Optimizing Color Performance of the Ngenuity 3-Dimensional Visualization System
Bibliographic record
Abstract
Purpose To evaluate the effect of surgeon-controlled parameters on the color performance of the Ngenuity 3-dimensional (3D) visualization system. Design A calibrated reference target was placed inside a model eye to assess the Ngenuity 3D camera under different settings. The Ngenuity 3D display was assessed with a commercial colorimeter. Methods Manufacturer-recommended methodology for white balancing was compared against all common deviations in technique. Following white balance, images of a calibrated reference target were extracted and tested using Imatest Master software to calculate quantitative color differences (delta E and delta C). The Ngenuity monitor was assessed using a SpyderX Elite commercial colorimeter to assess for image burn-in by quantifying color uniformity and maximum luminescence. Main Outcome Measures Delta E and delta C were calculated for all variables. Color uniformity and luminance were assessed in candelas per square meter (nits). Results Color performance using the manufacturer-recommended specifications yielded a delta E of 12.81 ± 1.67. Changing the white balance target to a videography grey card ( P = 0.07) and 4 × 4 gauze ( P = 0.37) provided similar performance, whereas using white computer paper or the operator's palm significantly increased the delta E from 12.81 ± 1.67 to 15.28 ± 1.22 ( P = 0.01) and 17.71 ± 2.03 ( P < 0.01), respectively. Changes to card position, magnification, stability, or ambient lighting did not significantly impact white balance results, whereas having the card in crisp focus did decrease color accuracy (15.78 ± 1.63; P = 0.03). Minor improvement in performance occurred when the laser filter was off for white balance and image acquisition (9.28 ± 0.25; P < 0.01), but deterioration occurred if the laser filter was placed after balancing (16.59 ± 1.17; P < 0.01). Both light sources of 23-gauge light pipe at 34% intensity and 25-gauge chandelier at 50% intensity gave similar color accuracy ( P = 0.37). When comparing different Ngenuity machines, color uniformity and maximum luminescence decreased with increased device use. Conclusions Overall, the Ngenuity 3D has robust color performance. A few limitations of both the camera and monitor were identified, and surgeons should be aware of these pitfalls as well as solutions examined herein to mitigate their effects during surgery.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".