Accessory-free quantitative smartphone imaging of colorimetric paper-based assays
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The combination of smartphone technology and colorimetric paper-based microfluidics can enable simple, inexpensive diagnostics. However, imaging colorimetric diagnostic results via smartphones currently requires accessories to mitigate the influence of variability in surrounding lighting conditions. Here, we present an accessory-free smartphone-based colorimetric imaging method that enlists the built-in LED light source to dominate ambient lighting in combination with background and colour rescaling. This simple approach enables quantitative measurements from paper-based tests by compensating for different environmental lighting conditions and is universally applicable with respect to phone models and manufacturers. We demonstrate the method with three dominant phone makes and models in a cell counting application with a paper-based yeast detection device. The detection results are in good agreement with cell counting using automated cell counters. Eliminating the need for make/model specific accessories, this approach helps realize the potential for low-cost, broadly applicable paper-based diagnostics.
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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) | 0.000 | 0.000 |
| 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) | 0.000 | 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 it