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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
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| AI-Based Assessment | Other |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| AI | Other | using AI-driven simulations which enable the generation of realistic preoperative predictions, thereby improving surgical planning and patient communication. |
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| Measure | Description | Time Frame |
|---|---|---|
| Agreement between ImageJ and AI application measurements | 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 | basline |
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Inclusion Criteria:
Exclusion Criteria:
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| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 39157708 | Background | Omer G, Girolamo S, Hamatofiq B, Tofiq S, Mohammed MA, Abdulkarim D, Mohammed S, Gubari M, Habibullah I, Mustafa A, Fatah M, Ahmed S, Kakamad F. Functional, Cosmetic, and Psychological Outcomes after Rhinoplasty. Plast Reconstr Surg Glob Open. 2024 Aug 16;12(8):e6057. doi: 10.1097/GOX.0000000000006057. eCollection 2024 Aug. | |
| 35099579 |
| Label | URL |
|---|---|
| Automated Assessment of Aesthetic Outcomes in Facial Plastic Surgery | View source |
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| Zhao R, Chen K, Tang Y. Effects of Functional Rhinoplasty on Nasal Obstruction: A Meta-Analysis. Aesthetic Plast Surg. 2022 Apr;46(2):873-885. doi: 10.1007/s00266-021-02741-2. Epub 2022 Jan 31. |