Can We Trust the Use of Smartphone Cameras in Clinical Practice? Laypeople Assessment of Their Image Quality
Bibliographic record
Abstract
BACKGROUND: Smartphone cameras are rapidly being introduced in medical practice, among other devices for image-based teleconsultation. Little is known, however, about the actual quality of the images taken, which is the object of this study. MATERIALS AND METHODS: A series of nonclinical objects (from three broad categories) were photographed by a professional photographer using three smartphones (iPhone(®) 4 [Apple, Cupertino, CA], Samsung [Suwon, Korea] Galaxy S2, and BlackBerry(®) 9800 [BlackBerry Ltd., Waterloo, ON, Canada]) and a digital camera (Canon [Tokyo, Japan] Mark II). In a Web survey a convenience sample of 60 laypeople "blind" to the types of camera assessed the quality of the photographs, individually and best overall. We then measured how each camera scored by object category and as a whole and whether a camera ranked best using a Mann-Whitney U test for 2×2 comparisons. RESULTS: There were wide variations between and within categories in the quality assessments for all four cameras. The iPhone had the highest proportion of images individually evaluated as good, and it also ranked best for more objects compared with other cameras, including the digital one. The ratings of the Samsung or the BlackBerry smartphone did not significantly differ from those of the digital camera. CONCLUSIONS: Whereas one smartphone camera ranked best more often, all three smartphones obtained results at least as good as those of the digital camera. Smartphone cameras can be a substitute for digital cameras for the purposes of medical teleconsulation.
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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 | 0.010 | 0.110 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".