Facelift Surgery Turns Back the Clock: Artificial Intelligence and Patient Satisfaction Quantitate Value of Procedure Type and Specific Techniques.
Abstract
[BACKGROUND] Patients desire facelifting procedures to look younger, refreshed, and attractive. Unfortunately, there are few objective studies assessing the success of types of facelift procedures and ancillary techniques.
[OBJECTIVES] The authors sought to utilize convolutional neural network algorithms alongside patient-reported FACE-Q outcomes to evaluate perceived age reduction and patient satisfaction following various facelift techniques.
[METHODS] Standardized preoperative and postoperative (1-year) images of patients who underwent facelift procedures were analyzed by 4 neural networks to estimate age reduction after surgery (n = 105). FACE-Q surveys were employed to measure patient-reported facial aesthetic outcome. We compared (1) facelift procedure type: skin-only vs superficial musculoaponeurotic system (SMAS)-plication, vs SMAS-ectomy; and (2) ancillary techniques: fat grafting (malar) vs no fat grafting. Outcomes were based on complications, estimated age-reduction, and patient satisfaction.
[RESULTS] The neural network preoperative age accuracy score demonstrated that all neural networks were accurate in identifying our patients' ages (mean score = 100.4). SMAS-ectomy and SMAS-plication had significantly greater age-reduction (5.85 and 5.35 years, respectively) compared with skin-only (2.95 years, P < 0.05). Fat grafting compared to no fat grafting demonstrated 2.1 more years of age reduction. Facelift procedure type did not affect FACE-Q scores; however, patients who underwent fat grafting had a higher satisfaction with outcome (78.1 ± 8 vs 69 ± 6, P < 0.05) and decision to have the procedure (83.0 ± 6 vs 72 ± 9, P < 0.05).
[CONCLUSIONS] Artificial intelligence algorithms can reliably estimate the reduction in apparent age after facelift surgery. Facelift technique, like SMAS-ectomy or SMAS-plication, and specific technique, like fat grafting, were found to enhance facelifting outcomes and patient satisfaction.
[OBJECTIVES] The authors sought to utilize convolutional neural network algorithms alongside patient-reported FACE-Q outcomes to evaluate perceived age reduction and patient satisfaction following various facelift techniques.
[METHODS] Standardized preoperative and postoperative (1-year) images of patients who underwent facelift procedures were analyzed by 4 neural networks to estimate age reduction after surgery (n = 105). FACE-Q surveys were employed to measure patient-reported facial aesthetic outcome. We compared (1) facelift procedure type: skin-only vs superficial musculoaponeurotic system (SMAS)-plication, vs SMAS-ectomy; and (2) ancillary techniques: fat grafting (malar) vs no fat grafting. Outcomes were based on complications, estimated age-reduction, and patient satisfaction.
[RESULTS] The neural network preoperative age accuracy score demonstrated that all neural networks were accurate in identifying our patients' ages (mean score = 100.4). SMAS-ectomy and SMAS-plication had significantly greater age-reduction (5.85 and 5.35 years, respectively) compared with skin-only (2.95 years, P < 0.05). Fat grafting compared to no fat grafting demonstrated 2.1 more years of age reduction. Facelift procedure type did not affect FACE-Q scores; however, patients who underwent fat grafting had a higher satisfaction with outcome (78.1 ± 8 vs 69 ± 6, P < 0.05) and decision to have the procedure (83.0 ± 6 vs 72 ± 9, P < 0.05).
[CONCLUSIONS] Artificial intelligence algorithms can reliably estimate the reduction in apparent age after facelift surgery. Facelift technique, like SMAS-ectomy or SMAS-plication, and specific technique, like fat grafting, were found to enhance facelifting outcomes and patient satisfaction.
추출된 의학 개체 (NER)
| 유형 | 영어 표현 | 한국어 / 풀이 | UMLS CUI | 출처 | 등장 |
|---|---|---|---|---|---|
| 시술 | facelift
|
안면거상술 | dict | 8 | |
| 해부 | smas
|
표재성근건막계 | dict | 6 | |
| 해부 | superficial musculoaponeurotic system
|
표재성근건막계 | dict | 1 | |
| 해부 | malar
|
광대뼈 | dict | 1 | |
| 해부 | fat
|
scispacy | 1 | ||
| 약물 | ± 9
|
C0205455
Nine
|
scispacy | 1 | |
| 약물 | [BACKGROUND] Patients desire facelifting procedures
|
scispacy | 1 | ||
| 약물 | [OBJECTIVES]
|
scispacy | 1 | ||
| 약물 | 5.35
|
scispacy | 1 | ||
| 약물 | [CONCLUSIONS] Artificial
|
scispacy | 1 | ||
| 기타 | Patient
|
scispacy | 1 | ||
| 기타 | neural network
|
scispacy | 1 | ||
| 기타 | patients
|
scispacy | 1 | ||
| 기타 | neural networks
|
scispacy | 1 | ||
| 기타 | skin-only
|
scispacy | 1 |
MeSH Terms
Artificial Intelligence; Humans; Patient Reported Outcome Measures; Patient Satisfaction; Rhytidoplasty; Superficial Musculoaponeurotic System
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