Management of Long-Standing Flaccid Facial Palsy

Facial Paralysis Treatment Using Selective Neurectomy: A Comprehensive Review

Facial paralysis can affect patients undergoing full mouth rehabilitation, regardless of what caused their paralysis. A procedure known as modified selective neurectomy of the facial nerve can enhance the movement of facial muscles in individuals with facial synkinesis safely and effectively. This approach is proposed as an alternative to more invasive surgical options when used independently as a treatment for incomplete facial palsy. Selective neurectomy offers a promising surgical option for managing nonflaccid facial paralysis and synkinesis, enhancing patients' quality of life. However, treatment plans should be individually tailored considering the complexity of facial paralysis and the unique needs of each patient, taking into account the timing and type of treatment. The objective of this review is to explore the use of selective neurectomy as a treatment for facial paralysis based on previously published papers.

Secondary dynamic midface reanimation with gracilis free muscle transfer after failed reconstruction attempt: A 15-year experience

BACKGROUND

The quantitative outcome of secondary reanimation after a failed primary reconstruction attempt for facial paralysis is rarely reported in the literature. This study aimed to investigate the feasibility of secondary reanimation with gracilis free muscle transfer (GFMT) and whether this outcome is influenced by the primary reconstruction.

METHODS

Twelve patients with previously failed static procedures (static group, n = 6), temporal muscle transfer (temporal transfer group, n = 2), and GFMT (GFMT group, n = 4) were all secondarily reanimated with GFMT. The clinical outcome was graded with the eFACE metric. The objective oral commissure excursion was measured with Emotrics, and the artificial intelligence software FaceReader evaluated the intensity score (IS) of emotional expression.

RESULTS

The mean follow-up was 40 ± 27 months. The eFACE metric showed a statistically significant (p < 0.05) postoperative improvement in the dynamic and smile scores across all groups. In the GFMT group, oral commissure with smile (75.75 ± 20.43 points), oral commissure excursion while smiling with teeth showing (32.7 ± 4.35 mm), and the intensity of happiness emotion while smiling without teeth showing (IS of 0.37 ± 0.23) were significantly lower as compared with the static group postoperatively (98.83 ± 2.86 points, p = 0.038; 41.7 ± 4.35 mm, p = 0.025; IS 0.83 ± 0.16, p = 0.01).

CONCLUSIONS

Our data suggest that secondary dynamic reconstruction with GFMT is feasible should the primary reconstruction fail. The secondary GFMT appears to improve the outcome of primary GFMT; however, the oral commissure excursion while smiling might be lower than that in patients who had static procedures as primary reconstruction.

Rhinoplasty: Considerations for Patients with Facial Paralysis

Nasal obstruction is a common sequela of flaccid facial paralysis but one that is often underaddressed surgically. Weakness of nasal musculature on the paralyzed side of the face leads to nasal valve narrowing through loss of static and dynamic nasal side wall tone as well as inferomedial displacement of the alar base. Standard rhinoplasty techniques such as alar batten grafts or flaring sutures may be used to support the nasal side wall in facial paralysis. However, to address the inferomedial alar displacement, suspension techniques are often required. Suture resuspension and fascia lata resuspension techniques are described, with modifications to each to improve longevity of the suspension.

Towards a Reliable and Rapid Automated Grading System in Facial Palsy Patients: Facial Palsy Surgery Meets Computer Science

Background: Reliable, time- and cost-effective, and clinician-friendly diagnostic tools are cornerstones in facial palsy (FP) patient management. Different automated FP grading systems have been developed but revealed persisting downsides such as insufficient accuracy and cost-intensive hardware. We aimed to overcome these barriers and programmed an automated grading system for FP patients utilizing the House and Brackmann scale (HBS). Methods: Image datasets of 86 patients seen at the Department of Plastic, Hand, and Reconstructive Surgery at the University Hospital Regensburg, Germany, between June 2017 and May 2021, were used to train the neural network and evaluate its accuracy. Nine facial poses per patient were analyzed by the algorithm. Results: The algorithm showed an accuracy of 100%. Oversampling did not result in altered outcomes, while the direct form displayed superior accuracy levels when compared to the modular classification form (n = 86; 100% vs. 99%). The Early Fusion technique was linked to improved accuracy outcomes in comparison to the Late Fusion and sequential method (n = 86; 100% vs. 96% vs. 97%). Conclusions: Our automated FP grading system combines high-level accuracy with cost- and time-effectiveness. Our algorithm may accelerate the grading process in FP patients and facilitate the FP surgeon’s workflow.

An overview of surgical techniques and non-surgical treatments in lifting the eyebrow including current treatments available

The eyebrow is a complex structure of the human face, which has both functional (communication) and aesthetic aspects and contributes to a persons’ individual appearance. Ptosis of the eyebrow is a common condition, especially among the elderly and smokers. Facial appearance is disrupted by eyebrow ptosis, and in many cases, correction and treatment are needed. No specific type of browlift has been shown to be superior to another, and since there is a wide variation in treatment preference between patients, it requires an individual approach to each specific patient. This review provides an overview of the history of the browlift, non-surgical treatment and current surgical techniques available.

Level of evidence: Not ratable.

Advanced Analysis of 3D Kinect Data: Supervised Classification of Facial Nerve Function via Parallel Convolutional Neural Networks

In this paper, we designed a methodology to classify facial nerve function after head and neck surgery. It is important to be able to observe the rehabilitation process objectively after a specific brain surgery, when patients are often affected by face palsy. The dataset that is used for classification problems in this study only contains 236 measurements of 127 patients of complex observations using the most commonly used House–Brackmann (HB) scale, which is based on the subjective opinion of the physician. Although there are several traditional evaluation methods for measuring facial paralysis, they still suffer from ignoring facial movement information. This plays an important role in the analysis of facial paralysis and limits the selection of useful facial features for the evaluation of facial paralysis. In this paper, we present a triple-path convolutional neural network (TPCNN) to evaluate the problem of mimetic muscle rehabilitation, which is observed by a Kinect stereovision camera. A system consisting of three modules for facial landmark measure computation and facial paralysis classification based on a parallel convolutional neural network structure is used to quantitatively assess the classification of facial nerve paralysis by considering facial features based on the region and the temporal variation of facial landmark sequences. The proposed deep network analyzes both the global and local facial movement features of a patient’s face. These extracted high-level representations are then fused for the final evaluation of facial paralysis. The experimental results have verified the better performance of TPCNN compared to state-of-the-art deep learning networks.

Static Sling Options for Facial Paralysis: Now Versus 10 Years Ago

Static facial sling procedures are one of many facial reanimation options to address long-standing and irreversible facial paralysis. The primary goals of static reanimation are to provide symmetry at rest and improve static function at repose. Choosing the best option depends on patient factors, such as age, comorbidities, and injury factors. Different materials are available for static sling surgery; we believe autologous tendon offers the most reliable and long-lasting results. Static suspension procedures provide immediate results, improved resting position, and can augment other techniques. This article discusses available options for static reanimations to address the eye complex, midface, and mouth.

Region Based Parallel Hierarchy Convolutional Neural Network for Automatic Facial Nerve Paralysis Evaluation

In this article, we propose a parallel hierarchy convolutional neural network (PHCNN) combining a Long Short-Term Memory (LSTM) network structure to quantitatively assess the grading of facial nerve paralysis (FNP) by considering the region-based asymmetric facial features and temporal variation of the image sequences. FNP, such as Bell's palsy, is the most common facial symptom of neuromotor dysfunctions. It causes the weakness of facial muscles for the normal emotional expression and movements. The subjective judgement by clinicians completely depends on individual experience, which may not lead to a uniform evaluation. Existing computer-aided methods mainly rely on some complicated imaging equipment, which is complicated and expensive for facial functional rehabilitation. Compared with the subjective judgment and complex imaging processing, the objective and intelligent measurement can potentially avoid this issue. Considering dynamic variation in both global and regional facial areas, the proposed hierarchical network with LSTM structure can effectively improve the diagnostic accuracy and extract paralysis detail from the low-level shape, contour to sematic level features. By segmenting the facial area into two palsy regions, the proposed method can discriminate FNP from normal face accurately and significantly reduce the effect caused by age wrinkles and unrepresentative organs with shape and position variations on feature learning. Experiment on the YouTube Facial Palsy Database and Extended CohnKanade Database shows that the proposed method is superior to the state of the art deep learning methods.