5 each)

  • Face width 12º

  • Parameters (varied one by one from standard):

    • Dot size: 23-78 arcmin

    • Gap size: 5-41 arcmin

    • Grid size: 10X10, 16X16, 25X25, 32X32

    • Random dropout: 10%, 30%, 50%, 70%

    • Gray levels: 2, 4, 6, 8

  • Tests performed at 98% and 13% contrast

  • Each parameter combination presented 6 times

  • Data from 4 normally-sighted subjects


Face identification: Dot size


Face identification: Dot spacing


Face identification: Grid size


Face identification: Dropout percentage


Face identification: Gray levels


Face identification: Summary

  • Performance well above chance, except for:

    • large dots and/or gaps (i.e., <6 c/fw)

    • small grid or small dots (< 0.5 fw)

    • >50% drop-out

    • <4 gray levels

  • Low contrast does not seriously reduce performance

  • Significant between-subject variability (unfamiliar task?)


Reading test: Procedure


Reading test: Sample clips


Reading test: Methods

  • Novel, meaningful text; grade 6 level

  • Scored for reading rate and accuracy

  • Font size 31, 40, 50, 62 points (2-4º characters)

  • Parameters (varied separately from standard):

    • Dot size: 23-78 arcmin

    • Gap size: 5-41 arcmin

    • Grid size: 10X10, 16X16, 25X25, 32X32

    • Random dropout: 10%, 30%, 50%, 70%

    • Gray levels: 2, 4, 6, 8

  • Tests performed at 98% and 13% contrast


Reading speed: Font size


Reading speed: Dot size


Reading speed: Dot spacing


Reading speed: Grid size


Reading speed: Dropout percentage


Reading speed: Gray levels


Reading test: Summary

  • Reading adequate, but drops off for:

    • Small fonts (<6 dots/char)

    • Small grid (plateau beyond 25X25 dots)

    • >30% drop-out (esp. low contrast)

    • Note: even 2 gray levels adequate

  • Low contrast reduces performance, but reading still adequate

  • Much less intersubject variability than for face identification (familiar task?)


Introducing Virtual Reality

  • Flexible tasks:

    • Object and maze properties can be varied “endlessly”

    • Difficulty level can be adjusted (even automatically)

  • Precise response measures:

    • Subjects’ actions can be logged automatically

    • Constant response criteria can be built in

  • It’s safe!


Virtual mobility task

  • Ten different “floor plans” in a virtual building

  • Pixelized and stabilized view, 6x10 dots

  • Drop-out percentage and dynamic noise varied

  • Use cursor keys to maneuver through 10 rooms


Video: Virtual mobility, normal view


Video: Virtual mobility, 6x10 pixelized view


Prosthetic vision simulations:Visual inspection/coordination

Playing checkers:

A challenge for visually guided performance


Introducing Eye Movements

  • Until now, free viewing conditions:

    • Subject can scan eye across dot raster

    • Mouse or camera movement used to scan raster across scene

  • Electrodes will be stabilized on the retina:

    • When the eyes move, dots move along

    • Mouse or camera used to move scene “behind” dots

  • Tough task !


Video pair: Face identification taskFree-viewing vs. gaze-locked


Face identification, free-viewing vs. gaze-locked: Learning

FV= free viewing, FX= fixation controlled


Video pair: Reading taskFree-viewing vs. gaze-locked


Prosthetic vision simulations:Low Vision Science

  • Reading with pixelized vision, stabilized vs. free-viewing:

    • Accuracy falls off a little sooner, and reading rate is 5x lower, BUT

    • Spatial processing properties (dots/charwidth and char/window drop-off) do not change

    • At low contrast, window restriction more severe (not shown)


Prosthetic vision simulations:Rehabilitation

  • Learning makes all the difference:

    • Accuracy increases over time, both for high and for low contrast

    • Reading speed increases over time, for high and low contrast

    • Stabilized reading takes longer to learn, but improves relative to free viewing, both in accuracy and speed


So what’s the use of simulations?

Simulating prosthetic vision can help in:

  • Determining requirements for vision tasks

  • Exploring and understanding wearers’ reports

  • Helping to find solutions for wearers’ problems

  • Conveying the “prosthetic experience” to clinicians and public

    AND:

  • Designing rehabilitation programs to help future prosthesis recipients


Functional prosthetic vision:How far off ?

  • Our subjects perform quite well with 16X16 (or more) electrodes

  • They can learn to perform most tasks with 6X10

  • They can learn to avoid obstacles with 4X4

  • Typical daily living activities will require larger numbers of electrodes (at least 10X10), and intensive rehabilitation


Conclusion

Prosthetic vision is not just a technological challenge:

It promises to bring new areas of vision research and rehabilitation

http://lions.med.jhu.edu/lvrc/gd.htm


Simulations supported by:

National Eye Institute and Foundation Fighting Blindness

Special thanks to:

  • Anna Cronin-Scott

  • Paul Dagnelie

  • Chris De Marco, Ph.D.

  • Jasmine Hayes

  • Pearse Keane

  • Wentai Liu, Ph.D.

  • Laura Martin

  • Kathy Turano, Ph.D.

  • Matthias Walter

  • Vivian Yin

  • Second Sight, LLC

Towards artificial sight: A long, exciting road ahead!


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