Design and Validation of a Low-Cost, Open-Source, 3D-Printed Otoscope
Bibliographic record
Abstract
Abstract The modern otoscope is an indispensable instrument utilized by primary care physicians as the gold standard tool to diagnose an array of otologic diseases and conditions. At present, commercially available, traditional otoscopes remain cost-prohibitive to many potential users despite limited innovation since its invention in the early 19 th century. In this publication, the design and assembly of a low-cost, open-source, 3D-printed otoscope, the Glia Otoscope V1.0, is outlined. Subsequently, we describe the benchtop evaluation conducted, which measured several outcomes relevant to otoscopy performance against a traditional, gold standard otoscope, the Welch Allyn Rechargeable V3.5 Halogen HPX Otoscope. Measured outcomes included illuminance, correlated color temperature, color rendering index, spatial resolution, field of view, weight, battery life, and cost. Overall, the Glia Otoscope V1.0 demonstrated comparable performance across measured outcomes against the traditional otoscope. Further validation in the clinical setting is warranted as the Glia Otoscope V1.0 and its future iterations hold tremendous potential in improving access and alleviating the burden of otologic disease in lower and middle-income countries. Finally, we present a novel tool, the Otoscope Assessment Tool, which establishes a standard set of performance characteristics for benchtop evaluation of otoscope performance.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".