Development of an Eye Model Using 3D-Printing for Correlating Measured Intraocular Pressure with Actual Internal Pressure
Bibliographic record
Abstract
PURPOSE: The aim of this study was to develop a 3D-printed eye model to simulate measuring intraocular pressure (IOP) as a training device, and to assess the correlation between measured IOP using common clinical techniques and actual internal pressure. METHODS: The IOP eye model was designed using CAD software and printed with a resin stereolithography (SLA) 3D-printer (Formlabs 3B, Formlabs Inc., MA, USA). Two clinical instruments, Tono-pen (Tono-Pen AVIA, Reichert Ophthalmic Instruments, USA), and Perkins hand-held tonometer (Clement Clarke Perkins Tonometer Mk2, Vision Equipment Inc., USA) were used for IOP measurements of the model. The pressure within the model was adjusted between 7 to 55 mmHg at 5 mmHg increments, and the IOP values of the tonometry were correlated to the internal pressure displayed on the gauge. RESULTS: < 0.0001). However, aligning the mires and measuring IOP accurately with the Perkins device was challenging. CONCLUSION: The 3D-printed eye model was able to strongly correlate IOP readings taken with a Tono-pen with internal pressure measured by a pressure gauge. The internal pressure of this model can be regulated and is envisioned as a potential model for practicing tonometry at different ranges of pressure.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".