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Simulation and Artificial Intelligence in Rhinoplasty: A Systematic Review

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  • Rhinoplasty
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Aesthetic Plastic Surgery Aims and scope Submit manuscript

Abstract

Background

Rhinoplasty is one of the most popular cosmetic procedures. The complexity of the nasal structure and the substantial aesthetic and functional impact of the operation make rhinoplasty very challenging.

The past few years have witnessed an increasing implementation of artificial intelligence (AI) and simulation systems into plastic surgery practice. This review explores the potential uses of AI and simulation models in rhinoplasty.

Methods

Five electronic databases were searched: PubMed, CINAHL, EMBASE, Scopus, and Web of Science. We used the Preferred Reporting Items for Systematic Reviews and Meta-Analysis as our basis of organization.

Results

Several simulation models were described to predict the nasal shape that aesthetically matches the patient's face, indicate the implant size in augmentation rhinoplasty and construct three-dimensional (3D) facial images from two-dimensional images.

Machine learning was used to learn surgeons' rhinoplasty styles and accurately simulate the outcomes. Deep learning was used to predict rhinoplasty status accurately and analyze the factors associated with increased facial attractiveness after rhinoplasty. Finally, a deep learning model was used to predict patients' age before and after rhinoplasty proving that the procedure made the patients look younger.

Conclusion

3D simulation models and AI models can revolutionalize the practice of functional and aesthetic rhinoplasty. Simulation systems can be beneficial in preoperative planning, intra-operative decision making, and postoperative evaluation. In addition, AI models can be trained to carry out tasks that are either challenging or time-consuming for surgeons.

Level of Evidence III

This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266.

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Acknowledgments

Figures 2, 3, and 4 were created using BioRender.com. Patient images used in figure 2 were extracted from the Centers for Disease Control (CDC) Public Health Image Library (ID # 15456 & ID # 23097). These images are in the public domain and thus free of any copyright restrictions.

Funding

This study was supported in part by the Mayo Clinic Center for Regenerative Medicine.

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Authors and Affiliations

  1. Division of Plastic Surgery, Mayo Clinic, 4500 San Pablo Rd, Jacksonville, FL, 32224, USA

    Abdullah S. Eldaly, Francisco R. Avila, Ricardo A. Torres-Guzman, Karla Maita, John P. Garcia, Luiza Palmieri Serrano & Antonio J. Forte

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  1. Abdullah S. Eldaly
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Correspondence to Antonio J. Forte.

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Eldaly, A.S., Avila, F.R., Torres-Guzman, R.A. et al. Simulation and Artificial Intelligence in Rhinoplasty: A Systematic Review. Aesth Plast Surg 46, 2368–2377 (2022). https://doi.org/10.1007/s00266-022-02883-x

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