How Should the Cellphone Be Used to Obtain Good Pictures for Rhinoplasty?
Bibliographic record
Abstract
Standardized, high-quality, preoperative photographs of the nose are of utmost importance for preoperative rhinoplasty planning, comparative postoperative assessment, and demonstration of surgical results. Photographic standards for using digital single-lens reflex cameras have been well documented in the literature.1–3 Recently, cellphone cameras have made great progress. Despite the fact that cellphones are being used more often in rhinoplasty photography, to our knowledge, the standards for using them have not been addressed much in the literature. In this study, six standard rhinoplasty pictures were taken of five subjects. In addition, frontal, lateral, and basal views were photographed while the patient was holding a ruler near the face for further millimetric analysis using Rhinobase 2.0 software.4 The following values were measured and compared with direct nasal measurements: tip width, base bony width, dorsum width, interalar width, and nasal length. Photographic documentation was realized using the camera of the iPhone X (Apple, Inc., Cupertino, Calif.), first with the use of two continuous LED lights and then with ordinary room lights. The cellphone was placed at five different distances with five different zoom values: 150 cm at 5× zoom, 115 cm at 4× zoom, 80 cm at 3× zoom, 45 cm at 2× zoom, and 30 cm at 1× zoom. The head and neck filled the screen in all the views. After a very careful statistical analysis of the results, changing the zoom value had a different effect on the studied parameters, with the same pattern seen in room and LED light. The obtained data showed that the tip width values decreased when the zoom value increased. Statistically, the percentage of deviation from reality was significantly increased by increasing the zoom value (p < 0.0001). The same pattern was noted for the dorsal width values (p < 0.0001). Regarding nasal length, bony base width, and interalar width, the values would decrease when the zoom value decreased, and the percentage of deviation from reality also decreased when the zoom values increased (with p values of <0.0001, <0.0226, and <0.0001, respectively). Accordingly, nasal length was the most affected parameter, while the bony base width was the least affected. It was found that at 2.5× zoom, the parameter values would be the closest to the reality measurements. After observing the quality of the images, the resolution of the picture decreased when the digital zoom increased, while the distortion decreased when the zoom value increased (Fig. 1). In addition, changing the light source did not significantly affect image quality.Fig. 1.: Frontal views taken by cellphone camera in LED light conditions with different zoom values. From left to right, the first picture was taken at 1× zoom, the second at 2.5× zoom, and the third at 5× zoom.In conclusion, satisfactory pictures can be obtained by using cellphone cameras set at 2.5× zoom value while keeping a distance of 65 cm from the object. To preserve standardization, the pictures should be taken in a studio setting that includes two LED lights, a blue background, a rotating chair, and a tripod for the cellphone. Additional advantages of using a cellphone camera are cost-effectiveness, ease of handling, portability, and easier connectivity to other devices and the internet. SUBJECT CONSENT The subject gave written consent for the use of his images. ACKNOWLEDGMENT The authors acknowledge Semiha Ozgul, Department of Biostatistics and Medical Informatics, Ege University Faculty of Medicine. DISCLOSURE The authors have no financial interest to declare in relation to the content of this article.
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How this classification was reachedexpand
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 | 0.001 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".