Yehong Zhong1,2, Sizheng Zhou1, Aiguo Xie1, Dong Li1, Dejun Cao3, Min Wei4, Zheyuan Yu5. 1. Department of Plastic and Reconstructive Surgery, Shanghai 9th People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. 2. Department of Craniomaxillofacial Surgery and Digital Plastic Center, Plastic Surgery Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China. 3. Department of Plastic and Reconstructive Surgery, Shanghai 9th People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. dejuncao@163.com. 4. Department of Plastic and Reconstructive Surgery, Shanghai 9th People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. drweimin@163.com. 5. Department of Plastic and Reconstructive Surgery, Shanghai 9th People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. Zheyuan_yu@qq.com.
Abstract
AIM: To provide referenced classifications of alar dynamic aesthetics from both subjective and objective perspectives for determining proper surgical strategies in alarplasty. METHODS: A total of 150 healthy Asian female participants were instructed to perform two standardized facial movements including a resting pose and a maximum smile while taking care not to show their teeth. The participants were recorded using a dynamic three-dimensional surface imaging system. Frames depicting the resting position and the alar maximum enlargement during the smile were exported separately for anthropometric analysis and classification. The alar dynamic aesthetic was assessed through measurement of the anthropomorphic changes comparing the resting and maximum smile statuses and then transformed into quantitative analysis through the algorithm [Formula: see text]. Subjective classification and evaluation of the subject cosmetic deficiencies and proposals for therapeutic interventions to improve the subjects' alar dynamic aesthetic were performed by three senior plastic surgeons through visualization of the resting and smiling images. The surgeons were asked to divide and classify the subjects into three groups (Class I, Class II and Class III) according to the surgeons' perceptions of degree of the subjects' deficiencies in alar dynamic aesthetic. The more deficiency there was in the aesthetic, the higher the class that the subject was assigned into. The surgeons were presented with the full set of images of the patients on two separate occasions each three months apart, to assess interobserver reliability. Clustering analysis, which is based on machine learning, was applied for objective classification of the images. RESULTS: According to the senior plastic surgeon experts' subjective classification, the subjects' alar flaring mobility was judged as follows: Class I (6.78 ± 3.84%), Class II (10.35 ± 4.18%), and Class III (18.68 ± 4.15%), while alar base mobility was judged as Class I (12.71 ± 7.57%), Class II (20.06 ± 10.06%), and Class III (30.86 ± 13.20%). By clustering analysis, alar flaring mobility was determined to be Class I (7.01 ± 3.51%), Class II (11.18 ± 4.76%), and Class III (12.72 ± 5.66%), while alar base mobility was Class I (9.07 ± 4.23%), Class II (21.88 ± 4.25%), and Class III (38.59 ± 7.08%). No statistical significance was found in the distribution and assignment of classes between the two methodologies. CONCLUSION: Classifications of alar dynamic aesthetics could arouse attention to facial dynamic aesthetics and provide referenced quantitative parameters for plastic surgeons to determine appropriate treatments for alarplasty. For patients with Class I mobility, treatments are not recommended, while minimally invasive treatments can be deemed to be optional for patients with Class II alar mobility to potentially improve alar dynamic aesthetics. For patients with Class III alar mobility, surgical treatments are strongly recommended as options. Combing subjective classification with automated algorithms can provide a novel perspective and improve reliability for facial aesthetic classification analysis. LEVEL OF EVIDENCE IV: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .
AIM: To provide referenced classifications of alar dynamic aesthetics from both subjective and objective perspectives for determining proper surgical strategies in alarplasty. METHODS: A total of 150 healthy Asian female participants were instructed to perform two standardized facial movements including a resting pose and a maximum smile while taking care not to show their teeth. The participants were recorded using a dynamic three-dimensional surface imaging system. Frames depicting the resting position and the alar maximum enlargement during the smile were exported separately for anthropometric analysis and classification. The alar dynamic aesthetic was assessed through measurement of the anthropomorphic changes comparing the resting and maximum smile statuses and then transformed into quantitative analysis through the algorithm [Formula: see text]. Subjective classification and evaluation of the subject cosmetic deficiencies and proposals for therapeutic interventions to improve the subjects' alar dynamic aesthetic were performed by three senior plastic surgeons through visualization of the resting and smiling images. The surgeons were asked to divide and classify the subjects into three groups (Class I, Class II and Class III) according to the surgeons' perceptions of degree of the subjects' deficiencies in alar dynamic aesthetic. The more deficiency there was in the aesthetic, the higher the class that the subject was assigned into. The surgeons were presented with the full set of images of the patients on two separate occasions each three months apart, to assess interobserver reliability. Clustering analysis, which is based on machine learning, was applied for objective classification of the images. RESULTS: According to the senior plastic surgeon experts' subjective classification, the subjects' alar flaring mobility was judged as follows: Class I (6.78 ± 3.84%), Class II (10.35 ± 4.18%), and Class III (18.68 ± 4.15%), while alar base mobility was judged as Class I (12.71 ± 7.57%), Class II (20.06 ± 10.06%), and Class III (30.86 ± 13.20%). By clustering analysis, alar flaring mobility was determined to be Class I (7.01 ± 3.51%), Class II (11.18 ± 4.76%), and Class III (12.72 ± 5.66%), while alar base mobility was Class I (9.07 ± 4.23%), Class II (21.88 ± 4.25%), and Class III (38.59 ± 7.08%). No statistical significance was found in the distribution and assignment of classes between the two methodologies. CONCLUSION: Classifications of alar dynamic aesthetics could arouse attention to facial dynamic aesthetics and provide referenced quantitative parameters for plastic surgeons to determine appropriate treatments for alarplasty. For patients with Class I mobility, treatments are not recommended, while minimally invasive treatments can be deemed to be optional for patients with Class II alar mobility to potentially improve alar dynamic aesthetics. For patients with Class III alar mobility, surgical treatments are strongly recommended as options. Combing subjective classification with automated algorithms can provide a novel perspective and improve reliability for facial aesthetic classification analysis. LEVEL OF EVIDENCE IV: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .
Authors: Chieh-Han John Tzou; Nicole M Artner; Igor Pona; Alina Hold; Eva Placheta; Walter G Kropatsch; Manfred Frey Journal: J Plast Reconstr Aesthet Surg Date: 2014-01-15 Impact factor: 2.740