Fundus camera versus smartphone camera attachment: image quality analysis
Bibliographic record
Abstract
Fundus cameras are the current clinical standard for capturing retinal images, which are used to diagnose a variety of sight-threatening conditions. Traditional fundus cameras are not easily transported, making them unsuitable for field use. In addition, traditional fundus cameras are expensive. Due to this, a variety of technologies have been developed such as the D-EYE Digital Ophthalmoscope (D-EYE Srl, Padova, Italy) which is compatible with various cellphone cameras. This paper reports on the comparison of the image quality of the Nidek RS-330 OCT Retina Scan Duo (Nidek, Tokyo, Japan) and the D-EYE paired with an iPhone 6 (Apple, Cupertino, USA). Twenty-one participants were enrolled in the study of whom 14 underwent nonmydriatic and mydriatic imaging with the D-EYE and the Nidek. Seven participants underwent nonmydriatic imaging with the D-EYE and the Nidek. The images were co-registered and cropped so that the region of interest was equal in both the D-EYE and Nidek images, as the D-EYE had a smaller field of view. Using the Nidek image as the reference, objective full-reference image quality analysis was performed. Metrics such as structural similarity index and peak signal noise ratio were obtained. It was found that the image quality of the D-EYE is limited by the attached iPhone camera, and is lower when compared to the Nidek. Quantification of the differences between the D-EYE and Nidek allows for targeted development of smartphone camera attachments that can help to bridge the current gap in image quality.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".