AI in Assessing Aesthetic Outcomes in Rhinoplasty

This study aims to thoroughly assess the predictive accuracy of artificial intelligence-based nasal outcome simulations by comparing AI-generated preoperative predictions with objective postoperative nasal morphology using digital image analysis.

To assess accuracy of AI-image measurement compared with imageJ software

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Key information

Conditions

Rhinoplasty AI (Artificial Intelligence)

Age range

18 year and older

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

About this study

Rhinoplasty is a surgical procedure that aims to enhance nasal aesthetics while preserving structural integrity and function. It focuses on minimizing tissue disruption through techniques such as cartilage reshaping, selective preservation, and grafting to maintain support. The primary goal is to achieve natural-looking outcomes while ensuring adequate nasal breathing and reducing postoperative complications.

Despite its widespread application, rhinoplasty remains one of the most complex procedures in aesthetic surgery due to the variability in individual anatomy and patient expectations. Conventional standardized approaches often fail to fully address these differences. Subjective assessment tools, including patient-reported outcome measures, provide insight into satisfaction with aesthetic and functional results; however, they are limited by lack of objectivity. Zojaji et al. demonstrated no strong correlation between objective facial proportion changes and Rhinoplasty Outcome Evaluation (ROE) scores, emphasizing the discrepancy between perceived and measured outcomes.

Recent advances in artificial intelligence (AI) have introduced innovative solutions to these challenges. AI-driven simulations enable the generation of realistic preoperative predictions, thereby improving surgical planning and patient communication.Furthermore, AI-based image analysis applications allow for precise and automated measurement of nasal parameters, including linear distances, angles, proportions, and symmetry, using standardized digital photographs. These tools provide objective and reproducible data, reduce observer variability, and enhance the accuracy of postoperative outcome assessment.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

Patients age> 18 years old.
patients schedule for rhinoplasty surgery

Exclusion criteria

Pervious nasal trauma that affect anatomical land mark
pervious nasal surgery (rhinoplasty or others)
patients with psychological disorders.
patients with any coagulopathy disorders

Treatment and study plan

AI

Other

using AI-driven simulations which enable the generation of realistic preoperative predictions, thereby improving surgical planning and patient communication.

Primary outcomes

Agreement between ImageJ and AI application measurements

Time frame: basline
Evaluate the accuracy of AI-based simulation in predicting postoperative aesthetic outcomes following structural rhinoplasty by comparing AI-generated preoperative simulations with actual postoperative nasal morphology using objective digital image a

Time frame: basline

Sponsors and collaborators

Lead sponsor

Assiut University

Other

Registry information

Official study title

Use of Artificial Intelligence in Assessment of Aesthetic Outcomes in Rhinoplasty

Important dates

Study start: 2026
Primary completion: 2027
Study completion: 2027
First posted: May 12, 2026
Registry last updated: May 12, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

Published trials that share one or more normalized conditions with this study.

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