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DOI: 10.1055/s-0041-1729911
An Artificial Intelligence Tool for Image Simulation in Rhinoplasty
Abstract
During rhinoplasty consultations, surgeons typically create a computer simulation of the expected result. An artificial intelligence model (AIM) can learn a surgeon's style and criteria and generate the simulation automatically. The objective of this study is to determine if an AIM is capable of imitating a surgeon's criteria to generate simulated images of an aesthetic rhinoplasty surgery. This is a cross-sectional survey study of resident and specialist doctors in otolaryngology conducted in the month of November 2019 during a rhinoplasty conference. Sequential images of rhinoplasty simulations created by a surgeon and by an AIM were shown at random. Participants used a seven-point Likert scale to evaluate their level of agreement with the simulation images they were shown, with 1 indicating total disagreement and 7 total agreement. Ninety-seven of 122 doctors agreed to participate in the survey. The median level of agreement between the participant and the surgeon was 6 (interquartile range or IQR 5–7); between the participant and the AIM it was 5 (IQR 4–6), p-value < 0.0001. The evaluators were in total or partial agreement with the results of the AIM's simulation 68.4% of the time (95% confidence interval or CI 64.9–71.7). They were in total or partial agreement with the surgeon's simulation 77.3% of the time (95% CI 74.2–80.3). An AIM can emulate a surgeon's aesthetic criteria to generate a computer-simulated image of rhinoplasty. This can allow patients to have a realistic approximation of the possible results of a rhinoplasty ahead of an in-person consultation. The level of evidence of the study is 4.
Publication History
Article published online:
29 May 2021
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References
- 1 Lekakis G, Claes P, Hamilton III GS, Hellings PW. Evolution of preoperative rhinoplasty consult by computer imaging. Facial Plast Surg 2016; 32 (01) 80-87
- 2 Persing S, Timberlake A, Madari S, Steinbacher D. Three-dimensional imaging in rhinoplasty: a comparison of the simulated versus actual Result. Aesthetic Plast Surg 2018; 42 (05) 1331-1335
- 3 Vuyk HD, Stroomer J, Vinayak B. The role of computer imaging in facial plastic surgery consultation: a clinical study. Clin Otolaryngol Allied Sci 1998; 23 (03) 235-243
- 4 Aksakal İA, Keles MK, Engin MS, Aydoğdu İO, Küçüker İ. Preoperative simulation in planning rhinoplasty: evaluation from patients' and surgeons' perspectives. Facial Plast Surg 2017; 33 (03) 324-328
- 5 Chinski H, Chinski L, Armijos J, Arias JP. Rhinoplasty and its effects on the perception of beauty. Int Arch Otorhinolaryngol 2013; 17 (01) 47-50
- 6 Constantian MB. Interactive computer graphics: a new technology to improve judgment in rhinoplasty. Clin Plast Surg 1987; 14 (04) 623-630
- 7 Sharp HR, Tingay RS, Coman S, Mills V, Roberts DN. Computer imaging and patient satisfaction in rhinoplasty surgery. J Laryngol Otol 2002; 116 (12) 1009-1013
- 8 Kayastha D, Vakharia KT. The evolving roles of computer-based technology and smartphone applications in facial plastic surgery. Curr Opin Otolaryngol Head Neck Surg 2019; 27 (04) 267-273
- 9 Larrosa F, Dura MJ, Roura J, Hernandez A. Rhinoplasty planning with an iPhone app: analysis of otolaryngologists response. Eur Arch Otorhinolaryngol 2013; 270 (09) 2473-2477
- 10 Henderson JL, Larrabee Jr WF, Krieger BD. Photographic standards for facial plastic surgery. Arch Facial Plast Surg 2005; 7 (05) 331-333
- 11 Hamet P, Tremblay J. Artificial intelligence in medicine. Metabolism 2017; 69S: S36-S40
- 12 Kanevsky J, Corban J, Gaster R, Kanevsky A, Lin S, Gilardino M. Big data and machine learning in plastic surgery: a new frontier in surgical innovation. Plast Reconstr Surg 2016; 137 (05) 890e-897e
- 13 Knoops PGM, Papaioannou A, Borghi A. et al. A machine learning framework for automated diagnosis and computer-assisted planning in plastic and reconstructive surgery. Sci Rep 2019; 9 (01) 13597
- 14 Yeong E-K, Hsiao T-C, Chiang HK, Lin C-W. Prediction of burn healing time using artificial neural networks and reflectance spectrometer. Burns 2005; 31 (04) 415-420