3D-Printed eye phantoms as physiological and pathological standards for training and instrument development
Bibliographic record
Abstract
Early identification of conditions that can lead to blindness is critical for saving vision. The optical red-reflex test (RRT), which assesses the light reflections from the back of the eye, is a key exam for identifying adverse eye conditions in very young children. However, healthcare workers generally learn the RRT using peer practice and do not have the opportunity to observe abnormal reflexes, especially for rarer conditions, during training. The light reflections also differ in appearance between populations due to different pigmentation levels, so effective training requires practice with a diverse population. We have developed a set of 3D model eyes that aim to accurately mimic the response of eyes with varying pigmentation levels in the RRT, both for healthy eyes and pathologies that can be identified using the RRT. We characterized the optical properties of a set of full-color 3D printing materials (a white scattering material and four transparent colors - cyan, magenta, yellow and black). These properties were used to determine the number of layers, layer thicknesses, and color and scattering material combinations needed to match the reflectance of different fundi, given the constraints of the 3Dprinter. The model eyes can be used as an inexpensive tool for training a wide variety of health professionals to recognize abnormal reflections from the eye and as a reference standard for developing or calibrating eye screening instruments and tools.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".