A Critical Assessment and Review of Artificial Intelligence in Facial Paralysis Analysis: Uncovering the Truth

FACE ◽  
2021 ◽  
pp. 273250162110228
Author(s):  
David T. Mitchell ◽  
David Z. Allen ◽  
Matthew R. Greives ◽  
Phuong D. Nguyen
Keyword(s):  
Artificial Intelligence ◽  
Facial Paralysis ◽  
Critical Assessment ◽  
Facial Emotion ◽  
Data Systems ◽  
Computer Algorithms ◽  
Future Directions ◽  
Sample Data ◽  
Before And After ◽  
Facial Reanimation

Machine learning is a rapidly growing subset of artificial intelligence (AI) which involves computer algorithms that automatically build mathematical models based on sample data. Systems can be taught to learn from patterns in existing data in order to make similar conclusions from new data. The use of AI in facial emotion recognition (FER) has become an area of increasing interest for providers who wish to quantify facial emotion before and after interventions such as facial reanimation surgery. While FER deep learning algorithms are less subjective when compared to layperson assessments, the databases used to train them can greatly alter their outputs. There are currently many well-established modalities for assessing facial paralysis, but there is also increasing interest in a more objective and universal measurement system to allow for consistent assessments between practitioners. The purpose of this article is to review the development of AI, examine its existing uses in facial paralysis assessment, and discuss the future directions of its implications.

scholarly journals Artificial Intelligence: A Primer for Breast Imaging Radiologists

2020 ◽  
Vol 2 (4) ◽  
pp. 304-314
Author(s):  
Manisha Bahl
Keyword(s):  
Breast Cancer ◽  
Artificial Intelligence ◽  
Cancer Detection ◽  
Breast Imaging ◽  
Breast Cancer Detection ◽  
Computer Algorithms ◽  
Prevention Strategies ◽  
Future Directions ◽  
Optimal Patient Care ◽  
Screening And Prevention

Abstract Artificial intelligence (AI) is a branch of computer science dedicated to developing computer algorithms that emulate intelligent human behavior. Subfields of AI include machine learning and deep learning. Advances in AI technologies have led to techniques that could increase breast cancer detection, improve clinical efficiency in breast imaging practices, and guide decision-making regarding screening and prevention strategies. This article reviews key terminology and concepts, discusses common AI models and methods to validate and evaluate these models, describes emerging AI applications in breast imaging, and outlines challenges and future directions. Familiarity with AI terminology, concepts, methods, and applications is essential for breast imaging radiologists to critically evaluate these emerging technologies, recognize their strengths and limitations, and ultimately ensure optimal patient care.

A CRITICAL ASSESSMENT OF THE NOVEL «ASSEMBLY HALL» BY HERMANN KANT IN THE REUNIFIED GERMANY (AFTER 1991)

2020 ◽  
pp. 144-152
Author(s):  
Rimma Gurevich
Keyword(s):  
Critical Assessment ◽  
The Novel ◽  
Aesthetic Appreciation ◽  
Party Position ◽  
Creative Personality ◽  
Before And After

H. Kant’s novel received a high literary and aesthetic appreciation in criticism and wide recognition by readers. Criticism (before and after the unification of Germany) concerns mainly one aspect viz. authenticity of the events depicted in the novel and the charcters’ images. Opponents argue that Kant’s ideological views, his consistent socialist and party position have prompt him to embellish reality, create simulacra, and the idyllic world of socialist Biedermeyer. The article shows that these assessments ignore the nature of his talent, especially his creative personality peculiarities such as journalistic orientation of the motivated «political» person and writer.

Artificial Intelligence for Skin Cancer Detection: A Scoping Review (Preprint)

2020 ◽  
Author(s):  
Abdulrahman Takiddin ◽  
Jens Schneider ◽  
Yin Yang ◽  
Alaa Abd-Alrazaq ◽  
Mowafa Househ
Keyword(s):  
Artificial Intelligence ◽  
Skin Cancer ◽  
Scoping Review ◽  
Performance Metrics ◽  
Data Extraction ◽  
Characteristic Curve ◽  
Evaluation Metrics ◽  
Cancer Type ◽  
Computer Algorithms ◽  
Scoping Reviews

BACKGROUND Skin cancer is the most common cancer type affecting humans. Traditional skin cancer diagnosis methods are costly, require a professional physician, and take time. Hence, to aid in diagnosing skin cancer, Artificial Intelligence (AI) tools are being used, including shallow and deep machine learning-based techniques that are trained to detect and classify skin cancer using computer algorithms and deep neural networks. OBJECTIVE The aim of this study is to identify and group the different types of AI-based technologies used to detect and classify skin cancer. The study also examines the reliability of the selected papers by studying the correlation between the dataset size and number of diagnostic classes with the performance metrics used to evaluate the models. METHODS We conducted a systematic search for articles using IEEE Xplore, ACM DL, and Ovid MEDLINE databases following the PRISMA Extension for Scoping Reviews (PRISMA-ScR) guidelines. The study included in this scoping review had to fulfill several selection criteria; to be specifically about skin cancer, detecting or classifying skin cancer, and using AI technologies. Study selection and data extraction were conducted by two reviewers independently. Extracted data were synthesized narratively, where studies were grouped based on the diagnostic AI techniques and their evaluation metrics. RESULTS We retrieved 906 papers from the 3 databases, but 53 studies were eligible for this review. While shallow techniques were used in 14 studies, deep techniques were utilized in 39 studies. The studies used accuracy (n=43/53), the area under receiver operating characteristic curve (n=5/53), sensitivity (n=3/53), and F1-score (n=2/53) to assess the proposed models. Studies that use smaller datasets and fewer diagnostic classes tend to have higher reported accuracy scores. CONCLUSIONS The adaptation of AI in the medical field facilitates the diagnosis process of skin cancer. However, the reliability of most AI tools is questionable since small datasets or low numbers of diagnostic classes are used. In addition, a direct comparison between methods is hindered by a varied use of different evaluation metrics and image types.

scholarly journals A Study of Automatic Judgment of Food Color and Cooking Conditions with Artificial Intelligence Technology

Processes ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 1128
Author(s):  
Chern-Sheng Lin ◽  
Yu-Ching Pan ◽  
Yu-Xin Kuo ◽  
Ching-Kun Chen ◽  
Chuen-Lin Tien
Keyword(s):  
Artificial Intelligence ◽  
Image Processing ◽  
Color Space ◽  
Color Difference ◽  
Color Space Conversion ◽  
Before And After ◽  
Artificial Intelligence Technology ◽  
Artificial Intelligence Algorithm ◽  
Time Image ◽  
Cooking Conditions

In this study, the machine vision and artificial intelligence algorithms were used to rapidly check the degree of cooking of foods and avoid the over-cooking of foods. Using a smart induction cooker for heating, the image processing program automatically recognizes the color of the food before and after cooking. The new cooking parameters were used to identify the cooking conditions of the food when it is undercooked, cooked, and overcooked. In the research, the camera was used in combination with the software for development, and the real-time image processing technology was used to obtain the information of the color of the food, and through calculation parameters, the cooking status of the food was monitored. In the second year, using the color space conversion, a novel algorithm, and artificial intelligence, the foreground segmentation was used to separate the vegetables from the background, and the cooking ripeness, cooking unevenness, oil glossiness, and sauce absorption were calculated. The image color difference and the distribution were used to judge the cooking conditions of the food, so that the cooking system can identify whether or not to adopt partial tumbling, or to end a cooking operation. A novel artificial intelligence algorithm is used in the relative field, and the error rate can be reduced to 3%. This work will significantly help researchers working in the advanced cooking devices.

scholarly journals Human-Centered Artificial Intelligence for Designing Accessible Cultural Heritage

2021 ◽  
Vol 11 (2) ◽  
pp. 870
Author(s):  
Galena Pisoni ◽  
Natalia Díaz-Rodríguez ◽  
Hannie Gijlers ◽  
Linda Tonolli
Keyword(s):  
Artificial Intelligence ◽  
Literature Review ◽  
Cultural Heritage ◽  
Conceptual Framework ◽  
Interaction Design ◽  
Participatory Design ◽  
Future Directions ◽  
Heritage Sites ◽  
Review Analysis

This paper reviews the literature concerning technology used for creating and delivering accessible museum and cultural heritage sites experiences. It highlights the importance of the delivery suited for everyone from different areas of expertise, namely interaction design, pedagogical and participatory design, and it presents how recent and future artificial intelligence (AI) developments can be used for this aim, i.e.,improving and widening online and in situ accessibility. From the literature review analysis, we articulate a conceptual framework that incorporates key elements that constitute museum and cultural heritage online experiences and how these elements are related to each other. Concrete opportunities for future directions empirical research for accessibility of cultural heritage contents are suggested and further discussed.

scholarly journals Twelve Years of the Italian Program to Enhance Relational and Communication Skills (PERCS)

2021 ◽  
Vol 18 (2) ◽  
pp. 439
Author(s):  
Lidia Borghi ◽  
Elaine C. Meyer ◽  
Elena Vegni ◽  
Roberta Oteri ◽  
Paolo Almagioni ◽  
...  
Keyword(s):  
Communication Skills ◽  
Repeated Measures ◽  
Behavioral Changes ◽  
Chi Square ◽  
Future Directions ◽  
Public And Private ◽  
Learner Centered ◽  
Difficult Conversations ◽  
Booster Sessions ◽  
Before And After

To describe the experience of the Italian Program to Enhance Relations and Communication Skills (PERCS-Italy) for difficult healthcare conversations. PERCS-Italy has been offered in two different hospitals in Milan since 2008. Each workshop lasts 5 h, enrolls 10–15 interdisciplinary participants, and is organized around simulations and debriefing of two difficult conversations. Before and after the workshops, participants rate their preparation, communication, relational skills, confidence, and anxiety on 5-point Likert scales. Usefulness, quality, and recommendation of the program are also assessed. Descriptive statistics, t-tests, repeated-measures ANOVA, and Chi-square were performed. A total of 72 workshops have been offered, involving 830 interdisciplinary participants. Participants reported improvements in all the dimensions (p < 0.001) without differences across the two hospitals. Nurses and other professionals reported a greater improvement in preparation, communication skills, and confidence, compared to physicians and psychosocial professionals. Usefulness, quality, and recommendation of PERCS programs were highly rated, without differences by discipline. PERCS-Italy proved to be adaptable to different hospital settings, public and private. After the workshops, clinicians reported improvements in self-reported competencies when facing difficult conversations. PERCS-Italy’s sustainability is based on the flexible format combined with a solid learner-centered approach. Future directions include implementation of booster sessions to maintain learning and the assessment of behavioral changes.

Facial Reanimation of Patients With Neurofibromatosis Type 2

2011 ◽  
Vol 70 (suppl_2) ◽  
pp. ons237-ons243 ◽  
Author(s):  
Kalpesh T. Vakharia ◽  
Doug Henstrom ◽  
Scott R. Plotkin ◽  
Mack Cheney ◽  
Tessa A. Hadlock
Keyword(s):  
Facial Paralysis ◽  
Neurofibromatosis Type ◽  
Neurofibromatosis Type 2 ◽  
Gracilis Muscle ◽  
Single Stage ◽  
Care Plan ◽  
Muscle Transfer ◽  
Facial Reanimation ◽  
Oral Commissure

ABSTRACT BACKGROUND: Neurofibromatosis type 2 (NF2) is a tumor suppressor syndrome defined by bilateral vestibular schwannomas. Facial paralysis, from either tumor growth or surgical intervention, is a devastating complication of this disorder and can contribute to disfigurement and corneal keratopathy. Historically, physicians have not attempted to treat facial paralysis in these patients. OBJECTIVE: To review our clinical experience with free gracilis muscle transfer for the purpose of facial reanimation in patients with NF2. METHODS: Five patients with NF2 and complete unilateral facial paralysis were referred to the facial nerve center at our institution. Charts and operative reports were reviewed; treatment details and functional outcomes are reported. RESULTS: Patients were treated between 2006 and 2009. Three patients were men and 2 were women. The age of presentation of debilitating facial paralysis ranged from 12 to 50 years. All patients were treated with a single-stage free gracilis muscle transfer for smile reanimation. Each obturator nerve of the gracilis was coapted to the masseteric branch of the trigeminal nerve. Measurement of oral commissure excursions at rest and with smile preoperatively and postoperatively revealed an improved and nearly symmetric smile in all cases. CONCLUSION: Management of facial paralysis is oftentimes overlooked when defining a care plan for NF2 patients who typically have multiple brain and spine tumors. The paralyzed smile may be treated successfully with single-stage free gracilis muscle transfer in the motivated patient.

scholarly journals A Survey on Bias and Fairness in Machine Learning

2021 ◽  
Vol 54 (6) ◽  
pp. 1-35
Author(s):  
Ninareh Mehrabi ◽  
Fred Morstatter ◽  
Nripsuta Saxena ◽  
Kristina Lerman ◽  
Aram Galstyan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Real World ◽  
State Of The Art ◽  
Future Directions ◽  
Discriminatory Behavior ◽  
Real World Applications ◽  
Near Future ◽  
Different Sources

With the widespread use of artificial intelligence (AI) systems and applications in our everyday lives, accounting for fairness has gained significant importance in designing and engineering of such systems. AI systems can be used in many sensitive environments to make important and life-changing decisions; thus, it is crucial to ensure that these decisions do not reflect discriminatory behavior toward certain groups or populations. More recently some work has been developed in traditional machine learning and deep learning that address such challenges in different subdomains. With the commercialization of these systems, researchers are becoming more aware of the biases that these applications can contain and are attempting to address them. In this survey, we investigated different real-world applications that have shown biases in various ways, and we listed different sources of biases that can affect AI applications. We then created a taxonomy for fairness definitions that machine learning researchers have defined to avoid the existing bias in AI systems. In addition to that, we examined different domains and subdomains in AI showing what researchers have observed with regard to unfair outcomes in the state-of-the-art methods and ways they have tried to address them. There are still many future directions and solutions that can be taken to mitigate the problem of bias in AI systems. We are hoping that this survey will motivate researchers to tackle these issues in the near future by observing existing work in their respective fields.

scholarly journals Reviewing the relationship between machines and radiology: the application of artificial intelligence

2021 ◽  
Vol 10 (2) ◽  
pp. 205846012199029
Author(s):  
Rani Ahmad
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Health Care Professionals ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Health Science ◽  
Computer Algorithms ◽  
Learning Models ◽  
Specificity And Sensitivity

Background The scope and productivity of artificial intelligence applications in health science and medicine, particularly in medical imaging, are rapidly progressing, with relatively recent developments in big data and deep learning and increasingly powerful computer algorithms. Accordingly, there are a number of opportunities and challenges for the radiological community. Purpose To provide review on the challenges and barriers experienced in diagnostic radiology on the basis of the key clinical applications of machine learning techniques. Material and Methods Studies published in 2010–2019 were selected that report on the efficacy of machine learning models. A single contingency table was selected for each study to report the highest accuracy of radiology professionals and machine learning algorithms, and a meta-analysis of studies was conducted based on contingency tables. Results The specificity for all the deep learning models ranged from 39% to 100%, whereas sensitivity ranged from 85% to 100%. The pooled sensitivity and specificity were 89% and 85% for the deep learning algorithms for detecting abnormalities compared to 75% and 91% for radiology experts, respectively. The pooled specificity and sensitivity for comparison between radiology professionals and deep learning algorithms were 91% and 81% for deep learning models and 85% and 73% for radiology professionals (p < 0.000), respectively. The pooled sensitivity detection was 82% for health-care professionals and 83% for deep learning algorithms (p < 0.005). Conclusion Radiomic information extracted through machine learning programs form images that may not be discernible through visual examination, thus may improve the prognostic and diagnostic value of data sets.

scholarly journals Schizophrenia: A Survey of Artificial Intelligence Techniques Applied to Detection and Classification

2021 ◽  
Vol 18 (11) ◽  
pp. 6099
Author(s):  
Joel Weijia Lai ◽  
Candice Ke En Ang ◽  
U. Rajendra Acharya ◽  
Kang Hao Cheong
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Daily Living ◽  
Learning Algorithms ◽  
Health Condition ◽  
Machine Learning Algorithms ◽  
Human Cognition ◽  
Computer Algorithms ◽  
Large Sets ◽  
Prevention Methods

Artificial Intelligence in healthcare employs machine learning algorithms to emulate human cognition in the analysis of complicated or large sets of data. Specifically, artificial intelligence taps on the ability of computer algorithms and software with allowable thresholds to make deterministic approximate conclusions. In comparison to traditional technologies in healthcare, artificial intelligence enhances the process of data analysis without the need for human input, producing nearly equally reliable, well defined output. Schizophrenia is a chronic mental health condition that affects millions worldwide, with impairment in thinking and behaviour that may be significantly disabling to daily living. Multiple artificial intelligence and machine learning algorithms have been utilized to analyze the different components of schizophrenia, such as in prediction of disease, and assessment of current prevention methods. These are carried out in hope of assisting with diagnosis and provision of viable options for individuals affected. In this paper, we review the progress of the use of artificial intelligence in schizophrenia.

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