3D, 4D, Mobile APP, VR, AR, and MR Systems in Facial Palsy

Steinbacher, Johannes; Metz, Aaron Antaeus; Tzou, Chieh‐Han John

doi:10.1007/978-3-030-50784-8_38

Cited by 1 publication

(2 citation statements)

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“…Primarily, this analysis provides quantitative criteria that ensure an efficient follow-up for patients with facial pathology, e.g., facial paralysis [5]. Several techniques for assessing facial movement have been developed [6], with a view to quantifying the extent of facial paralysis and facilitating diagnosis and therapy, e.g., plastic or reconstructive surgery. Generally speaking, techniques for assessing facial movement can be categorized as either subjective or objective [7].…”

Section: Introductionmentioning

confidence: 99%

“…In practice, the analysis through 3D scans [6] can be used for planning future maxillofacial surgery [29], soft tissues changes quantification [30] and facial mimic variations of patients before and after treatment [31]. This class can be further subdivided depending on the sensor: laser-based scanning, stereophotogrammetry, structured-light scanning, or RGB-D (red, green, blue-depth) sensors [32].…”

Section: Introductionmentioning

confidence: 99%

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DeepSmile: Anomaly Detection Software for Facial Movement Assessment

et al. 2023

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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.

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Section: Introductionmentioning

confidence: 99%