Abstract:IMPORTANCE Study of the association of regional facial dysfunction with quality of life will lead to a better understanding of quality of life in facial palsy.OBJECTIVE To determine the association of regional facial dysfunction with facial palsy-related quality of life.
DESIGN, SETTING, AND PARTICIPANTSThis retrospective cohort analysis included patients with flaccid and nonflaccid (synkinetic) facial palsy treated at a tertiary care facial nerve center; the flaccid facial palsy group included 529 patients, a… Show more
“…In this study we have shown that patient-perceived synkinesis severity contributed significantly to the statistical prediction of facial palsy-related quality of life.Other variables predictive of facial palsy-related quality of life were eFace total score, gender and type of visit. The eFace total score explained 25.2% of the FaCE total score, a finding similar to other literature reports 6,10. Likewise, lower quality of life scores in female patients compared to males and the small proportional…”
supporting
confidence: 89%
“…Specifically, statistical prediction of quality of life for patients with non-flaccid facial palsy had a lower explained variance compared to that of flaccid facial palsy patients. 6 The aim of this study was to analyse whether the addition of patient-perceived severity of synkinesis to clinician-graded facial function permits better prediction of quality of life in facial palsy patients.…”
Section: Patient-perceived Severity Of Synkinesis Reduces Quality Of mentioning
“…In this study we have shown that patient-perceived synkinesis severity contributed significantly to the statistical prediction of facial palsy-related quality of life.Other variables predictive of facial palsy-related quality of life were eFace total score, gender and type of visit. The eFace total score explained 25.2% of the FaCE total score, a finding similar to other literature reports 6,10. Likewise, lower quality of life scores in female patients compared to males and the small proportional…”
supporting
confidence: 89%
“…Specifically, statistical prediction of quality of life for patients with non-flaccid facial palsy had a lower explained variance compared to that of flaccid facial palsy patients. 6 The aim of this study was to analyse whether the addition of patient-perceived severity of synkinesis to clinician-graded facial function permits better prediction of quality of life in facial palsy patients.…”
Section: Patient-perceived Severity Of Synkinesis Reduces Quality Of mentioning
“…Furthermore, function of individual facial regions seems not equally important for estimating facial palsy-related quality of life. The ability to smile seems to be of greatest importance (25). Therefore, one has to be careful with direct comparisons of the results of the different tools.…”
Background: To evaluate the face-specific quality of life after hypoglossal-facial jump nerve suture for patients with long-term facial paralysis. Methods: A single-center retrospective cohort study was performed. Forty-one adults (46% women; median age: 55 years) received a hypoglossal-facial jump nerve suture. Sunnybrook and eFACE grading was performed before surgery and at a median time of 42 months after surgery. The Facial Clinimetric Evaluation (FaCE) survey and the Facial Disability Index (FDI) were used to quantify face-specific quality of life after surgery. Results: Hypoglossal-facial jump nerve suture was successful in all cases without tongue dysfunction. After surgery, the median FaCE Total score was 60 and the median FDI Total score was 76.3. Most Sunnybrook and eFACE grading subscores improved significantly after surgery. Younger age was the only consistent independent predictor for better FaCE outcome. Additional upper eyelid weight loading further improved the FaCE Eye comfort subscore. Sunnybrook grading showed a better correlation to FaCE assessment than the eFACE. Neither Sunnybrook nor eFACE grading correlated to the FDI assessment. Conclusion: The hypoglossal-facial jump nerve suture is a good option for nerve transfer to reanimate the facial muscles to improve facial motor function and face-specific quality of life.
“…These results agree with our understanding of the typical sequelae associated with cerebrovascular accidents. First, stroke survivors typically develop unilateral facial paralysis [56], which is characterized by decreased facial symmetry during movement, and affects the patients' ability to smile [57]. Second, speech movements are commonly affected in stroke [58].…”
Section: B Detection Of Neurological Diseasesmentioning
Background: Automatic facial landmark localization is an essential component in many computer vision applications, including video-based detection of neurological diseases. Machine learning models for facial landmarks localization are typically trained on faces of healthy individuals, and we found that model performance is inferior when applied to faces of people with neurological diseases. Fine-tuning pre-trained models with representative images improves performance on clinical populations significantly. However, questions related to the characteristics of the database used to fine-tune the model and the clinical impact of the improved model remain. Methods: We employed the Toronto NeuroFace dataset – a dataset consisting videos of Healthy Controls (HC), individuals Post-Stroke, and individuals with Amyotrophic Lateral Sclerosis performing speech and non-speech tasks with thousands of manually annotated frames - to fine-tune a well-known deep learning-based facial landmark localization model. The pre-trained and fine-tuned models were used to extract landmark-based facial features from videos, and the facial features were used to discriminate clinical groups from HC. Results: Fine-tuning a facial landmark localization model with a diverse database that includes HC and individuals with neurological disorders resulted in significantly improved performance for all groups. Our results also showed that fine-tuning the model with representative data greatly improved the ability of the subsequent classifier to classify clinical groups vs. HC from videos. Conclusions: Using a diverse database for model fine-tuning might result in better model performance for HC and clinical groups. We demonstrated that fine-tuning a model for landmark localization with representative data results in improved detection of neurological diseases.
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