Quantifying Perceived Facial Asymmetry to Enhance Physician–Patient Communications
In cosmetic surgery, bridging the anticipation gap between the patients and the physicians can be challenging if there lacks objective and transparent information exchange during the decision-making and surgical process. Among all factors, facial symmetry is the most important for assessing facial a...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-09-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/18/8398 |
| _version_ | 1797520370356453376 |
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| author | Shu-Yen Wan Pei-Ying Tsai Lun-Jou Lo |
| author_facet | Shu-Yen Wan Pei-Ying Tsai Lun-Jou Lo |
| author_sort | Shu-Yen Wan |
| collection | DOAJ |
| description | In cosmetic surgery, bridging the anticipation gap between the patients and the physicians can be challenging if there lacks objective and transparent information exchange during the decision-making and surgical process. Among all factors, facial symmetry is the most important for assessing facial attractiveness. The aim of this work is to promote communications between the two parties by providing a quadruple of quantitative measurements: overall asymmetry index (oAI), asymmetry vector, classification, and confidence vector, using an artificial neural network classifier to model people’s perception acquired from visual questionnaires concerning facial asymmetry. The questionnaire results exhibit a Cronbach’s Alpha value of 0.94 and categorize the respondents’ perception of each stimulus face into perceived normal (PN), perceived asymmetrically normal (PAN), and perceived abnormal (PA) categories. The trained classifier yields an overall root mean squared error < 0.01, and its result shows that the oAI is, in general, proportional to the degree of perceived asymmetry. However, there exist faces that are difficult to classify as either PN or PAN or either PAN or PA with competing confidence values. In such cases, oAI alone is not sufficient to articulate facial asymmetry. Assisting surgeon–patient conversations with the proposed asymmetry quadruple is advised to avoid or to mitigate potential medical disputes. |
| first_indexed | 2024-03-10T07:55:48Z |
| format | Article |
| id | doaj.art-1b7b3634b76f4ca6864cbcae6e265e84 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T07:55:48Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-1b7b3634b76f4ca6864cbcae6e265e842023-11-22T11:52:04ZengMDPI AGApplied Sciences2076-34172021-09-011118839810.3390/app11188398Quantifying Perceived Facial Asymmetry to Enhance Physician–Patient CommunicationsShu-Yen Wan0Pei-Ying Tsai1Lun-Jou Lo2Craniofacial Research Center, Chang Gung Memorial Hospital, Taoyuan 333008, TaiwanDepartment of Information Management, Chang Gung University, Taoyuan 333323, TaiwanCraniofacial Research Center, Chang Gung Memorial Hospital, Taoyuan 333008, TaiwanIn cosmetic surgery, bridging the anticipation gap between the patients and the physicians can be challenging if there lacks objective and transparent information exchange during the decision-making and surgical process. Among all factors, facial symmetry is the most important for assessing facial attractiveness. The aim of this work is to promote communications between the two parties by providing a quadruple of quantitative measurements: overall asymmetry index (oAI), asymmetry vector, classification, and confidence vector, using an artificial neural network classifier to model people’s perception acquired from visual questionnaires concerning facial asymmetry. The questionnaire results exhibit a Cronbach’s Alpha value of 0.94 and categorize the respondents’ perception of each stimulus face into perceived normal (PN), perceived asymmetrically normal (PAN), and perceived abnormal (PA) categories. The trained classifier yields an overall root mean squared error < 0.01, and its result shows that the oAI is, in general, proportional to the degree of perceived asymmetry. However, there exist faces that are difficult to classify as either PN or PAN or either PAN or PA with competing confidence values. In such cases, oAI alone is not sufficient to articulate facial asymmetry. Assisting surgeon–patient conversations with the proposed asymmetry quadruple is advised to avoid or to mitigate potential medical disputes.https://www.mdpi.com/2076-3417/11/18/8398artificial neural networksfacial asymmetryplastic surgerymedical disputes |
| spellingShingle | Shu-Yen Wan Pei-Ying Tsai Lun-Jou Lo Quantifying Perceived Facial Asymmetry to Enhance Physician–Patient Communications Applied Sciences artificial neural networks facial asymmetry plastic surgery medical disputes |
| title | Quantifying Perceived Facial Asymmetry to Enhance Physician–Patient Communications |
| title_full | Quantifying Perceived Facial Asymmetry to Enhance Physician–Patient Communications |
| title_fullStr | Quantifying Perceived Facial Asymmetry to Enhance Physician–Patient Communications |
| title_full_unstemmed | Quantifying Perceived Facial Asymmetry to Enhance Physician–Patient Communications |
| title_short | Quantifying Perceived Facial Asymmetry to Enhance Physician–Patient Communications |
| title_sort | quantifying perceived facial asymmetry to enhance physician patient communications |
| topic | artificial neural networks facial asymmetry plastic surgery medical disputes |
| url | https://www.mdpi.com/2076-3417/11/18/8398 |
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