AI in Assessing Aesthetic Outcomes in Rhinoplasty
Use of Artificial Intelligence in Assessment of 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
調査の概要
詳細な説明
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.
研究の種類
入学 (推定)
段階
- 適用できない
参加基準
適格基準
就学可能な年齢
- 大人
- 高齢者
健康ボランティアの受け入れ
説明
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
研究計画
研究はどのように設計されていますか？
デザインの詳細
- 主な目的：ふるい分け
- 割り当て：ランダム化
- 介入モデル：単一グループの割り当て
- マスキング：なし（オープンラベル）
武器と介入
参加者グループ / アーム |
介入・治療 |
|---|---|
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他の：AI-Based Assessment
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using AI-driven simulations which enable the generation of realistic preoperative predictions, thereby improving surgical planning and patient communication.
|
この研究は何を測定していますか？
主要な結果の測定
結果測定 |
時間枠 |
|---|---|
|
Agreement between ImageJ and AI application measurements
時間枠：basline
|
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
時間枠：basline
|
basline
|
協力者と研究者
スポンサー
出版物と役立つリンク
一般刊行物
- 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.
- 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.
研究記録日
主要日程の研究
研究開始 (推定)
一次修了 (推定)
研究の完了 (推定)
試験登録日
最初に提出
QC基準を満たした最初の提出物
最初の投稿 (実際)
学習記録の更新
投稿された最後の更新 (実際)
QC基準を満たした最後の更新が送信されました
最終確認日
詳しくは
本研究に関する用語
その他の研究ID番号
- UAIAAOR
医薬品およびデバイス情報、研究文書
米国FDA規制医薬品の研究
米国FDA規制機器製品の研究
この情報は、Web サイト clinicaltrials.gov から変更なしで直接取得したものです。研究の詳細を変更、削除、または更新するリクエストがある場合は、register@clinicaltrials.gov。 までご連絡ください。 clinicaltrials.gov に変更が加えられるとすぐに、ウェブサイトでも自動的に更新されます。
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