Intelligent augmented reality application for personalised rhinoplasty using machine learning

Heydari, MS, Kolivand, M, Al-Azzawi, M and Kolivand, H (2025) Intelligent augmented reality application for personalised rhinoplasty using machine learning. Intelligence and Robotics, 5 (2). pp. 355-377. ISSN 2770-3541

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Abstract

Rhinoplasty, a common yet complex cosmetic surgery, often results in patient dissatisfaction due to the reliance on subjective surgeon evaluations. This study introduces an intelligent augmented reality (AR) application for personalised nose surgery, integrating three core innovations: (1) Preoperative 3D Modelling; (2) Machine Learning (ML) Analysis; and (3) AR Visualisation. The system employs advanced computer vision algorithms to extract precise facial measurements from high-resolution 3D scans or photographs. These measurements are analysed using ML techniques to calculate key facial ratios and recommend optimal nose shapes tailored to individual facial structures. AR further enhances the surgical process by providing real-time visualisations and guidance, enabling surgeons to implement data-driven decisions with greater precision. This novel approach addresses key challenges in rhinoplasty by automating critical steps of the surgical planning process, reducing subjectivity, and significantly improving surgical accuracy. The application’s contribution extends beyond the operating room, offering surgeons a powerful educational tool with real-time feedback and interactive visualisations to support continuous skill development. This study represents a transformative step in leveraging AR and ML for enhanced precision, patient satisfaction, and surgical outcomes in cosmetic surgery.

Item Type:	Article
Uncontrolled Keywords:	46 Information and Computing Sciences; 4608 Human-Centred Computing; 32 Biomedical and Clinical Sciences; Patient Safety; Machine Learning and Artificial Intelligence; Bioengineering; Networking and Information Technology R&D (NITRD); 6.4 Surgery; 3 Good Health and Well Being
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science R Medicine > RD Surgery
Divisions:	Computer Science and Mathematics
Publisher:	OAE Publishing Inc.
Date of acceptance:	31 March 2025
Date of first compliant Open Access:	29 May 2025
Date Deposited:	29 May 2025 10:30
Last Modified:	29 May 2025 10:45
DOI or ID number:	10.20517/ir.2025.18
URI:	https://researchonline.ljmu.ac.uk/id/eprint/26457

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