From Cellular to Metabolic: Advances in Imaging of Inherited Retinal Diseases
Bibliographic record
Abstract
Background: Inherited retinal diseases (IRDs) are a genetically complex group of disorders, usually resulting in progressive vision loss due to retinal degeneration. Traditional imaging methods help in structural assessments, but limitations exist in early functional cellular-level detection that are crucial for guiding new therapies. Methods: This review includes a systematic search of PubMed and Google Scholar for studies on advanced imaging techniques for IRDs. Results: Key modalities covered are adaptive optics, fluorescence lifetime imaging ophthalmoscopy, polarization-sensitive optical coherence tomography, optoretinography, mitochondrial imaging, flavoprotein fluorescence imaging, and retinal oximetry. Each imaging method covers its principles, acquisition techniques, data from healthy eyes, applications in IRDs with specific examples, and current challenges and future directions. Conclusions: Emerging technologies, including adaptive optics and metabolic imaging, offer promising potential for cellular-level imaging and functional correlation in IRDs, allowing for earlier intervention and improved therapeutic targeting. Their integration into clinical practice may significantly improve IRD management and patient outcomes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| 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) | 0.003 | 0.001 |
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".