Rodríguez Martínez Eder A, Polezhaeva Olga, Marcellin Félix, Colin Émilien, Boyaval Lisa, Sarhan François-Régis, Dakpé Stéphanie
UR 7516 Laboratory CHIMERE, University of Picardie Jules Verne, 80039 Amiens, France.
Institut Faire Faces, 80000 Amiens, France.
Diagnostics (Basel). 2023 Jan 10;13(2):254. doi: 10.3390/diagnostics13020254.
Facial movements are crucial for human interaction because they provide relevant information on verbal and non-verbal communication and social interactions. From a clinical point of view, the analysis of facial movements is important for diagnosis, follow-up, drug therapy, and surgical treatment. Current methods of assessing facial palsy are either (i) objective but inaccurate, (ii) subjective and, thus, depending on the clinician's level of experience, or (iii) based on static data. To address the aforementioned problems, we implemented a deep learning algorithm to assess facial movements during smiling. Such a model was trained on a dataset that contains healthy smiles only following an anomaly detection strategy. Generally speaking, the degree of anomaly is computed by comparing the model's suggested healthy smile with the person's actual smile. The experimentation showed that the model successfully computed a high degree of anomaly when assessing the patients' smiles. Furthermore, a graphical user interface was developed to test its practical usage in a clinical routine. In conclusion, we present a deep learning model, implemented on open-source software, designed to help clinicians to assess facial movements.
面部运动对人际互动至关重要，因为它们提供了有关言语和非言语交流以及社会互动的相关信息。从临床角度来看，面部运动分析对于诊断、随访、药物治疗和手术治疗都很重要。当前评估面瘫的方法要么（i）客观但不准确，（ii）主观，因此取决于临床医生的经验水平，要么（iii）基于静态数据。为了解决上述问题，我们实施了一种深度学习算法来评估微笑时的面部运动。这样的模型是在一个仅包含健康微笑的数据集上按照异常检测策略进行训练的。一般来说，异常程度是通过将模型建议的健康微笑与个人实际微笑进行比较来计算的。实验表明，该模型在评估患者微笑时成功计算出了高度异常。此外，还开发了一个图形用户界面来测试其在临床常规中的实际应用。总之，我们展示了一个在开源软件上实现的深度学习模型，旨在帮助临床医生评估面部运动。
