scholarly journals Language, Speech, and Facial Expression Features for Artificial Intelligence–Based Detection of Cancer Survivors’ Depression: Scoping Meta-Review

10.2196/30439 ◽  
2021 ◽  
Vol 8 (12) ◽  
pp. e30439
Author(s):  
Urška Smrke ◽  
Izidor Mlakar ◽  
Simon Lin ◽  
Bojan Musil ◽  
Nejc Plohl
Keyword(s):  
Artificial Intelligence ◽  
Facial Expression ◽  
Cancer Survivors ◽  
Successful Implementation ◽  
Inclusion Criteria ◽  
Psychological States ◽  
Scoping Reviews ◽  
Meta Review ◽  
Opening Up ◽  
Detection Of Depression

Background Cancer survivors often experience disorders from the depressive spectrum that remain largely unrecognized and overlooked. Even though screening for depression is recognized as essential, several barriers prevent its successful implementation. It is possible that better screening options can be developed. New possibilities have been opening up with advances in artificial intelligence and increasing knowledge on the connection of observable cues and psychological states. Objective The aim of this scoping meta-review was to identify observable features of depression that can be intercepted using artificial intelligence in order to provide a stepping stone toward better recognition of depression among cancer survivors. Methods We followed a methodological framework for scoping reviews. We searched SCOPUS and Web of Science for relevant papers on the topic, and data were extracted from the papers that met inclusion criteria. We used thematic analysis within 3 predefined categories of depression cues (ie, language, speech, and facial expression cues) to analyze the papers. Results The search yielded 1023 papers, of which 9 met the inclusion criteria. Analysis of their findings resulted in several well-supported cues of depression in language, speech, and facial expression domains, which provides a comprehensive list of observable features that are potentially suited to be intercepted by artificial intelligence for early detection of depression. Conclusions This review provides a synthesis of behavioral features of depression while translating this knowledge into the context of artificial intelligence–supported screening for depression in cancer survivors.

scholarly journals Language, Speech, and Facial Expression Features for Artificial Intelligence–Based Detection of Cancer Survivors’ Depression: Scoping Meta-Review (Preprint)

2021 ◽  
Author(s):  
Urška Smrke ◽  
Izidor Mlakar ◽  
Simon Lin ◽  
Bojan Musil ◽  
Nejc Plohl
Keyword(s):  
Artificial Intelligence ◽  
Facial Expression ◽  
Cancer Survivors ◽  
Successful Implementation ◽  
Inclusion Criteria ◽  
Psychological States ◽  
Scoping Reviews ◽  
Meta Review ◽  
Opening Up ◽  
Detection Of Depression

BACKGROUND Cancer survivors often experience disorders from the depressive spectrum that remain largely unrecognized and overlooked. Even though screening for depression is recognized as essential, several barriers prevent its successful implementation. It is possible that better screening options can be developed. New possibilities have been opening up with advances in artificial intelligence and increasing knowledge on the connection of observable cues and psychological states. OBJECTIVE The aim of this scoping meta-review was to identify observable features of depression that can be intercepted using artificial intelligence in order to provide a stepping stone toward better recognition of depression among cancer survivors. METHODS We followed a methodological framework for scoping reviews. We searched SCOPUS and Web of Science for relevant papers on the topic, and data were extracted from the papers that met inclusion criteria. We used thematic analysis within 3 predefined categories of depression cues (ie, language, speech, and facial expression cues) to analyze the papers. RESULTS The search yielded 1023 papers, of which 9 met the inclusion criteria. Analysis of their findings resulted in several well-supported cues of depression in language, speech, and facial expression domains, which provides a comprehensive list of observable features that are potentially suited to be intercepted by artificial intelligence for early detection of depression. CONCLUSIONS This review provides a synthesis of behavioral features of depression while translating this knowledge into the context of artificial intelligence–supported screening for depression in cancer survivors.

Artificial Intelligence for epigenetics: towards personalized medicine

2020 ◽  
Vol 27 ◽  
Author(s):  
Giulia De Riso ◽  
Sergio Cocozza
Keyword(s):  
Biological Sciences ◽  
Artificial Intelligence ◽  
Personalized Medicine ◽  
Molecular Mechanisms ◽  
Genomic Sequence ◽  
Health Care Interventions ◽  
Genome Wide ◽  
Genetic And Environmental Factors ◽  
Opening Up ◽  
Omic Data

: Epigenetics is a field of biological sciences focused on the study of reversible, heritable changes in gene function not due to modifications of the genomic sequence. These changes are the result of a complex cross-talk between several molecular mechanisms, that is in turn orchestrated by genetic and environmental factors. The epigenetic profile captures the unique regulatory landscape and the exposure to environmental stimuli of an individual. It thus constitutes a valuable reservoir of information for personalized medicine, which is aimed at customizing health-care interventions based on the unique characteristics of each individual. Nowadays, the complex milieu of epigenomic marks can be studied at the genome-wide level thanks to massive, highthroughput technologies. This new experimental approach is opening up new and interesting knowledge perspectives. However, the analysis of these complex omic data requires to face important analytic issues. Artificial Intelligence, and in particular Machine Learning, are emerging as powerful resources to decipher epigenomic data. In this review, we will first describe the most used ML approaches in epigenomics. We then will recapitulate some of the recent applications of ML to epigenomic analysis. Finally, we will provide some examples of how the ML approach to epigenetic data can be useful for personalized medicine.

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.

Interventions to optimise medication prescribing and adherence in older people with cancer: A systematic scoping review

2021 ◽  
Vol 29 (Supplement_1) ◽  
pp. i23-i24
Author(s):  
M Murphy ◽  
K Bennett ◽  
S Ryan ◽  
C Hughes ◽  
A Lavan ◽  
...  
Keyword(s):  
Older Adults ◽  
Scoping Review ◽  
Grey Literature ◽  
Intervention Development ◽  
Control Group ◽  
Sample Population ◽  
Inclusion Criteria ◽  
Scoping Reviews ◽  
Medication Prescribing ◽  
Broad Overview

Abstract Introduction Older adults with cancer often require multiple medications (polypharmacy) comprising cancer-specific treatments, supportive care medications (e.g. analgesics) and medications for pre-existing conditions. The reported prevalence of polypharmacy in older adults with cancer ranges from 13–92% (1). Increasing numbers of medications pose risks of potentially inappropriate prescribing and medication non-adherence. Aim The aim of this scoping review was to provide an overview of evaluations of interventions to optimise medication prescribing and/or adherence in older adults with cancer, with a particular focus on the interventions, study populations and outcome measures that have been assessed in previous evaluations. Methods Four databases (PubMed, EMBASE, CINAHL, PsycINFO) were searched from inception to 29th November 2019 using relevant search terms (e.g. cancer, older adults, prescribing, adherence). Eligible studies evaluated interventions seeking to improve medication prescribing and/or adherence in older adults (≥65 years) with an active cancer diagnosis using a comparative evaluation (e.g. inclusion of a control group). All outcomes for studies that met inclusion criteria were included in the review. Two reviewers independently screened relevant abstracts for inclusion and performed data extraction. As a scoping review aims to provide a broad overview of existing literature, formal assessments of methodological quality of included studies were not undertaken. Extracted data were collated using tables and accompanying narrative descriptive summaries. The review was reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews (PRISMA-ScR) guidelines (2). Results The electronic searches yielded 21,136 citations (Figure 1). Nine studies met inclusion criteria. Included studies consisted of five randomised controlled trials (RCTs), including one cluster RCT, and four before-and-after study designs. Studies were primarily conducted in oncology clinics, ranging from single study sites to 109 oncology clinics. Sample sizes ranged from 33 to 4844 patients. All studies had a sample population with a mean/median age of ≥65 years, however, only two studies focused specifically on older populations. Interventions most commonly involved patient education (n=6), and were delivered by pharmacists or nurses. Five studies referred to the intervention development process and no studies reported any theoretical underpinning. Three studies reported on prescribing-related outcomes and seven studies reported on adherence-related outcomes, using different terminology and a range of assessments. Prescribing-related outcomes comprised assessments of medication appropriateness (using Beers criteria), drug-related problems and drug interactions. Adherence-related outcomes included assessments of self-reported medication adherence and calculation of patients’ medication possession ratio. Conclusion The main strength of this scoping review is that it provides a broad overview of the existing literature on interventions aimed at optimising medication prescribing and adherence in older adults with cancer. The review highlights a lack of robust studies specifically targeting this patient population and limited scope to pool outcome data across included studies. Limitations of the review were that searches were restricted to English language publications and no grey literature was searched. Future research should focus specifically on older patients with cancer, and exercise rigour during intervention development, evaluation and reporting in order to generate findings that could inform future practice. References 1. Maggiore RJ, Gross CP, Hurria A. Polypharmacy in older adults with cancer. The oncologist. 2010;15(5):507–22. 2. Tricco AC, Lillie E, Zarin W, O'Brien KK, Colquhoun H, Levac D, et al. PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation. Ann Intern Med. 2018;169(7):467–73.

School Administrators and Physical Educators: A Scoping Review of Perceptions and Partnerships From 2000 to 2020

2021 ◽  
pp. 105268462199061
Author(s):  
Scott McNamara ◽  
Matthew Townsley ◽  
Kelly Hangauer
Keyword(s):  
Scoping Review ◽  
School Administrators ◽  
Physical Educators ◽  
Inclusion Criteria ◽  
Comprehensive Overview ◽  
Physically Active ◽  
Full Inclusion ◽  
Teachers Perceptions ◽  
Scoping Reviews ◽  
Perceptions Of School

Physical education (PE) is an academic subject that delivers students a standards-based program designed to foster the knowledge and skills needed to be physically active for a lifetime. Although there is a dearth of research that has examined school administrators’ perceptions and interactions with PE, it has been reported that school administrators often are a barrier that disrupts effective PE programming. This study aimed to conduct a scoping review of the literature to capture a comprehensive view of the peer-reviewed research that has focused on physical educators’ collaboration with and perceptions of school administrators, and literature related to school administrators’ collaboration and perceptions of physical educators. Preferred Reporting Items for Systematic Reviews extension for Scoping Reviews Checklist guided this investigation. Seven databases were searched, and 29 articles met the full inclusion criteria. This scoping review provides a comprehensive overview of the evidence and research trends; nonetheless, the heterogeneity of the studies and limited literature on this topic make it difficult to form any substantial conclusions. The need for additional research is especially true for research examining PE teachers’ perceptions and interactions with school administrators, as only three of the identified studies in this review focused on this topic. The recognition of these gaps in the literature may be important to the fields of educational leadership and PE, as it may lead to more concerted efforts to examine how these fields interact and how they can collaborate more effectively.

scholarly journals Brain-inspired model for decision-making in the selection of beneficial information patterns among signals received by an unpredictable information-development environment

2021 ◽  
Author(s):  
Alireza Asgari ◽  
yvan beauregard
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Health And Safety ◽  
Vital Role ◽  
Successful Implementation ◽  
Development Environment ◽  
Brain Functions ◽  
Operational Costs ◽  
Industrial Operations ◽  
The Brain

With its diversification in products and services, today’s marketplace makes competition wildly dynamic and unpredictable for industries. In such an environment, daily operational decision-making has a vital role in producing value for products and services while avoiding the risk of loss and hazard to human health and safety. However, it makes a large portion of operational costs for industries. The main reason is that decision-making belongs to the operational tasks dominated by humans. The less involvement of humans, as a less controllable entity, in industrial operation could also favorable for improving workplace health and safety. To this end, artificial intelligence is proposed as an alternative to doing human decision-making tasks. Still, some of the functional characteristics of the brain that allow humans to make decisions in unpredictable environments like the current industry, especially knowledge generalization, are challenging for artificial intelligence. To find an applicable solution, we study the principles that underlie the human brain functions in decision-making. The relative base functions are realized to develop a model in a simulated unpredictable environment for a decision-making system that could decide which information is beneficial to choose. The method executed to build our model's neuronal interactions is unique that aims to mimic some simple functions of the brain in decision-making. It has the potential to develop for systems acting in the higher abstraction levels and complexities in real-world environments. This system and our study will help to integrate more artificial intelligence in industrial operations and settings. The more successful implementation of artificial intelligence will be the steeper decreasing operational costs and risks.

scholarly journals The Importance Of Gender And Facial Information Science On Information Systems Design

2011 ◽  
Vol 7 (8) ◽  
Author(s):  
Ralph Reilly ◽  
Andrew Nyaboga ◽  
Carl Guynes
Keyword(s):  
Artificial Intelligence ◽  
Facial Expression ◽  
Facial Expressions ◽  
Information Science ◽  
Systems Design ◽  
Font Size ◽  
Men And Women ◽  
Facial Information ◽  
Computer Scientists ◽  
The Subject

<p class="MsoNormal" style="text-align: justify; margin: 0in 0.5in 0pt;"><span style="layout-grid-mode: line; font-family: &quot;Times New Roman&quot;,&quot;serif&quot;;"><span style="font-size: x-small;">Facial Information Science is becoming a discipline in its own right, attracting not only computer scientists, but graphic animators and psychologists, all of whom require knowledge to understand how people make and interpret facial expressions. (Zeng, 2009). Computer advancements enhance the ability of researchers to study facial expression. Digitized computer-displayed faces can now be used in studies. Current advancements are facilitating not only the researcher&rsquo;s ability to accurately display information, but recording the subject&rsquo;s reaction automatically.<span style="mso-spacerun: yes;">&nbsp; </span><span style="mso-bidi-font-weight: bold;"><span style="mso-spacerun: yes;">&nbsp;</span></span>With increasing interest in Artificial Intelligence and man-machine communications, what importance does the gender of the user play in the design of today&rsquo;s multi-million dollar applications? Does research suggest that men and women respond to the &ldquo;gender&rdquo; of computer displayed images differently? Can this knowledge be used effectively to design applications specifically for use by men or women? This research is an attempt to understand these questions while studying whether automatic, or pre-attentive, processing plays a part in the identification of the facial expressions.</span></span></p>

scholarly journals Research on the impact of artificial intelligence on the business model of an organization

E-Management ◽  
2021 ◽  
Vol 3 (4) ◽  
pp. 26-36
Author(s):  
A. A. Dashkov ◽  
Yu. O. Nesterova
Keyword(s):  
Artificial Intelligence ◽  
Information And Communication Technologies ◽  
Business Model ◽  
Digital Transformation ◽  
Successful Implementation ◽  
New Ways Of Working ◽  
Use Of Technology ◽  
Physical Assets ◽  
The Moment ◽  
The Impact

The digital transformation of our world and the inevitable interaction between people, digital technologies and physical assets create a rapidly changing and complex environment that requires organizations to be more flexible, better fit and ready to accept new ways of working. Businesses are coming to realize the need for change to operate successfully in the digital age. In the period of global digitalization, information and communication technologies are one of the most important aspects of existence for a business, which makes it more efficient, efficient and allows you to respond quickly to a rapidly changing external environment, as well as customer needs. At the moment, there is a high interest in the possibilities of artificial intelligence for use in business tasks in the world, as there are already examples of successful implementation, when Artificial Intelligence and machine learning radically change the way they work and increase the profit of organizations in different countries.The purpose of this study is to consider how artificial intelligence affects the value proposition and how the elements of the business model change when using this technology. The paper gives the existing examples of the use of technology, the consequences of its application and the emerging prospects for the use of Artificial Intelligence as one of the advanced technologies of digital transformation.

The Role of Artificial Intelligence (AI) in Cancer Treatment in Low-Middle Income Countries: The Watson for Oncology (WfO) Experience (Preprint)

2021 ◽  
Author(s):  
Angela Rui ◽  
Srinivas Emani ◽  
Hermano Alexandre Lima Rocha ◽  
Rubina F. Rizvi ◽  
Sergio Ferreira Juaçaba ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Cancer Treatment ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Cultural Barriers ◽  
Successful Implementation ◽  
Middle Income ◽  
Middle Income Countries ◽  
Cancer Management ◽  
Low Middle Income Countries

UNSTRUCTURED As technology continues to improve, healthcare systems have the opportunity to utilize a variety of innovative tools for decision making that extend beyond traditional clinical decision support systems (CDSSs). The feasibility and efficacy integrating artificial intelligence (AI) systems into medical practice has shown variable success, especially in resource-poor areas. In this paper, we cover the existing challenges surrounding cancer treatment in low-middle income countries (LMICs). By focusing on the implementation of an AI-based CDSS for oncology, we aim to demonstrate how AI can be both beneficial and challenging for cancer management globally. Additionally, we summarize current physician perspectives from China, India, Brazil, Thailand, and Mexico in regard to their experiences and recommendations for improving the system. By doing so, we hope to highlight the need for additional research on user experience and unique cultural barriers for the successful implementation of AI in LMICs.

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