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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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; both teacher heads agree on what is shown here.
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".