Personal identification with artificial intelligence under COVID-19 crisis: a scoping review

Abstract Background Artificial intelligence is useful for building objective and rapid personal identification systems. It is important to research and develop personal identification methods as social and institutional infrastructure. A critical consideration during the coronavirus disease 2019 pandemic is that there is no contact between the subjects and personal identification systems. The aim of this study was to organize the recent 5-year development of contactless personal identification methods that use artificial intelligence. Methods This study used a scoping review approach to map the progression of contactless personal identification systems using artificial intelligence over the past 5 years. An electronic systematic literature search was conducted using the PubMed, Web of Science, Cochrane Library, CINAHL, and IEEE Xplore databases. Studies published between January 2016 and December 2020 were included in the study. Results By performing an electronic literature search, 83 articles were extracted. Based on the PRISMA flow diagram, 8 eligible articles were included in this study. These eligible articles were divided based on the analysis targets as follows: (1) face and/or body, (2) eye, and (3) forearm and/or hand. Artificial intelligence, including convolutional neural networks, contributed to the progress of research on contactless personal identification methods. Conclusions This study clarified that contactless personal identification methods using artificial intelligence have progressed and that they have used information obtained from the face and/or body, eyes, and forearm and/or hand.

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Reporting of post-operative rehabilitation interventions for Total knee arthroplasty: a scoping review

BMC Musculoskeletal Disorders ◽

10.1186/s12891-021-04460-w ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Nora Bakaa ◽

Lu Hsi Chen ◽

Lisa Carlesso ◽

Julie Richardson ◽

Luciana Macedo

Keyword(s):

Total Knee Arthroplasty ◽

Knee Arthroplasty ◽

Scoping Review ◽

Literature Search ◽

Exercise Adherence ◽

Risk Of Bias ◽

Cochrane Library ◽

Quality Of Reporting ◽

Exercise Interventions ◽

Total Knee

Abstract Objective The aim of this study was to evaluate the completeness of reporting of exercise adherence and exercise interventions delivered as part of clinical trials of post-operative total knee replacement (TKA) rehabilitation. Design: Scoping review Literature search A literature search was conducted in PubMed, EMBASE, AMED, CINAHL, SPORTDiscus and Cochrane Library. Study selection criteria All randomized controlled trials (RCT) that examined post-operative exercise-based interventions for total knee arthroplasty were eligible for inclusion. Studies that were multifactorial or contained exercise interventions for both hip and knee arthroplasty were also included. Data synthesis The definition, type of measurement used and outcome for exercise adherence were collected and analyzed descreptively. Quality of reporting of exercise interventions were assessed using the Consensus for Exercise Reporting Tool (CERT) and the Cochrane Risk of Bias Tool. Results There were a total of 112 RCTs included in this review. The majority of RCTs (63%, n = 71) did not report exercise adherence. Only 23% (n = 15) of studies provided a definition of adherence. RCTs were of poor quality, with 85% (n = 95) of studies having high or unclear risk of bias. Reporting of exercise interventions was poor, with only 4 items (of 19) (21%) of the CERT adequately reported (88–99%), with other items not fulfilled on at least 60% of the RCTs. There were no RCTs that had fulfilled all the criteria for the CERT. Conclusion The RCTs included in this study poorly reported exercise adherence, as well as description of the post-operative TKA rehabilitation intervention. Future RCTs should use valid and reliable measures of adherence and a proper tool for reporting of exercise interventions (e.g., CERT, TiDER). Pre-registration OSF:https://osf.io/9ku8a/

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Artificial Intelligence and Ethics in Dentistry: A Scoping Review

Journal of Dental Research ◽

10.1177/00220345211013808 ◽

2021 ◽

pp. 002203452110138

Author(s):

C.M. Mörch ◽

S. Atsu ◽

W. Cai ◽

X. Li ◽

S.A. Madathil ◽

...

Keyword(s):

Artificial Intelligence ◽

Scoping Review ◽

Ethical Issues ◽

Data Extraction ◽

Cochrane Library ◽

Data Sets ◽

Ethical Challenges ◽

Internal Validation ◽

Neural Learning ◽

Learning Techniques

Dentistry increasingly integrates artificial intelligence (AI) to help improve the current state of clinical dental practice. However, this revolutionary technological field raises various complex ethical challenges. The objective of this systematic scoping review is to document the current uses of AI in dentistry and the ethical concerns or challenges they imply. Three health care databases (MEDLINE [PubMed], SciVerse Scopus, and Cochrane Library) and 2 computer science databases (ArXiv, IEEE Xplore) were searched. After identifying 1,553 records, the documents were filtered, and a full-text screening was performed. In total, 178 studies were retained and analyzed by 8 researchers specialized in dentistry, AI, and ethics. The team used Covidence for data extraction and Dedoose for the identification of ethics-related information. PRISMA guidelines were followed. Among the included studies, 130 (73.0%) studies were published after 2016, and 93 (52.2%) were published in journals specialized in computer sciences. The technologies used were neural learning techniques for 75 (42.1%), traditional learning techniques for 76 (42.7%), or a combination of several technologies for 20 (11.2%). Overall, 7 countries contributed to 109 (61.2%) studies. A total of 53 different applications of AI in dentistry were identified, involving most dental specialties. The use of initial data sets for internal validation was reported in 152 (85.4%) studies. Forty-five ethical issues (related to the use AI in dentistry) were reported in 22 (12.4%) studies around 6 principles: prudence (10 times), equity (8), privacy (8), responsibility (6), democratic participation (4), and solidarity (4). The ratio of studies mentioning AI-related ethical issues has remained similar in the past years, showing that there is no increasing interest in the field of dentistry on this topic. This study confirms the growing presence of AI in dentistry and highlights a current lack of information on the ethical challenges surrounding its use. In addition, the scarcity of studies sharing their code could prevent future replications. The authors formulate recommendations to contribute to a more responsible use of AI technologies in dentistry.

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Characteristics of SARS-CoV-2 aerosol dispersion in indoor air: scoping review

Research Society and Development ◽

10.33448/rsd-v10i4.14300 ◽

2021 ◽

Vol 10 (4) ◽

pp. e44310414300

Author(s):

Anyele Albuquerque Lima ◽

Izabelly Carollynny Maciel Nunes ◽

José Leandro da Silva Duarte ◽

Lucas Meili ◽

Patricia de Carvalho Nagliate ◽

...

Keyword(s):

Viral Load ◽

Relative Humidity ◽

Air Temperature ◽

Indoor Air ◽

Scoping Review ◽

Grey Literature ◽

Flow Diagram ◽

Minimum Size ◽

Management Tool ◽

Cochrane Library

Background: SARS-CoV-2 is the infectious agent responsible for COVID-19, its transmission occurs through the release of respiratory droplets and aerosols. Aim: Identify the main characteristics of SARS-CoV-2 aerosols dispersion in indoor air. Methods: Scoping Review was conducted using the databases: National Library of Medicines – MEDLINE/Pubmed, Scopus, Web of Science, Virtual Health Library (VHL) and Cochrane Library, the search in gray literature was performed on Google Scholar, OpenGrey and Grey Literature Report, from March to September 2020. The descriptors used were "coronavirus" and "aerosol". Data were selected and screened following the protocol established by the The Joanna Briggs Institute, PRISMA flow diagram and EndNote reference management tool. Findings: Ten papers were selected, which presented characteristics that could influence the SARS-CoV-2 aerosols dispersion, with highlight to: aerosol origin; viral load identified in the air (2.86 copies/liter of air); aerosol particle size with viral load (0.25 μm); dispersion (10.00 m); air stay time (3 h); influence of air temperature and relative humidity. Conclusion: Aerosol particles containing SARS-CoV-2 may have infectious viral charge, presenting a minimum size up to 0.25 μm, being able to reach up to 10 m of distance and survive in the air for a few hours. The variables air temperature and relative humidity did not present consistent evidence to influence the dispersion of SARS-CoV-2 aerosols.

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Non-invasive methods for the assessment of biomarkers and their correlation with radiographic maturity indicators — a scoping review

Progress in Orthodontics ◽

10.1186/s40510-021-00372-6 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Veena GV ◽

Tulika Tripathi

Keyword(s):

Scoping Review ◽

Literature Search ◽

Skeletal Maturity ◽

Skeletal Maturation ◽

Cochrane Library ◽

Subjective Perception ◽

Cochrane Central Register ◽

Non Invasive ◽

Pubertal Growth ◽

Significant Difference

Abstract Background Detection of skeletal maturity is vital in orthodontic treatment timing and planning. Traditional methods include hand-wrist radiography and cervical vertebral maturation index (CVMI). Though the radiographic methods are well established and routinely used to assess skeletal maturation, they carry the drawback of subjective perception and low reproducibility. With evolving concepts, skeletal maturation has been assessed quantitatively through biomarkers obtained from saliva, gingival crevicular fluid (GCF), and urine. The scoping review aims to explore the various biomarkers assessed through non-invasive methods and their correlation with radiographic skeletal maturity. Methodology The literature search was carried out on MEDLINE via Pubmed, Cochrane Library (Cochrane database of systematic reviews), Cochrane central register of controlled trials (CENTRAL), Google Scholar, Semantic Scholar, ScienceDirect, and Opengrey.eu for articles up to and including November 2020. Pertinent articles were selected based on inclusion and exclusion criteria. The results were tabulated based on the type of sample collected, the biomarker assessed, method of sample collection, and the radiographic method used. Results The literature search resulted in 12 relevant articles. Among all the studies, 10 studies showed that the concentration of biomarkers increases during the pubertal growth peak. On the contrary, 2 articles showed no significant difference between the levels of biomarkers and pubertal growth peak. Conclusion It can be concluded that the level of biomarkers increases during the pubertal growth spurt and can provide a quantitative way of assessing skeletal maturity.

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A scoping review of the Clinical Frailty Scale

BMC Geriatrics ◽

10.1186/s12877-020-01801-7 ◽

2020 ◽

Vol 20 (1) ◽

Author(s):

Sophie Church ◽

Emily Rogers ◽

Kenneth Rockwood ◽

Olga Theou

Keyword(s):

Scoping Review ◽

Literature Search ◽

Research Evidence ◽

Web Of Science ◽

Cochrane Library ◽

Publication Date ◽

Clinical Frailty Scale ◽

And Function ◽

Comprehensive Literature Search ◽

Frailty Score

Abstract Background Frailty is increasingly recognized as an important construct which has health implications for older adults. The Clinical Frailty Scale (CFS) is a judgement-based frailty tool that evaluates specific domains including comorbidity, function, and cognition to generate a frailty score ranging from 1 (very fit) to 9 (terminally ill). The aim of this scoping review is to identify and document the nature and extent of research evidence related to the CFS. Methods We performed a comprehensive literature search to identify original studies that used the Clinical Frailty Scale. Medline OVID, Scopus, Web of Science, CINAHL, PsycINFO, Cochrane Library and Embase were searched from January 2005 to March 2017. Articles were screened by two independent reviewers. Data extracted included publication date, setting, demographics, purpose of CFS assessment, and outcomes associated with CFS score. Results Our search yielded 1688 articles of which 183 studies were included. Overall, 62% of studies were conducted after 2015 and 63% of the studies measured the CFS in hospitalized patients. The association of the CFS with an outcome was examined 526 times; CFS was predictive in 74% of the cases. Mortality was the most common outcome examined with CFS being predictive 87% of the time. CFS was associated with comorbidity 73% of the time, complications 100%, length of stay 75%, falls 71%, cognition 94%, and function 91%. The CFS was associated with other frailty scores 94% of the time. Conclusions This scoping review revealed that the CFS has been widely used in multiple settings. The association of CFS score with clinical outcomes highlights its utility in the care of the aging population.

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Health Equity in Artificial Intelligence and Primary Care Research: Protocol for a Scoping Review (Preprint)

10.2196/preprints.27799 ◽

2021 ◽

Author(s):

Jonathan Xin Wang ◽

Sulaiman Somani ◽

Jonathan H Chen ◽

Sara Murray ◽

Urmimala Sarkar

Keyword(s):

Artificial Intelligence ◽

Primary Care ◽

Health Equity ◽

Scoping Review ◽

Underserved Populations ◽

Care Delivery ◽

Data Extraction ◽

Cochrane Library ◽

Preventative Care ◽

The Impact

BACKGROUND Though artificial intelligence (AI) has potential to augment the patient-physician relationship in primary care, bias in intelligent healthcare systems has the potential to differentially impact vulnerable patient populations. OBJECTIVE The purpose of this scoping review is to summarize the extent to which AI systems in primary care examine the inherent bias towards or against vulnerable populations and appraise how these systems have mitigated the impact of such biases during their development. METHODS We will conduct a search update from an existing scoping review to identify AI and primary care articles in the following databases: Medline-OVID,Embase,CINAHL, Cochrane Library, Web of Science, Scopus, IEEE Xplore, ACM Digital Library, MathSciNet, AAAI, and arXiv. Two screeners will independently review all abstracts, titles and full-texts. The team will extract data using structured data extraction form and synthesize the results according to PRISMA-Scr guidelines. RESULTS This review will provide an assessment of the current state of healthcare equity within AI for primary care. Specifically, we will identify the degree to which vulnerable patients have been included, assess how bias is interpreted and documented, and understand the extent harmful biases are addressed. As of October 2020, the scoping review is in the title and abstract screening stage. The results are expected to be submitted for publication in fall of 2021. CONCLUSIONS AI applications in primary care are becoming an increasingly common tool in health care delivery, including in preventative care efforts for underserved populations. This scoping review aims to understand to what extent AI-primary care studies employ a health equity lens and take steps to mitigate bias.

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Scoping review protocol on non-pharmacological interventions for interpersonal and self-directed violence in adults with severe mental illness

BMJ Open ◽

10.1136/bmjopen-2020-037006 ◽

2020 ◽

Vol 10 (9) ◽

pp. e037006

Author(s):

Maria Concepcion Moreno-Calvete ◽

Ivan Ruiz-Ibañez ◽

Jose Juan Uriarte-Uriarte

Keyword(s):

Mental Health ◽

Mental Illness ◽

Severe Mental Illness ◽

Systematic Reviews ◽

Scoping Review ◽

Interpersonal Violence ◽

Flow Diagram ◽

Cochrane Library ◽

Pharmacological Interventions ◽

Review Protocol

IntroductionViolence committed by people with mental illness has implications for mental health policy and clinical practice. Several strategies to reduce the risk of aggressive and violent behaviour have been proposed, and these include non-pharmacological interventions. There is, however, a need to identify which of these interventions are effective, and as a first step, we will conduct a scoping review to identify non-pharmacological interventions for self-directed or interpersonal violence in adults with severe mental illness across different conditions and settings.Methods and analysisThis is a scoping review protocol. The review will include any randomised controlled trials (RCTs) and cluster RCTs that assess the efficacy of interventions on self-directed or interpersonal violence with no restrictions on the control treatment in people with severe mental illness in any setting. No restrictions will be applied in terms of language or date of publication. To identify studies, a search will be performed in the following databases: Embase, MEDLINE (via PubMed), PsycINFO, CINAHL, LILACS, SciELO, Cochrane Library, Web of Science, Scopus, ProQuest, Epistemonikos and databases of clinical trials. The Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement will be followed for reporting the findings, including the use of a PRISMA flow diagram. A standardised form will be used to extract data from studies. The findings will be classified using conceptual categories that will be specified in detail and a descriptive summary of the main results will be created. Moreover, it will be assessed whether the studies identified have been included in systematic reviews or meta-analyses and the results will be used to generate a conceptual map.Ethics and disseminationNo patients or other participants will be involved in this study. We will prepare a manuscript for publication in a peer-reviewed journal and the results will be presented at mental health conferences.

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Early Detection of Sepsis Using Artificial Intelligence: A Scoping Review Protocol

10.21203/rs.3.rs-22474/v1 ◽

2020 ◽

Cited By ~ 1

Author(s):

Stefan Candefjord ◽

Ivana Pepic ◽

Robert Feldt ◽

Lars Ljungström ◽

Richard Torkar ◽

...

Keyword(s):

Artificial Intelligence ◽

Decision Support ◽

Early Detection ◽

Scoping Review ◽

Web Search ◽

Scientific Journal ◽

Case Fatality ◽

Cochrane Library ◽

Suspected Sepsis ◽

Wide Range

Abstract Background: Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection. To decrease the high case fatality rates and morbidity for sepsis and septic shock, there is a need to increase the accuracy of early detection of suspected sepsis in prehospital and emergency department settings. This may be achieved by developing risk prediction decision support systems based on artificial intelligence. Methods: The overall aim of this scoping review is to summarize the literature on existing methods for early detection of sepsis using artificial intelligence. The review will be performed using the framework formulated by Arksey and O’Malley and further developed by Levac and colleagues. To identify primary studies and reviews that are suitable to answer our research questions, a comprehensive literature collection will be compiled by searching several sources. Databases/web search engines that will be used are PubMed, Web of Science, Scopus, IEEE Xplore, Google Scholar, Cochrane Library and ACM Digital Library. Furthermore, clinical studies that have completed patient recruitment and reported results found in the database ClinicalTrials.gov will be considered. The term artificial intelligence is viewed broadly and a wide range of machine learning and mathematical models suitable as base for decision support will be evaluated. Two members of the team will test the framework on a sample of included studies to ensure that the coding framework is suitable and can be consistently applied. Analysis of collected data will provide a descriptive summary and thematic analysis. The reported results will convey knowledge about the state of current research and innovation for using artificial intelligence to detect sepsis in early phases of the medical care chain. Ethics and dissemination: The methodology used here is based on the use of publicly available information and does not need ethical approval. It aims at aiding further research towards digital solutions for disease detection and health innovation. Results will be extracted into a review report for submission to a peer-reviewed scientific journal. Results will be shared with relevant local and national authorities and disseminated in additional appropriate formats such as conferences, lectures, and press releases.

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Patient-centred infertility care: a scoping review protocol

BMJ Open ◽

10.1136/bmjopen-2019-032266 ◽

2019 ◽

Vol 9 (11) ◽

pp. e032266

Author(s):

Hana Hasan Webair ◽

Tengku Alina Tengku Ismail ◽

Shaiful Bahari Ismail ◽

Norhayati Mohd Noor

Keyword(s):

Scoping Review ◽

Literature Search ◽

Data Extraction ◽

Grey Literature ◽

Decision Makers ◽

Cochrane Library ◽

Quality Healthcare ◽

Study Selection ◽

History Of ◽

Review Protocol

IntroductionPatient-centred infertility care (PCIC) is one of the quality indicators of effective fertility care. The application of this indicator requires a clear definition from the patient’s perspective. This proposed scoping review aims to explore the extent and nature of published scientific literature on PCIC in the past decade, identify gaps in the literature and define PCIC from infertile patients’ perspectives.Methods and analysisWe will conduct the proposed scoping review following the method of Arksey and O’Malley. The literature search will include studies published from 2009 to 2019, and will be conducted on the MEDLINE, PsycINFO, Scopus, Cochrane Library, and Cumulative Index to Nursing and Allied Health Literature (CINAHL) databases; reference lists will be mined for literature not contained on these databases. A grey literature search will also be conducted. To be included in the review, studies should have been conducted on people with a history of infertility, with a focus on patient-centred fertility care. Studies that have not been published in full text and studies published in languages other than English will be excluded. After study selection, data will be charted in a prepared form. We will analyse the data using descriptive numerical and qualitative thematic analyses to answer the research questions. NVivo V.12 will be used for data extraction.Ethics and disseminationThis work does not warrant any ethical or safety concerns. This scoping review will synthesise existing literature on PCIC, and the results will be published to be readily available for clinical audiences and policymakers. These findings may support clinicians and decision-makers in applying PCIC, thereby promoting high-quality healthcare in the concerned population.

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Early detection of sepsis using artificial intelligence: a scoping review protocol

Systematic Reviews ◽

10.1186/s13643-020-01561-w ◽

2021 ◽

Vol 10 (1) ◽

Author(s):

Ivana Pepic ◽

Robert Feldt ◽

Lars Ljungström ◽

Richard Torkar ◽

Daniel Dalevi ◽

...

Keyword(s):

Artificial Intelligence ◽

Decision Support ◽

Early Detection ◽

Scoping Review ◽

Web Search ◽

Scientific Journal ◽

Case Fatality ◽

Cochrane Library ◽

Suspected Sepsis ◽

Wide Range

Abstract Background Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection. To decrease the high case fatality rates and morbidity for sepsis and septic shock, there is a need to increase the accuracy of early detection of suspected sepsis in prehospital and emergency department settings. This may be achieved by developing risk prediction decision support systems based on artificial intelligence. Methods The overall aim of this scoping review is to summarize the literature on existing methods for early detection of sepsis using artificial intelligence. The review will be performed using the framework formulated by Arksey and O’Malley and further developed by Levac and colleagues. To identify primary studies and reviews that are suitable to answer our research questions, a comprehensive literature collection will be compiled by searching several sources. Constrictions regarding time and language will have to be implemented. Therefore, only studies published between 1 January 1990 and 31 December 2020 will be taken into consideration, and foreign language publications will not be considered, i.e., only papers with full text in English will be included. Databases/web search engines that will be used are PubMed, Web of Science Platform, Scopus, IEEE Xplore, Google Scholar, Cochrane Library, and ACM Digital Library. Furthermore, clinical studies that have completed patient recruitment and reported results found in the database ClinicalTrials.gov will be considered. The term artificial intelligence is viewed broadly, and a wide range of machine learning and mathematical models suitable as base for decision support will be evaluated. Two members of the team will test the framework on a sample of included studies to ensure that the coding framework is suitable and can be consistently applied. Analysis of collected data will provide a descriptive summary and thematic analysis. The reported results will convey knowledge about the state of current research and innovation for using artificial intelligence to detect sepsis in early phases of the medical care chain. Ethics and dissemination The methodology used here is based on the use of publicly available information and does not need ethical approval. It aims at aiding further research towards digital solutions for disease detection and health innovation. Results will be extracted into a review report for submission to a peer-reviewed scientific journal. Results will be shared with relevant local and national authorities and disseminated in additional appropriate formats such as conferences, lectures, and press releases.

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