Neurotization Preferences in Smile Reanimation: A Discrete Choice Experiment.

BACKGROUND: Common donor nerve options in smile reanimation include ipsilateral trigeminal motor or contralateral facial nerve branches. Neurotization preference may be influenced by multiple factors, whose relative importance remains poorly understood. In this article, decision-making in smile reanimation is assessed using a stated preference model. METHODS: Qualitative interviews with facial palsy patients identified five relevant attributes for study: smile type ("smile when biting" versus "smile spontaneously" as proxies for trigeminal versus cross-facial neurotization), number of operations, success rates, complication rates, and side effects. Community volunteers (n = 250) completed a discrete-choice experiment relevant to free muscle transfer for smile reanimation. Preoperative and postoperative states were demonstrated through video vignettes, together with explanation of surgical risks, consequences, and benefits. Attribute importance was modeled using hierarchical Bayes estimation. RESULTS: Two hundred forty-one responses met quality controls. Attribute importance ranked as follows: chance of success, 37.3 percent; smile type, 21.4 percent; side effects, 13.9 percent; complication rates, 13.8; and number of operations, 13.6 percent. All attributes significantly correlated with decision making (p < 0.0001). An aggregate response model revealed most participants (67.6 percent; standard error, 3.0 percent) preferred smile reanimation by cross-facial (assuming a success rate of 80 percent) as opposed to ipsilateral trigeminal motor branch neurotization. When the success rate for cross-facial neurotization was reduced below 67 percent, trigeminal neurotization was preferred. CONCLUSIONS: Despite a higher risk of failure, most respondents preferred a cross-facial as opposed to trigeminal neurotization strategy for smile reanimation. These findings highlight the complexity of decision-making and need for individualized risk tolerance assessment in the field of facial reanimation.

The Auto-eFACE: Machine Learning-Enhanced Program Yields Automated Facial Palsy Assessment Tool.

BACKGROUND: Facial palsy assessment is nonstandardized. Clinician-graded scales are limited by subjectivity and observer bias. Computer-aided grading would be desirable to achieve conformity in facial palsy assessment and to compare the effectiveness of treatments. This research compares the clinician-graded eFACE scale to machine learning-derived automated assessments (auto-eFACE). METHODS: The Massachusetts Eye and Ear Infirmary Standard Facial Palsy Dataset was employed. Clinician-graded eFACE assessment was performed on 160 photographs. A Python script was used to automatically generate auto-eFACE scores on the same photographs. eFACE and auto-eFACE scores were compared for normal, flaccidly paralyzed, and synkinetic faces. RESULTS: Auto-eFACE and eFACE scores differentiated normal faces from those with facial palsy. Auto-eFACE produced significantly lower scores than eFACE for normal faces (93.83 ± 4.37 versus 100.00 ± 1.58; p = 0.01). Review of photographs revealed minor facial asymmetries in normal faces that clinicians tend to disregard. Auto-eFACE reported better facial symmetry in patients with flaccid paralysis (59.96 ± 5.80) and severe synkinesis (62.35 ± 9.35) than clinician-graded eFACE (52.20 ± 3.39 and 54.22 ± 5.35, respectively; p = 0.080 and p = 0.080, respectively); this result trended toward significance. CONCLUSIONS: Auto-eFACE scores can be obtained automatically using a freely available machine learning-based computer software. Automated scores predicted more asymmetry in normal patients, and less asymmetry in patients with flaccid palsy and synkinesis, compared to clinician grading. Auto-eFACE is a quick and easy-to-use assessment tool that holds promise for standardization of facial palsy outcome measures and may eliminate observer bias seen in clinician-graded scales. CLINICAL QUESTION/LEVEL OF EVIDENCE: Diagnostic, III.

Supporting caregivers of veterans with Alzheimer's disease and traumatic brain injury: study protocol for a randomized controlled trial.

BACKGROUND: Patients with Alzheimer's disease and related dementias (ADRD) and traumatic brain injury (TBI) and their caregivers require cognitive and behavioral symptom management, interdisciplinary care, support for caregivers, and seamless care coordination between providers. Caring for someone with ADRD or TBI is associated with higher rates of psychological morbidity and burden, social isolation, financial hardship, and deterioration of physical health. Tremendous need exists for primary care-based interventions that concurrently address the care needs of dyads and aim to improve care and outcomes for both individuals with ADRD and TBI and their family caregivers. METHODS: The Aging Brain Care Acquiring New Skills While Enhancing Remaining Strengths (ABC ANSWERS) study is a randomized controlled trial that tests the effectiveness of an intervention based on two evidence-based programs that have been developed for and previously tested in populations with ADRD, TBI, stroke, and late-life depression and/or who have survived an intensive care unit stay. This study includes 200 dyads comprised of a veteran with a diagnosis of ADRD or TBI and the veteran's primary informal caregiver. Dyads are randomized to receive the ABC ANSWERS intervention or routine Veterans Health Administration (VHA) primary care with a standardized educational and resource information packet. Data collection occurs at baseline and three follow-up time points (3 months, 6 months, and 12 months). The primary outcome is caregiver quality of life (QoL). A secondary measure for the caregiver is caregiver burden. Secondary measures for both the veteran and caregiver include symptoms of depression and anxiety. DISCUSSION: The ABC ANSWERS intervention integrates common features of an evidence-based collaborative care model for brain health while concurrently attending to the implementation barriers of delivering care and skills to dyads. We hypothesize that caregivers in dyads randomized to the ABC ANSWERS program will experience higher levels of QoL and lower levels of depression, anxiety, dyadic strain, and caregiver burden at 12 months than those receiving usual VHA primary care. TRIAL REGISTRATION: ClinicalTrials.gov, NCT03397667. Registered on 12 January 2018.

Toward an Automatic System for Computer-Aided Assessment in Facial Palsy.

Importance: Quantitative assessment of facial function is challenging, and subjective grading scales such as House-Brackmann, Sunnybrook, and eFACE have well-recognized limitations. Machine learning (ML) approaches to facial landmark localization carry great clinical potential as they enable high-throughput automated quantification of relevant facial metrics from photographs and videos. However, the translation from research settings to clinical application still requires important improvements. Objective: To develop a novel ML algorithm for fast and accurate localization of facial landmarks in photographs of facial palsy patients and utilize this technology as part of an automated computer-aided diagnosis system. Design, Setting, and Participants: Portrait photographs of 8 expressions obtained from 200 facial palsy patients and 10 healthy participants were manually annotated by localizing 68 facial landmarks in each photograph and by 3 trained clinicians using a custom graphical user interface. A novel ML model for automated facial landmark localization was trained using this disease-specific database. Algorithm accuracy was compared with manual markings and the output of a model trained using a larger database consisting only of healthy subjects. Main Outcomes and Measurements: Root mean square error normalized by the interocular distance (NRMSE) of facial landmark localization between prediction of ML algorithm and manually localized landmarks. Results: Publicly available algorithms for facial landmark localization provide poor localization accuracy when applied to photographs of patients compared with photographs of healthy controls (NRMSE, 8.56 ± 2.16 vs. 7.09 ± 2.34, p ≪ 0.01). We found significant improvement in facial landmark localization accuracy for the facial palsy patient population when using a model trained with a relatively small number photographs (1440) of patients compared with a model trained using several thousand more images of healthy faces (NRMSE, 6.03 ± 2.43 vs. 8.56 ± 2.16, p ≪ 0.01). Conclusions and Relevance: Retraining a computer vision facial landmark detection model with fewer than 1600 annotated images of patients significantly improved landmark detection performance in frontal view photographs of this population. The new annotated database and facial landmark localization model represent the first steps toward an automatic system for computer-aided assessment in facial palsy. Level of Evidence: 4.

The spectrum of facial palsy: The MEEI facial palsy photo and video standard set.

OBJECTIVES: Facial palsy causes variable facial disfigurement ranging from subtle asymmetry to crippling deformity. There is no existing standard database to serve as a resource for facial palsy education and research. We present a standardized set of facial photographs and videos representing the entire spectrum of flaccid and nonflaccid (aberrantly regenerated or synkinetic) facial palsy. To demonstrate the utility of the dataset, we describe the relationship between level of facial function and perceived emotion expression as determined by an automated emotion detection, machine learning-based algorithm. METHODS: Photographs and videos of patients with both flaccid and nonflaccid facial palsy were prospectively gathered. The degree of facial palsy was quantified using eFACE, House-Brackmann, and Sunnybrook scales. Perceived emotion during a standard video of facial movements was determined using an automated, machine learning algorithm. RESULTS: Sixty participants were enrolled and categorized by eFACE score across the range of facial function. Patients with complete flaccid facial palsy (eFACE <60) had a significant loss of perceived joy compared to the nonflaccid and normal groups. Additionally, patients with only moderate flaccid and nonflaccid facial palsy had a significant increase in perceived negative emotion (contempt) when compared to the normal group. CONCLUSION: We provide this open-source database to assist in comparing current and future scales of facial function as well as facilitate comprehensive investigation of the entire spectrum of facial palsy. The automated machine learning-based algorithm detected negative emotions at moderate levels of facial palsy and suggested a threshold severity of flaccid facial palsy beyond which joy was not perceived. LEVEL OF EVIDENCE: NA Laryngoscope, 130:32-37, 2020.

Spirocyclic acylphloroglucinol derivatives from Hypericum pyramidatum.

Five spirocyclic acylphloroglucinol derivatives (1-5) have been isolated from a hexanes extract of the leaves of Hypericum pyramidatum. Pyramidatones A-D (1-3, 5) are new, and chipericumin C (4) has been previously reported. The acylphloroglucinols were characterized based on spectroscopic (NMR, IR, UV-VIS) and mass spectrometric data.