Explore the vast range of titles available.

Background Hypophasis is one of the most frequently observed sequelae of patients with Bell’s palsy, who have not recovered completely, creating a clinical difficulty for physicians. Acupuncture therapy has been widely used to treat Bell’s palsy as a reasonable resolution for management of symptoms such as hypophasis. The number of acupuncture points (acu-points) is frequently selected in the approach of acupuncture therapy; however, whether these had high efficiency has not been proved. According to the literature review, Bi’nao was useful for treating eye and eye lipid diseases, which could be proved only by some successful cases. Thus, a randomized controlled trial was designed to evaluate the efficiency of the acu-point Bi’nao. Methods/Design Participants with hypophasis as the major symptom are selected among patients with Bell’s palsy and randomly allocated into one of the three groups at a 1:1:1 allocation ratio. All participants receive conventional acupuncture therapy; however, those assigned to the real acupuncture group will be given added acupuncture therapy on the acu-point Bi’nao, while those assigned to the sham acupuncture group were given extra acupuncture therapy on the sham Bi’nao as a placebo. The efficacy of the acupuncture therapy on the acu-point Bi’nao for hypophasis will be evaluated by Eye Crack Width Measurement (ECWM) and Eyelid Strength Assessment (ESA) before and after therapy. Discussion This is the first study assessing the safety and efficiency of Bi’nao in treating the hypophasis of patients with Bell’s palsy that might support the application of this acupuncture therapy. However, evaluating hypophasis is challenging, and, thus, ECWM and ESA were applied to measure the eyelid movement. Trial registration Chinese Clinical Trials Registry, ChiCTR-INR-17012955 . Registered on 12 October 2017.

This study aimed to determine the preferred timing and measurement sites for electroneuronography (ENoG) to predict early recovery from acute peripheral facial paralysis. We retrospectively evaluated 42 patients with acute peripheral facial paralysis who received standard treatment with oral corticosteroids. The severity of facial paralysis was assessed at the initial visit and after 1 month using the House-Brackmann grading system. Patients were classified into recovery and non-recovery groups according to changes in the grade. ENoG was performed at the initial visit and after 2 weeks. ENoG amplitudes of four facial muscles (frontalis, nasalis, orbicularis oculi, and orbicularis oris) at the initial visit and after 2 weeks, as well as age, sex, affected side, and diagnosis, were compared between the two groups. No differences were observed in degeneration ratios across all subsites in the initial ENoG, which can be explained by the fact that Wallerian degeneration is not yet complete at this early stage. However, the second ENoG, performed after degeneration had progressed, showed significant differences across all subsites. Binary logistic regression analysis revealed that the degeneration ratio of the orbicularis oris muscle was the best predictor of early recovery (odds ratio, 0.961; p = 0.014). Receiver operating characteristic curve analysis also revealed that the degeneration ratios of all subsites measured in the second ENoG were useful in predicting early recovery, with the highest possibility at the orbicularis oris muscle (area under the curve = 0.789). When the degeneration ratio exceeded 60% in all subsites in the second ENoG, a favorable prognosis was not expected. This study provides the preferred testing time and measurement sites for ENoG to predict early recovery from facial paralysis. Given the personal and social impact of facial paralysis, predicting early recovery is crucial for reassuring patients, providing better treatment, and encouraging early reintegration into society.

With the continuous progress of technology, the subject of life science plays an increasingly important role, among which the application of artificial intelligence in the medical field has attracted more and more attention. Bell facial palsy, a neurological ailment characterized by facial muscle weakness or paralysis, exerts a profound impact on patients’ facial expressions and masticatory abilities, thereby inflicting considerable distress upon their overall quality of life and mental well-being. In this study, we designed a facial attribute recognition model specifically for individuals with Bell’s facial palsy. The model utilizes an enhanced SSD network and scientific computing to perform a graded assessment of the patients’ condition. By replacing the VGG network with a more efficient backbone, we improved the model’s accuracy and significantly reduced its computational burden. The results show that the improved SSD network has an average precision of 87.9% in the classification of light, middle and severe facial palsy, and effectively performs the classification of patients with facial palsy, where scientific calculations also increase the precision of the classification. This is also one of the most significant contributions of this article, which provides intelligent means and objective data for future research on intelligent diagnosis and treatment as well as progressive rehabilitation.

Objective To systematically review the published cases of bilateral facial palsy (BFP) to gather evidence on the clinical assessment and management of this pathology. Methods Following PRISMA statement recommendations, 338 abstracts were screened independently by two authors. Inclusion criteria were research articles of human patients affected by BFP, either central or peripheral; English, Italian, French or Spanish language; availability of the abstract, while exclusion criteria were topics unrelated to FP, and mention of unilateral or congenital FP. Only full-text articles reporting the diagnostic work-up, the management, and the prognosis of the BFP considered for further specific data analysis. Results A total of 143 articles were included, resulting a total of 326 patients with a mean age of 36 years. The most common type of the paralysis was peripheral (91.7%), and the autoimmune disease was the most frequent aetiology (31.3%). The mean time of onset after first symptoms was 12 days and most patients presented with a grade higher than III. Associated symptoms in idiopathic BFP were mostly non-specific. The most frequently positive laboratory exams were cerebrospinal fluid analysis, autoimmune screening and peripheral blood smear, and the most performed imaging was MRI. Most patients (74%) underwent exclusive medical treatment, while a minority were selected for a surgical or combined approach. Finally, in more than half of cases a complete bilateral recovery (60.3%) was achieved. Conclusions BFP is a disabling condition. If a correct diagnosis is formulated, possibilities to recover are elevated and directly correlated to the administration of an adequate treatment.

BackgroundFacial nerve palsy is the most common cranial nerve disorder. There is no consensus on a single diagnostic tool deemed as the ‘gold standard’ for distinguishing between idiopathic (Bell’s palsy) and symptomatic causes. The diagnosis is one of exclusion and most often made on physical examination. In the present study, we describe the etiological background of peripheral facial palsy in N = 509 patients and evaluate the relevance of cerebrospinal fluid (CSF) analysis and magnetic resonance imaging (MRI) in differential diagnosis.MethodsWe carried out a retrospective data analysis of 509 patients with the clinical diagnosis of peripheral facial palsy admitted to our emergency unit between January 2006 and January 2017. All patients were seen clinically; their CSF was analyzed and MRI was performed.ResultsOf N = 526 patients with isolated facial palsy, 17 patients were excluded because they did not consent to CSF analysis. Of the remaining N = 509 patients, 383 patients (75.2%) were diagnosed with idiopathic facial palsy. In the remaining 126 patients (24.8%), the following etiologies for facial palsy could be found: Ramsay-Hunt-Syndrome (N = 34), Lyme Neuroborreliosis (N = 32), other viral/bacterial central nervous system (CNS) infections (N = 8), neoplasias (N = 18), autoimmune disease (N = 12), otogenous processes (N = 6), or other etiologies (N = 16). Analysis of the CSF showed 85% sensitivity for Ramsay-Hunt-Syndrome and 100% for Lyme Neuroborreliosis and other viral/bacterial CNS infections. CSF analysis proved a reliable diagnostic tool for identifying these subgroups. MRI with contrast compounds, as performed in 409 patients, was the most important tool in diagnosing neoplasias (88% sensitivity) and otogenous processes (83% sensitivity). MRI with contrast-enhancing compounds did not reveal additional information concerning inflammatory facial nerve lesions when performed the same day as hospital admission.ConclusionsAlthough peripheral facial palsy was predominantly idiopathic (75.3%) in our cohort, the disease was caused in approximately 25% of the patients by factors which require specific treatment. In the present study, CSF analysis proved to be the leading method for the diagnosis of Ramsay-Hunt-Syndrome, Lyme Neuroborreliosis, and other CNS infections. These subgroups made up approximately 15% of our cohort. To detect these subgroups reliably, routine use of CSF analysis in peripheral facial palsy may be advisable, whereas MRI proved to be useful for exclusion of otogenic and neoplastic processes with a sensitivity of 83% and 88%. We found that the use of MRI with contrast-enhancing compounds does not provide additional diagnostic information on the day of hospital admission. Hence, the potential benefits of routine use of MRI in patients with facial nerve palsy should be weighed against health care cost factors.

Facial palsy (FP) impairs voluntary control of facial muscles, resulting in facial asymmetry and difficulties in emotional expression. Traditional assessment methods to define the severity of FP (e.g., House–Brackmann score, HB) rely on visual examinations and, therefore, are highly examiner-dependent. This study proposes an alternative approach using facial surface electromyography (EMG) for automated HB prediction. Time-domain EMG features were extracted during different facial movements (i.e., smile, close eyes, and raise forehead) and analyzed through nine different machine learning (ML) models in 58 subjects (51.98 ± 1.67 years, 20 male) with variable facial nerve function (HB 1: n = 16, HB 2–3: n = 32; HB 4–6: n = 10). Model performances were evaluated based on accuracy, precision, recall, and F1-score. Among the evaluated models, ensemble-based approaches—particularly a random forest model with 100 trees and a decision tree ensemble—proved to be the most effective with classification accuracies ranging from 81.7 to 84.8% and from 81.7 to 84.7%, depending on the evaluated facial movement. The results indicate that ensemble-based ML models can reliably distinguish between different FP grades using non-invasive EMG data. The approach offers a robust alternative to subjective clinical scoring, potentially improving diagnostic consistency and supporting longitudinal monitoring in clinical and research applications.

Quantitative grading and classification of the severity of facial paralysis (FP) are important for selecting the treatment plan and detecting subtle improvement that cannot be detected clinically. To date, none of the available FP grading systems have gained widespread clinical acceptance. The work presented here describes the development and testing of a system for FP grading and assessment which is part of a comprehensive evaluation system for FP. The system is based on the Kinect v2 hardware and the accompanying software SDK 2.0 in extracting the real time facial landmarks and facial animation units (FAUs). The aim of this paper is to describe the development and testing of the FP assessment phase (first phase) of a larger comprehensive evaluation system of FP. The system includes two phases; FP assessment and FP classification. A dataset of 375 records from 13 unilateral FP patients was compiled for this study. The FP assessment includes three separate modules. One module is the symmetry assessment of both facial sides at rest and while performing five voluntary facial movements. Another module is responsible for recognizing the facial movements. The last module assesses the performance of each facial movement for both sides of the face depending on the involved FAUs. The study validates that the FAUs captured using the Kinect sensor can be processed and used to develop an effective tool for the automatic evaluation of FP. The developed FP grading system provides a detailed quantitative report and has significant advantages over the existing grading scales. It is fast, easy to use, user-independent, low cost, quantitative, and automated and hence it is suitable to be used as a clinical tool.

The subjective assessment of facial paralysis relies on the expertise of clinicians; the main limitation is intra-observer and inter-observer reproducibility. In this paper, we proposed a deep learning approach combining point clouds of facial movements with expert consensus to objectively quantify the severity of facial paralysis. A dynamic 3D photogrammetry imaging system was used to capture the facial movements of five facial expressions. Point clouds of the face at rest and at maximum expressions were extracted. These were integrated with the experts grading of the severity of facial paralysis to train a PointNet network to quantify the severity of facial paralysis. The results showed an accuracy exceeding 95% for assessing facial paralysis.

Facial paralysis (FP) is an inability to move facial muscles voluntarily, affecting daily activities. There is a need for quantitative assessment and severity level classification of FP to evaluate the condition. None of the available tools are widely accepted. A comprehensive FP evaluation system has been developed by the authors. The system extracts real-time facial animation units (FAUs) using the Kinect V2 sensor and includes both FP assessment and classification. This paper describes the development and testing of the FP classification phase. A dataset of 375 records from 13 unilateral FP patients and 1650 records from 50 control subjects was compiled. Artificial Intelligence and Machine Learning methods are used to classify seven FP categories: the normal case and three severity levels: mild, moderate, and severe for the left and right sides. For better prediction results (Accuracy = 96.8%, Sensitivity = 88.9% and Specificity = 99%), an ensemble learning classifier was developed rather than one weak classifier. The ensemble approach based on SVMs was proposed for the high-dimensional data to gather the advantages of stacking and bagging. To address the problem of an imbalanced dataset, a hybrid strategy combining three separate techniques was used. Model robustness and stability was evaluated using fivefold cross-validation. The results showed that the classifier is robust, stable and performs well for different train and test samples. The study demonstrates that FAUs acquired by the Kinect sensor can be used in classifying FP. The developed FP assessment and classification system provides a detailed quantitative report and has significant advantages over existing grading scales.