Toward an application of automatic evaluation system for central facial palsy using two simple evaluation indices in emergency medicine.

A stroke is a medical emergency and thus requires immediate treatment. Paramedics should accurately assess suspected stroke patients and promptly transport them to a hospital with stroke care facilities; however, current assessment procedures rely on subjective visual assessment. We aim to develop an automatic evaluation system for central facial palsy (CFP) that uses RGB cameras installed in an ambulance. This paper presents two evaluation indices, namely the symmetry of mouth movement and the difference in mouth shape, respectively, extracted from video frames. These evaluation indices allow us to quantitatively evaluate the degree of facial palsy. A classification model based on these indices can discriminate patients with CFP. The results of experiments using our dataset show that the values of the two evaluation indices are significantly different between healthy subjects and CFP patients. Furthermore, our classification model achieved an area under the curve of 0.847. This study demonstrates that the proposed automatic evaluation system has great potential for quantitatively assessing CFP patients based on two evaluation indices.

The Objective Assessment of Event-Related Potentials: An Influence of Chronic Pain on ERP Parameters.

The article presents an original method for the automatic assessment of the quality of event-related potentials (ERPs), based on the calculation of the coefficient ε, which describes the compliance of recorded ERPs with some statistically significant parameters. This method was used to analyze the neuropsychological EEG monitoring of patients suffering from migraines. The frequency of migraine attacks was correlated with the spatial distribution of the coefficients ε, calculated for EEG channels. More than 15 migraine attacks per month was accompanied by an increase in calculated values in the occipital region. Patients with infrequent migraines exhibited maximum quality in the frontal areas. The automatic analysis of spatial maps of the coefficient ε demonstrated a statistically significant difference between the two analyzed groups with different means of migraine attack numbers per month.

The Objective Assessment of Event-Related Potentials: An Influence of Chronic Pain on ERP Parameters / 神经科学通报·英文版

The article presents an original method for the automatic assessment of the quality of event-related potentials (ERPs), based on the calculation of the coefficient ε, which describes the compliance of recorded ERPs with some statistically significant parameters. This method was used to analyze the neuropsychological EEG monitoring of patients suffering from migraines. The frequency of migraine attacks was correlated with the spatial distribution of the coefficients ε, calculated for EEG channels. More than 15 migraine attacks per month was accompanied by an increase in calculated values in the occipital region. Patients with infrequent migraines exhibited maximum quality in the frontal areas. The automatic analysis of spatial maps of the coefficient ε demonstrated a statistically significant difference between the two analyzed groups with different means of migraine attack numbers per month.

The Application of Deep Learning for the Evaluation of User Interfaces.

In this study, we tested the ability of a machine-learning model (ML) to evaluate different user interface designs within the defined boundaries of some given software. Our approach used ML to automatically evaluate existing and new web application designs and provide developers and designers with a benchmark for choosing the most user-friendly and effective design. The model is also useful for any other software in which the user has different options to choose from or where choice depends on user knowledge, such as quizzes in e-learning. The model can rank accessible designs and evaluate the accessibility of new designs. We used an ensemble model with a custom multi-channel convolutional neural network (CNN) and an ensemble model with a standard architecture with multiple versions of down-sampled input images and compared the results. We also describe our data preparation process. The results of our research show that ML algorithms can estimate the future performance of completely new user interfaces within the given elements of user interface design, especially for color/contrast and font/layout.

[Accuracy of key point matrix technology based contactless automatic measurement for joint motion of hand].

Objective: To validate the use of key point matrix technology based contactless automatic measurement for evaluation of joint motion of hand. Methods: Thirty-three volunteers were enrolled to evaluate the extension and flexion of hand joints between May 2021 and November 2021. There were 20 males and 13 females, the age ranged from 16 to 70 years with an average of 30.2 years. The extension angles of 14 joints of 5 fingers (including hyperextension) and the flexion angles of 12 joints of 4 fingers (excluding thumb) of volunteers were measured by key point matrix technology and manual goniometer, respectively. Then 5 participants and repeated measurement experiment were employed to test the system repeatability and accuracy; 28 participants and paired measurement experiment were employed to test the system accuracy. Results: The average repeatability of finger joint motion measured by the key point matrix technology was 1.801° (extension) and 7.823° (flexion), respectively. Compared with manual measurement, the average differences of each finger joint measured by the key point matrix technology were 3.225° in extension and 14.145° in flexion, respectively. The key point matrix technology based contactless automatic evaluation system offered excellent consistency with the manual goniometers ( ICC=0.875). While most of the consistency with manual goniometer of individual joints were at moderate levels (median of ICC, 0.440). The correlation coefficients between the measurement results of the two methods were mainly positive in the extension of the joint ( P<0.05) and negative in the flexion of the joints ( P<0.05). Conclusion: The key point matrix technology based contactless automatic evaluation provides sufficient measurement repeatability and accuracy in evaluation for the joint motion of hand.

Automatic Evaluation of Motor Rehabilitation Exercises Based on Deep Mixture Density Neural Networks.

An automatic assessment system for physical telerehabilitation could reduce the time and cost of treatments. But such assessment involves stochastic uncertainties, nonlinearities, and complexities of human movement. Probabilistic models and deep structures are two categories that could, respectively, address the stochastic uncertainty and complexity of motion data. In the proposed Deep Mixture Density Network (DMDN), probabilistic models were concurrently processed along with deep neural networks. More specifically, a multi-branch convolutional layer extracted the deep features of motion data, a Long Short Term Memory (LSTM) learned its temporal dependencies, and a Gaussian Mixture Model (GMM) handled the stochastic interaction of its preceding layers in reaching a more valid assessment and improved generalization to new movements. Finally, the weighted mean of the GMM components was used as the performance score for exercises. Input data were the time series related to the joints' position and orientation extracted from the Kinect v2 sensor video. A clinical reference score for each movement was also included for training the DMDN. In addition to comparisons with the state-of-the-art algorithms, an ablation study of the various elements comprising the DMDN was performed. Three configurations of convolutional filter window transitions across input data were also investigated. Results indicate that the proposed DMDN with one-dimensional parallel window transitions outperforms the other competing strategies in the ablation study. It also achieves higher reliability in terms of a lower RMSE standard deviation against a DMDN without GMM strategy while ranking competitively according to the Spearman correlation coefficient and Root Mean Square Error.

Powerful Effects of Diagnostic Information on Automatic and Self-Reported Evaluation: The Moderating Role of Memory Recall.

We sometimes learn about certain behaviors of others that we consider diagnostic of their character (e.g., that they did immoral things). Recent research has shown that such information trumps the impact of other (less diagnostic) information both on self-reported evaluations and on more automatic evaluations as probed with indirect measures such as the Affect Misattribution Procedure (AMP). We examined whether facilitating memory recall of alternative information moderates the impact of diagnostic information on evaluation. In Experiments 1 and 2, participants learned one diagnostic positive and one diagnostic negative behavior of two unfamiliar people. Presenting a cue semantically related to this information during evaluation influenced AMP scores but not self-reported liking scores. Experiments 3 and 4 showed that elaborative rehearsal of low diagnostic information eliminated diagnosticity effects on AMP scores and reduced them on self-reported liking scores. These findings help elucidate the role of memory recall and diagnosticity in evaluation.

Research and realization of automatic evaluation of stereotactic radiotherapy plan for early non-small cell lung cancer / 中华放射肿瘤学杂志

Objective:To realize the automatic evaluation of the target conformity, dose overflow, dose drop and other indicators in stereotactic body radiation therapy (SBRT) plan for the early non-small cell lung cancer (NSCLC) in the Eclipse planning system.Methods:Eclipse Scripting API application development interface and C# programming language were used to develop it with script plug-ins combined with independent programs. According to the requirements of the NSCLC SBRT plan in Radiation Therapy Oncology Group (RTOG) 0915 report, the automatic evaluation of related indicators was realized. A rule of equal interval sampling and double thresholds was designed during the period, which could more accurately and conveniently evaluate the dose drop outside the target area.Results:13 clinical cases of NSCLC SBRT plans were randomly selected, the method in this study and the module equipped with the Eclipse system to were utilized to evaluate and compare the results. It was concluded that the results of the two methods were in line with the clinical requirements, and the former was more efficient ( P<0.05). Conclusions:The NSCLC SBRT plan evaluation software in this article has a friendly interface. It can not only realize the automatic evaluation of target conformity, dose overflow and dose drop, but also effectively improve work efficiency and better serve the clinical and scientific research work of radiotherapy.

Can (Instructions About) Stimulus Pairings Influence Automatic and Self-Reported Evaluations in the Presence of More Diagnostic Evaluative Information?

Evaluative conditioning (EC) and persuasion are important pathways for shaping evaluations. However, little is known about how these pathways interact. Two preregistered experiments (total N = 1,510) examined effects of EC procedures (i.e., stimulus pairings) and EC instructions (i.e., instructions about stimulus pairings) on automatic and self-reported evaluations of social groups in the presence of more diagnostic information about the evaluative traits of those groups. Interestingly, both EC procedures and EC instructions still influenced automatic and self-reported evaluations when participants had read more diagnostic persuasive information. In line with predictions of propositional accounts of evaluation, EC instruction effects on automatic evaluations were not mediated by corresponding changes in self-reported evaluations. These results have theoretical implications and also highlight the important role that (instructions about) stimulus pairings have in social learning.

Illusory-Correlation Effects on Implicit and Explicit Evaluation.

Research suggests that people sometimes perceive a relationship between stimuli when no such relationship exists (i.e., illusory correlation). Illusory-correlation effects are thought to play a central role in the formation of stereotypes and evaluations of minority versus majority groups, often leading to less favorable impressions of minorities. Extant theories differ in terms of whether they attribute illusory-correlation effects to processes operating during learning (belief formation) or measurement (belief expression), and whether different evaluation measures should be differentially sensitive to illusory-correlation effects. Past research found mixed evidence for dissociative effects of illusory-correlation manipulations on measures of implicit (i.e., automatic) and explicit (i.e., controlled) evaluation. Four high-powered studies obtained illusory-correlation effects on explicit evaluations, but not implicit evaluations probed with an Implicit Association Test, Evaluative Priming Task, and Affect Misattribution Procedure. The results are consistent with theories that attribute illusory-correlation effects to processes during belief expression.

Dataset for comparable evaluation of machine translation between 11 South African languages.

This data article describes the Autshumato machine translation evaluation set. The evaluation set contains data that can be used to evaluate machine translation systems between any of the 11 official South African languages. The dataset is parallel with four reference translations available for each of the following languages: Afrikaans, English, isiNdebele, isiXhosa, isiZulu, Sepedi, Sesotho, Setswana, Siswati, Tshivenda and Xitsonga.

An evaluation of the construction of the device along with the software for digital archiving, sending the data, and supporting the diagnosis of cervical cancer.

Cervical cancer is still an important cause of mortality among women in a number of countries. There are effective methods of prevention and early diagnosis, but they require well-trained medical professionals including cytologists. Within this project, we built a prototype of a new device together with implemented software using U-NET and CNN architectures of neural networks (ANN), to convert the currently used optical microscopes into fully independent scanning and evaluating systems for cytological samples. To evaluate the specificity and sensitivity of the system, 2058 (2000 normal and 58 abnormal samples) consecutive liquid-based cytology (LBC) samples were analysed. The observed sensitivity and specificity to distinguish normal and abnormal samples was 100%. We observed slight incompatibility in the evaluation of the type of abnormality. The use of ANN is promising for increasing the effectiveness of cervical screening. The low cost of neural network usage further increases the potential areas of application of the presented method. Further refinement of neural networks on a larger sample size is required to evaluate the software.

Hypnotic Suggestions Can Induce Rapid Change in Implicit Attitudes.

We sometimes evaluate our environment (e.g., persons, objects, situations) in an automatic fashion. These automatic or implicit evaluations are often considered to be based on qualitatively distinct mental processes compared with more controlled or explicit evaluations. Important evidence for this claim comes from studies showing that implicit evaluations do not change as the result of counterattitudinal information, in contrast to their explicit counterparts. We examined the impact of counterattitudinal information on implicit evaluations in two experiments (N = 60, N = 72) that included an innovative manipulation: hypnotic suggestions to participants that they would strongly process upcoming counterattitudinal information. Both experiments indicated that hypnotic suggestions facilitated effects of counterattitudinal information on implicit evaluations. These findings extend recent evidence for rapid revision of implicit evaluations on the basis of counterattitudinal information and support the controversial idea that belief-based processes underlie not only explicit but also implicit evaluations.

Revisiting the past and back to the future: Horizons of cognition and emotion research.

To commemorate that Cognition & Emotion was established three decades ago, we asked some distinguished scholars to reflect on past research on the interface of cognition and emotion and prospects for the future. The resulting papers form the Special Issue on Horizons in Cognition and Emotion Research. The contributions to Horizons cover both the field in general and a diversity of specific topics, including affective neuroscience, appraisal theory, automatic evaluation, embodied emotion, emotional disorders, emotion-linked attentional bias, emotion recognition, emotion regulation, lifespan development, motivation, and social emotions. We hope that Horizons will spark constructive debates, while offering guidance for the future growth and development of research on the interface between cognition and emotion. Finally, we provide an update on how Cognition & Emotion has fared over the past year, and announce some changes in editorial policies and the editorial board.

Cognition and emotion: on paradigms and metaphors.

The field of cognition and emotion is characterised as the cognitive psychology of evaluative and affective processes. The most important development in this field is the fruitful adoption of cognitive psychology paradigms to study automatic evaluation processes, for example. This has led to a plethora of findings and theories. Two points are emphasised: First, the (often metaphorical) theoretical way of thinking has changed over the decades. Theorising with symbolic models (e.g. semantic networks), which was prevalent in earlier years, has been replaced more recently by subsymbolic models (i.e. PDP models). It is argued that - despite their still metaphorical character - the latter are better suited to capturing characteristics of emotional processes. Second, the field has adopted the methods of experimental cognitive psychology to develop and refine paradigms as "windows to the mind".

Performance metrics in mastoidectomy training: a systematic review.

OBJECTIVE: To investigate validity evidence, and strengths and limitations of performance metrics in mastoidectomy training. METHODS: A systematic review following the PRISMA guidelines. Studies reporting performance metrics in mastoidectomy/temporal bone surgery were included. Data on design, outcomes, and results were extracted by two reviewers. Validity evidence according to Messick's framework and level of evidence were assessed. RESULTS: The search yielded a total of 1085 studies from the years 1947-2018 and 35 studies were included for full data extraction after abstract and full-text screening. 33 different metrics on mastoidectomy performance were identified and ranked according to the number of reports. Most of the 33 metrics identified had some amount of validity evidence. The metrics with most validity evidence were related to drilling time, volume drilled per time, force applied near vital structures, and volume removed. CONCLUSIONS: This review provides an overview of current metrics of mastoidectomy performance, their validity, strengths and limitations, and identifies the gap in validity evidence of some metrics. Evidence-based metrics can be used for performance assessment in temporal bone surgery and for providing integrated and automated feedback in virtual reality simulation training. The use of such metrics in simulation-based mastoidectomy training can potentially address some of the limitations in current temporal bone skill assessment and ease assessment in repeated practice. However, at present, an automated feedback based on metrics in VR simulation does not have sufficient empirical basis and has not been generally accepted for use in training and certification. LEVEL OF EVIDENCE: 2a.

Automatic Evaluations and Exercising: Systematic Review and Implications for Future Research.

The general purpose of this systematic review was to summarize, structure and evaluate the findings on automatic evaluations of exercising. Studies were eligible for inclusion if they reported measuring automatic evaluations of exercising with an implicit measure and assessed some kind of exercise variable. Fourteen nonexperimental and six experimental studies (out of a total N = 1,928) were identified and rated by two independent reviewers. The main study characteristics were extracted and the grade of evidence for each study evaluated. First, results revealed a large heterogeneity in the applied measures to assess automatic evaluations of exercising and the exercise variables. Generally, small to large-sized significant relations between automatic evaluations of exercising and exercise variables were identified in the vast majority of studies. The review offers a systematization of the various examined exercise variables and prompts to differentiate more carefully between actually observed exercise behavior (proximal exercise indicator) and associated physiological or psychological variables (distal exercise indicator). Second, a lack of transparent reported reflections on the differing theoretical basis leading to the use of specific implicit measures was observed. Implicit measures should be applied purposefully, taking into consideration the individual advantages or disadvantages of the measures. Third, 12 studies were rated as providing first-grade evidence (lowest grade of evidence), five represent second-grade and three were rated as third-grade evidence. There is a dramatic lack of experimental studies, which are essential for illustrating the cause-effect relation between automatic evaluations of exercising and exercise and investigating under which conditions automatic evaluations of exercising influence behavior. Conclusions about the necessity of exercise interventions targeted at the alteration of automatic evaluations of exercising should therefore not be drawn too hastily.

Design and implementation of Magphan-phantom-based image parameters auto evaluation system for MRI quality inspection / 医疗卫生装备

Objective To design and implement an automatic analysis and evaluation system for the image parameters of medical MRI quality testing.Methods The system was developed and debugged by the study on MRI image quality parameters,the image denoising,integration,extraction and etc by MATLAB processing platform as well as the comparison and comparative calculation of the obtained data.Results The system replaced manual operation by auto processing and parameters analysis of MRI quality inspection image.Conclusion The system enhances the efficiency and avoids artificial error,and has a promising prospect in the future.

Design and implementation of Magphan-phantom-based image parameters auto evaluation system for MRI quality inspection / 医疗卫生装备

Objective To design and implement an automatic analysis and evaluation system for the image parameters of medical MRI quality testing.Methods The system was developed and debugged by the study on MRI image quality parameters,the image denoising,integration,extraction and etc by MATLAB processing platform as well as the comparison and comparative calculation of the obtained data.Results The system replaced manual operation by auto processing and parameters analysis of MRI quality inspection image.Conclusion The system enhances the efficiency and avoids artificial error,and has a promising prospect in the future.