Matches in SemOpenAlex for { <https://semopenalex.org/work/W4317352014> ?p ?o ?g. }
- W4317352014 endingPage "254" @default.
- W4317352014 startingPage "254" @default.
- W4317352014 abstract "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." @default.
- W4317352014 created "2023-01-19" @default.
- W4317352014 creator A5013217009 @default.
- W4317352014 creator A5016727746 @default.
- W4317352014 creator A5016766848 @default.
- W4317352014 creator A5024270212 @default.
- W4317352014 creator A5028538434 @default.
- W4317352014 creator A5068113274 @default.
- W4317352014 creator A5083162632 @default.
- W4317352014 date "2023-01-10" @default.
- W4317352014 modified "2023-10-18" @default.
- W4317352014 title "DeepSmile: Anomaly Detection Software for Facial Movement Assessment" @default.
- W4317352014 cites W1550236514 @default.
- W4317352014 cites W1929777505 @default.
- W4317352014 cites W1984849553 @default.
- W4317352014 cites W1988799283 @default.
- W4317352014 cites W1995203229 @default.
- W4317352014 cites W2013475500 @default.
- W4317352014 cites W2019658145 @default.
- W4317352014 cites W2024799401 @default.
- W4317352014 cites W2041084691 @default.
- W4317352014 cites W2048225593 @default.
- W4317352014 cites W2052153644 @default.
- W4317352014 cites W2059956646 @default.
- W4317352014 cites W2064629738 @default.
- W4317352014 cites W2064675550 @default.
- W4317352014 cites W2072750628 @default.
- W4317352014 cites W2079735306 @default.
- W4317352014 cites W2082148676 @default.
- W4317352014 cites W2102269622 @default.
- W4317352014 cites W2103269450 @default.
- W4317352014 cites W2110485445 @default.
- W4317352014 cites W2112796928 @default.
- W4317352014 cites W2130255526 @default.
- W4317352014 cites W2148113031 @default.
- W4317352014 cites W2168191473 @default.
- W4317352014 cites W2314297245 @default.
- W4317352014 cites W2342327831 @default.
- W4317352014 cites W2346111938 @default.
- W4317352014 cites W2411757440 @default.
- W4317352014 cites W2521182167 @default.
- W4317352014 cites W2554413037 @default.
- W4317352014 cites W2620358522 @default.
- W4317352014 cites W2698180911 @default.
- W4317352014 cites W2809279586 @default.
- W4317352014 cites W2887990646 @default.
- W4317352014 cites W2896400928 @default.
- W4317352014 cites W2900489240 @default.
- W4317352014 cites W2913005111 @default.
- W4317352014 cites W2944851425 @default.
- W4317352014 cites W2945074464 @default.
- W4317352014 cites W2955024498 @default.
- W4317352014 cites W2964336507 @default.
- W4317352014 cites W2966841471 @default.
- W4317352014 cites W2976574580 @default.
- W4317352014 cites W3004711626 @default.
- W4317352014 cites W3016108562 @default.
- W4317352014 cites W3081372655 @default.
- W4317352014 cites W3085356846 @default.
- W4317352014 cites W3106883650 @default.
- W4317352014 cites W3111082827 @default.
- W4317352014 cites W3129367324 @default.
- W4317352014 cites W3135969576 @default.
- W4317352014 cites W3170851865 @default.
- W4317352014 cites W4285305347 @default.
- W4317352014 cites W4293217532 @default.
- W4317352014 cites W4294875228 @default.
- W4317352014 cites W4306835819 @default.
- W4317352014 cites W4315436691 @default.
- W4317352014 cites W578546646 @default.
- W4317352014 doi "https://doi.org/10.3390/diagnostics13020254" @default.
- W4317352014 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36673064" @default.
- W4317352014 hasPublicationYear "2023" @default.
- W4317352014 type Work @default.
- W4317352014 citedByCount "0" @default.
- W4317352014 crossrefType "journal-article" @default.
- W4317352014 hasAuthorship W4317352014A5013217009 @default.
- W4317352014 hasAuthorship W4317352014A5016727746 @default.
- W4317352014 hasAuthorship W4317352014A5016766848 @default.
- W4317352014 hasAuthorship W4317352014A5024270212 @default.
- W4317352014 hasAuthorship W4317352014A5028538434 @default.
- W4317352014 hasAuthorship W4317352014A5068113274 @default.
- W4317352014 hasAuthorship W4317352014A5083162632 @default.
- W4317352014 hasBestOaLocation W43173520141 @default.
- W4317352014 hasConcept C107038049 @default.
- W4317352014 hasConcept C107457646 @default.
- W4317352014 hasConcept C113843644 @default.
- W4317352014 hasConcept C119857082 @default.
- W4317352014 hasConcept C121332964 @default.
- W4317352014 hasConcept C129307140 @default.
- W4317352014 hasConcept C12997251 @default.
- W4317352014 hasConcept C138885662 @default.
- W4317352014 hasConcept C154945302 @default.
- W4317352014 hasConcept C157915830 @default.
- W4317352014 hasConcept C173608175 @default.
- W4317352014 hasConcept C195704467 @default.
- W4317352014 hasConcept C199360897 @default.
- W4317352014 hasConcept C2524010 @default.