How Should the Cellphone Be Used to Obtain Good Pictures for Rhinoplasty?
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,065 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».