Machine learning and orthodontics, current trends and the future opportunities: A scoping review

AbstractArtificial intelligence (AI) is the use of mathematical algorithms to mimic human cognitive abilities and to address difficult healthcare challenges including complex biological abnormalities like cancer. The exponential growth of AI in the last decade is evidenced to be the potential platform for optimal decision-making by super-intelligence, where the human mind is limited to process huge data in a narrow time range. Cancer is a complex and multifaced disorder with thousands of genetic and epigenetic variations. AI-based algorithms hold great promise to pave the way to identify these genetic mutations and aberrant protein interactions at a very early stage. Modern biomedical research is also focused to bring AI technology to the clinics safely and ethically. AI-based assistance to pathologists and physicians could be the great leap forward towards prediction for disease risk, diagnosis, prognosis, and treatments. Clinical applications of AI and Machine Learning (ML) in cancer diagnosis and treatment are the future of medical guidance towards faster mapping of a new treatment for every individual. By using AI base system approach, researchers can collaborate in real-time and share knowledge digitally to potentially heal millions. In this review, we focused to present game-changing technology of the future in clinics, by connecting biology with Artificial Intelligence and explain how AI-based assistance help oncologist for precise treatment.

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Adversity Interpreted: A Scoping Review of Adversity Appraisal Measurement

Trauma Violence & Abuse ◽

10.1177/1524838020985544 ◽

2021 ◽

pp. 152483802098554

Author(s):

Stephanie Gusler ◽

Jessy Guler ◽

Rachel Petrie ◽

Heather Marshall ◽

Daryl Cooley ◽

...

Keyword(s):

Life Events ◽

Psychological Adjustment ◽

Scoping Review ◽

Reliability And Validity ◽

Primary Source ◽

Future Research ◽

Measurement Tool ◽

Self Blame ◽

Traumatic Life Events ◽

The Future

Although evidence suggests that individuals’ appraisals (i.e., subjective interpretations) of adverse or traumatic life events may serve as a mechanism accounting for differences in adversity exposure and psychological adjustment, understanding this mechanism is contingent on our ability to reliably and consistently measure appraisals. However, measures have varied widely between studies, making conclusions about how best to measure appraisal a challenge for the field. To address this issue, the present study reviewed 88 articles from three research databases, assessing adults’ appraisals of adversity. To be included in the scoping review, articles had to meet the following criteria: (1) published no earlier than 1999, (2) available in English, (3) published as a primary source manuscript, and (4) included a measure assessing for adults’ (over the age of 18) subjective primary and/or secondary interpretations of adversity. Each article was thoroughly reviewed and coded based on the following information: study demographics, appraisal measurement tool(s), category of appraisal, appraisal dimensions (e.g., self-blame, impact, and threat), and the tool’s reliability and validity. Further, information was coded according to the type of adversity appraised, the time in which the appraised event occurred, and which outcomes were assessed in relation to appraisal. Results highlight the importance of continued examination of adversity appraisals and reveal which appraisal tools, categories, and dimensions are most commonly assessed for. These results provide guidance to researchers in how to examine adversity appraisals and what gaps among the measurement of adversity appraisal which need to be addressed in the future research.

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Integrating machine learning and blockchain to develop a system to veto the forgeries and provide efficient results in education sector

Visual Computing for Industry Biomedicine and Art ◽

10.1186/s42492-021-00084-y ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Dhruvil Shah ◽

Devarsh Patel ◽

Jainish Adesara ◽

Pruthvi Hingu ◽

Manan Shah

Keyword(s):

Machine Learning ◽

Education Sector ◽

Student Records ◽

Job Roles ◽

Student Data ◽

Disruptive Technologies ◽

Valid Data ◽

The Future ◽

Student Achievements ◽

The University

AbstractAlthough the education sector is improving more quickly than ever with the help of advancing technologies, there are still many areas yet to be discovered, and there will always be room for further enhancements. Two of the most disruptive technologies, machine learning (ML) and blockchain, have helped replace conventional approaches used in the education sector with highly technical and effective methods. In this study, a system is proposed that combines these two radiant technologies and helps resolve problems such as forgeries of educational records and fake degrees. The idea here is that if these technologies can be merged and a system can be developed that uses blockchain to store student data and ML to accurately predict the future job roles for students after graduation, the problems of further counterfeiting and insecurity in the student achievements can be avoided. Further, ML models will be used to train and predict valid data. This system will provide the university with an official decentralized database of student records who have graduated from there. In addition, this system provides employers with a platform where the educational records of the employees can be verified. Students can share their educational information in their e-portfolios on platforms such as LinkedIn, which is a platform for managing professional profiles. This allows students, companies, and other industries to find approval for student data more easily.

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Machine learning-guided phenotyping of dilated cardiomyopathy and treatment of heart failure by antisense oligonucleotides: the future has begun

European Heart Journal ◽

10.1093/eurheartj/ehaa1063 ◽

2021 ◽

Vol 42 (2) ◽

pp. 139-142

Author(s):

Filippo Crea

Keyword(s):

Machine Learning ◽

Heart Failure ◽

Dilated Cardiomyopathy ◽

Antisense Oligonucleotides ◽

The Future

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Scoping Review of Thermal Comfort Research in Colombia

Buildings ◽

10.3390/buildings11060232 ◽

2021 ◽

Vol 11 (6) ◽

pp. 232

Author(s):

Juan Manuel Medina ◽

Carolina M. Rodriguez ◽

Maria Camila Coronado ◽

Lina Maria Garcia

Keyword(s):

Thermal Comfort ◽

Scoping Review ◽

Architectural Design ◽

Urban Climate ◽

Academic Community ◽

Additional Information ◽

The Future ◽

Art Research ◽

Occupant Satisfaction

The analysis of thermal comfort in buildings, energy consumption, and occupant satisfaction is crucial to influencing the architectural design methodologies of the future. However, research in these fields in developing countries is sectorised. Most times, the standards to study and assess thermal comfort such as ASHRAE Standard 55, EN 15251, and ISO 7730 are insufficient and not appropriate for the geographical areas of application. This article presents a scoping review of published work in Colombia, as a representative case study, to highlight the state-of-the-art, research trends, gaps, and potential areas for further development. It examines the amount, origin, extent, and content of research and peer-reviewed documentation over the last decades. The findings allow new insights regarding the preferred models and the evaluation tools that have been used to date and that are recommended to use in the future. It also includes additional information regarding the most and least studied regions, cities, and climates in the country. This work could be of interest for the academic community and policymakers in the areas related to indoor and urban climate management and energy efficiency.

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