scholarly journals Artificial Intelligence in Nutrients Science Research: A Review

Nutrients ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 322
Author(s):  
Jarosław Sak ◽  
Magdalena Suchodolska
Keyword(s):  
Artificial Intelligence ◽  
Clinical Medicine ◽  
Science Research ◽  
Medical Diagnostics ◽  
Food Composition ◽  
Nutritional Epidemiology ◽  
Global Network ◽  
Biomedical Sciences ◽  
Learning Abilities ◽  
Artificial Neural Network Ann

Artificial intelligence (AI) as a branch of computer science, the purpose of which is to imitate thought processes, learning abilities and knowledge management, finds more and more applications in experimental and clinical medicine. In recent decades, there has been an expansion of AI applications in biomedical sciences. The possibilities of artificial intelligence in the field of medical diagnostics, risk prediction and support of therapeutic techniques are growing rapidly. The aim of the article is to analyze the current use of AI in nutrients science research. The literature review was conducted in PubMed. A total of 399 records published between 1987 and 2020 were obtained, of which, after analyzing the titles and abstracts, 261 were rejected. In the next stages, the remaining records were analyzed using the full-text versions and, finally, 55 papers were selected. These papers were divided into three areas: AI in biomedical nutrients research (20 studies), AI in clinical nutrients research (22 studies) and AI in nutritional epidemiology (13 studies). It was found that the artificial neural network (ANN) methodology was dominant in the group of research on food composition study and production of nutrients. However, machine learning (ML) algorithms were widely used in studies on the influence of nutrients on the functioning of the human body in health and disease and in studies on the gut microbiota. Deep learning (DL) algorithms prevailed in a group of research works on clinical nutrients intake. The development of dietary systems using AI technology may lead to the creation of a global network that will be able to both actively support and monitor the personalized supply of nutrients.

scholarly journals Artificial Intelligence Applications in Nutrition and Dietetics

2021 ◽  
pp. 125-127
Author(s):  
Izzet Ulker ◽  
Feride Ayyildiz
Keyword(s):  
Artificial Intelligence ◽  
Dietary Intake ◽  
Clinical Medicine ◽  
Food Preferences ◽  
Thought Processes ◽  
Learning Abilities ◽  
Assessment And Monitoring ◽  
Important Field ◽  
Nutrition And Dietetics ◽  
Artificial Intelligence Application

Artificial intelligence (AI) is a branch of computer science whose purpose is to imitate thought processes, learning abilities, and knowledge management. The increasing number of applications in experimental and clinical medicine is striking. An artificial intelligence application in the field of nutrition and dietetics is a fairly new and important field. Different apps related to nutrition are offered to the use of individuals. The importance of individual nutrition has also triggered the increase in artificial intelligence apps. It is thought that different apps such as food preferences and dietary intake can play an important role in health promotion. Researchers may have some difficulties such as remembering the frequency or amount of intake in assessment of dietary intake. Some applications used in the assessment of food consumption contribute to overcoming these difficulties. Besides, these apps facilitate the work of researchers and provide more reliable results than traditional methods. The apps to be used in the field of nutrition and dietetics should be developed by considering the disadvantages. It is thought that artificial intelligence applications will contribute to both the improvement of health and the assessment and monitoring of nutritional status.

Oxford Handbook of Medical Sciences

2021 ◽  
Keyword(s):  
Clinical Medicine ◽  
System Development ◽  
Science Research ◽  
Biomedical Science ◽  
Cell Physiology ◽  
Biomedical Sciences ◽  
Medical Sciences ◽  
Allied Health Professions ◽  
Endocrine Organs ◽  
Scientific Principles

The Oxford Handbook of Medical Sciences is written by biomedical scientists and clinicians to be the definitive guide to the fundamental scientific principles that underpin medicine and the biomedical sciences. It provides a clear and easily digestible account of basic cell physiology, biochemistry, and molecular and medical genetics, followed by chapters integrating the traditional pillars of biomedicine (anatomy, physiology, biochemistry, pathology, and pharmacology) for each of the major systems and processes of the human body: nerve and muscle, musculoskeletal system, respiratory and cardiovascular systems, urinary system, digestive system, endocrine organs, reproductive system, development from fertilization to birth, neuroanatomy and neurophysiology, infection and immunity, and the growth of tissues and organs. Also included are chapters on medicine and society and techniques used in biomedical science research. In its third edition, the Oxford Handbook of Medical Sciences is now fully illustrated in colour, and cross-referenced to the Oxford Handbook of Clinical Medicine, tenth edition, Oxford Handbook of Clinical Specialities, eleventh edition, and Oxford Handbook of Practical Drug Therapy, second edition. Its concise writing style makes it an invaluable source of information for practitioners and students in medicine, biomedical sciences, and the allied health professions.

A Cohort study to compare Admission criteria and Academic performance among preclinical students at The Copperbelt University School of Medicine: complete enumeration (Preprint)

2019 ◽  
Author(s):  
Andrew Mwila
Keyword(s):  
Academic Performance ◽  
Cohort Study ◽  
Clinical Medicine ◽  
Average Score ◽  
First Year ◽  
Admission Criteria ◽  
Biomedical Sciences ◽  
Dental Surgery ◽  
School Of Medicine ◽  
Better Than

BACKGROUND The Copperbelt University is the second public University in Zambia. The School of Medicine has four major programs namely; Bachelor of Medicine and Surgery, Bachelor of Dental Surgery, Bachelor of Clinical Medicine and Bachelor of Biomedical sciences. The Copperbelt University School of Medicine runs a five-year training program for both the BDS and the MBCHB programs. Students are admitted into the Medical school after successfully completing their first year at the Main campus in the School of Natural Sciences with an average of 4 B grades or higher (B grade is a mark of 65 to 74%). OBJECTIVE The study was done to determine the association between admission criteria and academic performance among preclinical students. Hence, the study compares the academic performance among preclinical students admitted into the Bachelor of Dental Surgery and Bachelor of Medicine and Surgery at the Copperbelt University School of Medicine. METHODS This is a retrospective cohort study conducted at Michael Chilufya Sata School of medicine Campus. A pilot study was conducted with 30 BDS and 30 MBCHB students and the obtained information helped determine the sample size. SPSS was used to analyze the data. The study period lasted approximately 7 weeks at a cost of K1621. RESULTS In 2014, there was an improvement in average performance between 2nd and 3rd year for each program. An average score of 15.4 (SD 4.2) was obtained in 3rd year compared to 12.8 (SD 4.9) in 2nd year (p<0.001). Meanwhile, 3rd MB ChB mean score was 12.6 (SD 3.7) compared to 10.7 (SD 3.6) in 2nd years (p<0.05). However, in 2016, both programs, 3rd year mean scores were lower than 2nd year (MB ChB 2nd year mean score was 12.0 (SD 4.3) compared to 3rd year with a mean score of 9.5 (SD 4.5), p<0.001; BDS 2nd year mean score was 10.6 (SD 4.0) compared to 3rd year mean score of 8.2 (SD 3.4), p<0.01. On average MB ChB students performed better than BDS students in all the years (p<0.05), except in 2016 when the results were comparable. CONCLUSIONS Results from the study shows that entry criteria has a correlation to academic performance as students admitted with higher grades perform much better than those with lower grades.

Top 100 most-cited articles on medical imaging applications of artificial intelligence: a bibliometric analysis (Preprint)

2020 ◽  
Author(s):  
Ying Liu ◽  
Ziyan Yu ◽  
Shuolan Jing ◽  
Honghu Jiang ◽  
Chunxia Wang
Keyword(s):  
Artificial Intelligence ◽  
Medical Imaging ◽  
Bibliometric Analysis ◽  
Clinical Medicine ◽  
Medical Physics ◽  
Medical Image Analysis ◽  
The United States ◽  
Independent Learning ◽  
Learning Ability ◽  
Core Technology

BACKGROUND Artificial intelligence (AI) has penetrated into almost every aspect of our lives and is rapidly changing our way of life. Recently, the new generation of AI taking machine learning and particularly deep convolutional neural network theories as the core technology, has stronger learning ability and independent learning evolution ability, combined with a large amount of learning data, breaks through the bottleneck limit of model accuracy, and makes the model efficient use. OBJECTIVE To identify the 100 most cited papers in artificial intelligence in medical imaging, we performed a comprehensive bibliometric analysis basing on the literature search on Web of Science Core Collection (WoSCC). METHODS The 100 top-cited articles published in “AI, Medical imaging” journals were identified using the Science Citation Index Database. The articles were further reviewed, and basic information was collected, including the number of citations, journals, authors, publication year, and field of study. RESULTS The highly cited articles in AI were cited between 72 and 1,554 times. The majority of them were published in three major journals: IEEE Transactions on Medical Imaging, Medical Image Analysis and Medical Physics. The publication year ranged from 2002 to 2019, with 66% published in a three-year period (2016 to 2018). Publications from the United States (56%) were the most heavily cited, followed by those from China (15%) and Netherlands (10%). Radboud University Nijmegen from Netherlands, Harvard Medical School in USA, and The Chinese University of Hong Kong in China produced the highest number of publications (n=6). Computer science (42%), clinical medicine (35%), and engineering (8%) were the most common fields of study. CONCLUSIONS Citation analysis in the field of artificial intelligence in medical imaging reveals interesting information about the topics and trends negotiated by researchers and elucidates which characteristics are required for a paper to attain a “classic” status. Clinical science articles published in highimpact specialized journals are most likely to be cited in the field of artificial intelligence in medical imaging.

scholarly journals Promise and Perils of Big Data and Artificial Intelligence in Clinical Medicine and Biomedical Research

2018 ◽  
Vol 123 (12) ◽  
pp. 1282-1284 ◽  
Author(s):  
Fatima Rodriguez ◽  
David Scheinker ◽  
Robert A. Harrington
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Biomedical Research ◽  
Clinical Medicine

scholarly journals Artificial Intelligence in Thyroid Field. A Comprehensive Review

Cancers ◽  
2021 ◽  
Vol 13 (19) ◽  
pp. 4740
Author(s):  
Fabiano Bini ◽  
Andrada Pica ◽  
Laura Azzimonti ◽  
Alessandro Giusti ◽  
Lorenzo Ruinelli ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Cognitive Abilities ◽  
Medical Diagnostics ◽  
Critical Discussion ◽  
Research Field ◽  
Response To Treatment ◽  
Key Concepts ◽  
Mathematical Algorithms

Artificial intelligence (AI) uses mathematical algorithms to perform tasks that require human cognitive abilities. AI-based methodologies, e.g., machine learning and deep learning, as well as the recently developed research field of radiomics have noticeable potential to transform medical diagnostics. AI-based techniques applied to medical imaging allow to detect biological abnormalities, to diagnostic neoplasms or to predict the response to treatment. Nonetheless, the diagnostic accuracy of these methods is still a matter of debate. In this article, we first illustrate the key concepts and workflow characteristics of machine learning, deep learning and radiomics. We outline considerations regarding data input requirements, differences among these methodologies and their limitations. Subsequently, a concise overview is presented regarding the application of AI methods to the evaluation of thyroid images. We developed a critical discussion concerning limits and open challenges that should be addressed before the translation of AI techniques to the broad clinical use. Clarification of the pitfalls of AI-based techniques results crucial in order to ensure the optimal application for each patient.

scholarly journals Artificial intelligence in clinical medicine and dentistry

2017 ◽  
Vol 74 (3) ◽  
pp. 267-272 ◽  
Author(s):  
Milan Miladinovic ◽  
Branko Mihailovic ◽  
Dragan Mladenovic ◽  
Milos Duka ◽  
Dusan Zivkovic ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Clinical Medicine

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scholarly journals Assessment of Artificial Intelligence Models for Estimating Lengths of Gradually Varied Flow Profiles

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Majid Niazkar ◽  
Farshad Hajizadeh mishi ◽  
Gökçen Eryılmaz Türkkan
Keyword(s):  
Artificial Intelligence ◽  
Water Surface ◽  
Direct Method ◽  
Step Method ◽  
Channel Networks ◽  
Intelligence Models ◽  
Artificial Intelligence Models ◽  
Surface Profiles ◽  
Artificial Neural Network Ann ◽  
Steep Slopes

The study of water surface profiles is beneficial to various applications in water resources management. In this study, two artificial intelligence (AI) models named the artificial neural network (ANN) and genetic programming (GP) were employed to estimate the length of six steady GVF profiles for the first time. The AI models were trained using a database consisting of 5154 dimensionless cases. A comparison was carried out to assess the performances of the AI techniques for estimating lengths of 330 GVF profiles in both mild and steep slopes in trapezoidal channels. The corresponding GVF lengths were also calculated by 1-step, 3-step, and 5-step direct step methods for comparison purposes. Based on six metrics used for the comparative analysis, GP and the ANN improve five out of six metrics computed by the 1-step direct step method for both mild and steep slopes. Moreover, GP enhanced GVF lengths estimated by the 3-step direct step method based on three out of six accuracy indices when the channel slope is higher and lower than the critical slope. Additionally, the performances of the AI techniques were also investigated depending on comparing the water depth of each case and the corresponding normal and critical grade lines. Furthermore, the results show that the more the number of subreaches considered in the direct method, the better the results will be achieved with the compensation of much more computational efforts. The achieved improvements can be used in further studies to improve modeling water surface profiles in channel networks and hydraulic structure designs.

scholarly journals Design of a Microwave Lowpass – Bandpass Filter using Deep Learning and Artificial Intelligence

2021 ◽  
Vol 3 (1) ◽  
pp. 1-16
Author(s):  
Saeed Roshani ◽  
◽  
Hossein Heshmati ◽  
Sobhan Roshani ◽  
◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Insertion Loss ◽  
Dual Band ◽  
Transmission Zeros ◽  
Compact Size ◽  
Electrical Devices ◽  
Low Pass ◽  
Artificial Neural Network Ann ◽  
Better Than

In this paper, a lowpass – bandpass dual band microwave filter is designed by using deep learning and artificial intelligence. The designed filter has compact size and desirable pass bands. In the proposed filter, the resonators with Z-shaped and T-shaped lines are used to design the low pass channel, while coupling lines, stepped impedance resonators and open ended stubs are utilized to design the bandpass channel. Artificial neural network (ANN) and deep learning (DL) technique has been utilized to extract the proposed filter transfer function, so the values of the transmission zeros can be located in the desired frequency. This technique can also be used for the other electrical devices. The lowpass channel cut off frequency is 1 GHz, with better than 0.2 dB insertion loss. Also, the bandpass channel main frequency is designed at 2.4 GHz with 0.5 dB insertion loss in the passband.

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