Visual prostheses: The enabling technology to give sight to the blind
Bibliographic record
Abstract
Millions of patients are either slowly losing their vision or are already blind due to retinal degenerative diseases such as retinitis pigmentosa (RP) and age-related macular degeneration (AMD) or because of accidents or injuries. Employment of artificial means to treat extreme vision impairment has come closer to reality during the past few decades. Currently, many research groups work towards effective solutions to restore a rudimentary sense of vision to the blind. Aside from the efforts being put on replacing damaged parts of the retina by engineered living tissues or microfabricated photoreceptor arrays, implantable electronic microsystems, referred to as visual prostheses, are also sought as promising solutions to restore vision. From a functional point of view, visual prostheses receive image information from the outside world and deliver them to the natural visual system, enabling the subject to receive a meaningful perception of the image. This paper provides an overview of technical design aspects and clinical test results of visual prostheses, highlights past and recent progress in realizing chronic high-resolution visual implants as well as some technical challenges confronted when trying to enhance the functional quality of such devices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".