Detecting and Localizing Objects With Color-Contrast With a Second-Generation (44-Channel) Suprachoroidal Retinal Prosthesis
Purpose: To assess the detection of color contrast in retinal prosthesis users by evaluating a novel Detecting Objects with Color-Contrast (DOCC) vision processing algorithm. Methods: Three participants monocularly implanted with a suprachoroidal retinal prosthesis (NCT03406416) were asked to locate and identify objects on a tabletop that were color-contrasted with their backgrounds using the DOCC algorithm, a control algorithm (Lanczos2 [L2]) and system off. The task involved two setups; (i) high-contrast white-on-black objects, and (ii) low-grayscale-contrast (color-only contrast) red-on-green objects. Participants were required to verbally localize (nine alternative forced choice), then touch the object, with the distance measured from their finger to the true object location. Results: For low- (color-only) contrast objects, DOCC (58.3% ± 43.0%) performed better than L2 (27.9% ± 42.5%) for verbally localizing objects (P < 0.0001) and both DOCC (P < 0.0001) and L2 (P = 0.0018) performed better than system off (9.1% ± 33.1%). Furthermore, DOCC (9.7 ± 10.6 cm) had a closer final proximity to the object than L2 (28.0 ± 24.7 cm; P < 0.0001) and system off (45.0 ± 23.9 cm; P < 0.0001). For high-contrast objects, DOCC (59.8% ± 47.8%) performed similarly to L2 (60.8% ± 48.3%) and system off (25.4% ± 41.8%) for verbally localizing objects (P = 0.32, three-condition). Likewise, for final object proximity, DOCC (8.0 ± 7.5 cm) also performed similarly to L2 (6.2 ± 8.4 cm, P = 0.43), whereas both DOCC (P = 0.0014) and L2 (P < 0.0001) performed better than system off (25.6 ± 22.3 cm). Conclusions: Vision processing software can improve object detection and localization for objects of one color against a low-contrast background of another color. Translational Relevance: A color-contrast algorithm may assist retinal prosthesis users with detecting and localizing objects that are color-contrasted to their background.
Authors
- Maria Kolic (ORCID: https://orcid.org/0000-0002-8877-3264)
- Janine G. Walker (ORCID: https://orcid.org/0000-0002-9223-8950)
- Penelope J. Allen (ORCID: https://orcid.org/0000-0001-5916-1253)
- Samuel A. Titchener (ORCID: https://orcid.org/0000-0001-8312-2924)
- William G. Kentler
- Nick M. Barnes (ORCID: https://orcid.org/0000-0002-9343-9535)
- Carla J. Abbott (ORCID: https://orcid.org/0000-0002-1432-8977)
- Elizabeth K. Baglin (ORCID: https://orcid.org/0000-0002-1754-9310)
- Matthew A. Petoe (ORCID: https://orcid.org/0000-0002-1865-3566)
- Nariman Habili
Institutions
- Australian National University (AU)
- Bionics Institute (AU)
- The University of Melbourne (AU)
- Florey Institute of Neuroscience and Mental Health (AU)
- The Royal Victorian Eye & Ear Hospital (AU)
- Health Sciences and Nutrition (AU)
- Centre for Eye Research Australia (AU)
- CSIRO Health and Biosecurity (AU)
- Melbourne Clinic (AU)
Publication Details
- Journal
- Translational Vision Science & Technology
- Published
- 2026-10-15
- DOI
- https://doi.org/10.1167/tvst.15.10.21
- Primary Topic
- Neuroscience and Neural Engineering
- Type
- article
- Field-Weighted Citation Impact
- 0.00