scholarly journals Artificial Intelligence in Public Health Dentistry

2021 ◽  
Vol 5 (9) ◽  
pp. RV1-RV5
Author(s):  
Sahrish Tariq ◽  
Nidhi Gupta ◽  
Preety Gupta ◽  
Aditi Sharma
Keyword(s):  
Public Health ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Clinical Practice ◽  
Correct Diagnosis ◽  
Scientific Research ◽  
Modern Science ◽  
Appropriate Technology ◽  
Human Element ◽  
New Knowledge

The educational needs must drive the development of the appropriate technology”. They should not be viewed as toys for enthusiasts. Nevertheless, the human element must never be dismissed. Scientific research will continue to offer exciting technologies and effective treatments. For the profession and the patients, it serves to benefit fully from modern science, new knowledge and technologies must be incorporated into the mainstream of dental education. The technologies of modern science have astonished and intrigued our imagination. Correct diagnosis is the key to a successful clinical practice. In this regard, adequately trained neural networks can be a boon to diagnosticians, especially in conditions having multifactorial etiology.

scholarly journals Big data, machine learning and artificial intelligence: a neurologist’s guide

2020 ◽  
pp. practneurol-2020-002688
Author(s):  
Stephen D Auger ◽  
Benjamin M Jacobs ◽  
Ruth Dobson ◽  
Charles R Marshall ◽  
Alastair J Noyce
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Big Data ◽  
Clinical Practice ◽  
Clinical Data ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Basic Principles ◽  
Biological Neural Networks

Modern clinical practice requires the integration and interpretation of ever-expanding volumes of clinical data. There is, therefore, an imperative to develop efficient ways to process and understand these large amounts of data. Neurologists work to understand the function of biological neural networks, but artificial neural networks and other forms of machine learning algorithm are likely to be increasingly encountered in clinical practice. As their use increases, clinicians will need to understand the basic principles and common types of algorithm. We aim to provide a coherent introduction to this jargon-heavy subject and equip neurologists with the tools to understand, critically appraise and apply insights from this burgeoning field.

scholarly journals Artificial Intelligence Explained for Nonexperts

2020 ◽  
Vol 24 (01) ◽  
pp. 003-011 ◽  
Author(s):  
Narges Razavian ◽  
Florian Knoll ◽  
Krzysztof J. Geras
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Clinical Practice ◽  
Medical Imaging ◽  
Deep Neural Networks ◽  
Large Data ◽  
Large Data Sets ◽  
Natural Images ◽  
Data Sets ◽  
Current State

AbstractArtificial intelligence (AI) has made stunning progress in the last decade, made possible largely due to the advances in training deep neural networks with large data sets. Many of these solutions, initially developed for natural images, speech, or text, are now becoming successful in medical imaging. In this article we briefly summarize in an accessible way the current state of the field of AI. Furthermore, we highlight the most promising approaches and describe the current challenges that will need to be solved to enable broad deployment of AI in clinical practice.

scholarly journals COVID-19 Impact Estimation on ICU Capacity at Andalusia, Spain, Using Artificial Intelligence

2020 ◽  
Author(s):  
F. A. Prieto ◽  
N. G. Baltas ◽  
L. Rios-Pena ◽  
P. Rodriguez
Keyword(s):  
Public Health ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
Public Hospitals ◽  
Learning Tool ◽  
Infected Population ◽  
Available Resources

Abstract The objective of this article is to evaluate the spread of the virus and estimate the cases of infected population in need of urgent hospitalization, in order to provide sufficient resources to public health. To this end, a deep learning tool based on deep neural networks (DNN) was developed to predict COVID-19 infection and the need for urgent hospitalization in some of the infected patients. We associated the available resources of public hospitals and evaluated the need to increase them after the possible substantial increase caused by SARS-CoV-2 by provinces in the regions of Andalusia, Spain.

APPLICATION OF ARTIFICIAL INTELLIGENCE-BASED IMAGE ANALYSIS IN BIOINFORMATICS

2021 ◽  
Author(s):  
Zlatan Car ◽  
◽  
Nikola Anđelić ◽  
Ivan Lorencin ◽  
Jelena Musulin ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Computer Vision ◽  
Clinical Practice ◽  
Image Data ◽  
Algorithm Analysis ◽  
Diagnostic Systems ◽  
Common Procedure ◽  
Magnetic Resonance Imaging Mri ◽  
Standard Practices

The collection of image data is an extremely common procedure in clinical practice today. Many of the diagnostic approaches generate such data – computed tomography (CT), X-ray radiography, magnetic resonance imaging (MRI), and others. This data collection process allows for the use of computer vision approaches to be applied with the goal of analysis and diagnostics. Artificial Intelligence (AI) based algorithms have repeatedly been shown to be the best performing computer vision algorithms, in many fields including medicine. AI-based – or more precisely machine learning (ML) based, algorithms have capabilities which allow them to learn the patterns contained in the data from the data itself. Among the best performing algorithms are artificial neural networks (ANNs), or more precisely convolutional neural networks (CNNs). Their pitfall is the need for the large amounts of data – but as it has been previously mentioned, the amount of data collected in today’s clinical practice is large and ever increasing. This allows for the development of Smart Diagnostic systems which are meant to serve as support systems to the health professionals. In this paper first, the standard practices and review of the field is given – with the focus on challenges and best practices. Then, multiple examples of the research applying AI-based algorithm analysis are given – including diagnostics of various cancer types (bladder and oral) as well as COVID-19 severity diagnostics and image quality determination.

P1846HISTORY OF ARTIFICIAL INTELLIGENCE AND ITS APPLICATION IN NEPHROLOGY

2020 ◽  
Vol 35 (Supplement_3) ◽  
Author(s):  
Danijela Tasic ◽  
Milos Milovancevic ◽  
Katarina Djordjevic ◽  
Slobodanka Galovic ◽  
Zorica Dimitrijevic ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Clinical Practice ◽  
Technological Development ◽  
Basic Research ◽  
Good Clinical Practice ◽  
Von Neumann ◽  
Hermann Von Helmholtz ◽  
Norbert Wiener ◽  
New Good

Abstract Background and Aims The application of artificial intelligence and neural networks in medicine is used to help solve problems that cannot be handled by the classical approach. The common name “cybernetics” encompassed the fields of management, information technology and biomedicine, but these disciplines continued to evolve independently due to the explosion of new knowledge. Over time, the development of neural networks has been turbulent and is now widely used in various fields of medicine and even in nephrology. The aim of the paper is to analyze the history of the development of artificial intelligence and its application in nephrology. Method Data were collected from books, magazines, encyclopedias and databases. Results Basic research on cybernetics and medicine was done by Golgi and Kelley doctors after Isaak Newton and Hermann von Helmholtz. The first theoretical mathematical models were derived in 1943 by Warren Mc Culloch and Walter Pitts. A few years later, a more contemporary contribution to the development of neural networks was given by Norbert Wiener and John von Neumann because they thought that research into biomedicine based on human brain function would be very interesting. In addition, in 1948 Norbert Wiener was the first to publish a work explaining the term cybernetics. At that time, the first experiments were made and new theories in the field of artificial intelligence were put forward by Marvin Misnki. The first training of neurons and the basis of all methods for training neurons was described by the Canadian Donald O Hebb. After the first successful neurocomputer in 1957, on which Rosenblatt worked, scientists have perfected various models of neural networks to this day. So far, mostly retrospective studies have been done in clinical nephrology, transplantation and dialysis with the help of algorithms used in neural networks. Particularly complex nephrologic patient relationships as well as assistance with timely implementation of new good clinical practice guidelines, patient prediction in at least the next month, and patient selection for palliative care are just some segments in nephrology that require the introduction of such tools into daily clinical practice with the aim of sensitive patient populations have better treatment outcomes, with physicians having more comprehensive insight and control over the mass of data. Conclusion Today‘s application of artificial intelligence in nephrology is based on retrospective research. The dizzying rise in technological development so far will allow the use of cybernetics and available tools based on neural network algorithms to enable and improve the nephrologists’ dedication and effectiveness.

scholarly journals On modern methods of automated diagnosis of skin tumors in clinical practice

2020 ◽  
pp. 76-78
Author(s):  
V. Yu. Sergeev ◽  
Yu. Yu. Sergeev ◽  
O. B. Tamrazova ◽  
V. G. Nikitaev ◽  
A. N. Pronichev
Keyword(s):  
Public Health ◽  
Artificial Intelligence ◽  
Clinical Practice ◽  
Public Health Service ◽  
Automated Diagnosis ◽  
Skin Cancers ◽  
Modern Methods ◽  
Number Of Publications ◽  
New Algorithms ◽  
Artificial Intelligence Systems

Despite the existence of many algorithms for automated diagnosis of melanoma and other skin cancers, these remain almost inaccessible to public health service. A small number of publications on the efficacy of existing artificial intelligence systems marks the problems of their implementation into current examination routines in dermatology and oncology. New algorithms and software solutions as well as studies demonstrating their diagnostic accuracy on compatible and verifiable clinical material are still in demand.

scholarly journals ARTIFICIAL INTELLIGENCE ON THE IDENTIFICATION OF DIABETES-RELATED OSTEOMETABOLIC DISORDERS

2021 ◽  
Vol 06 (01) ◽  
pp. 73-74
Author(s):  
Sain Safarova Sain Safarova
Keyword(s):  
Artificial Intelligence ◽  
Diabetes Mellitus ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Clinical Practice ◽  
Premature Death ◽  
Diabetic Patients ◽  
New Therapeutic Approaches ◽  
Patients With Diabetes ◽  
Artificial Neural

Introduction: Complications of diabetes mellitus (DM) are of great medical and social importance, as they cause severe disability and premature death of patients with diabetes mellitus. Bone remodeling disorders occurring in diabetes increase the risk of fractures and move the problem of diabetic osteopathy beyond the narrow specialty, making it the subject of extensive scientific research [1-3]. However, osteopathy remains an underestimated complication and is not considered in most diabetes guidelines. The fact that diabetic osteopathy is often asymptomatic leads to the fact that diabetic patients turn their attention to this pathology late and turn to a specialist, as a rule, already having a high degree of progression of this complication. One of the important issues is the timely detection and prediction of bone changes in diabetes mellitus. The introduction of artificial intelligence technologies (AIT) into clinical practice is one of the main trends in world medicine [4]. AIT and Artificial Neural Networks (ANN) can fundamentally change the criteria for diagnosis and prognosis, which will contribute to the development of new therapeutic approaches, improve the efficiency of medical care and reduce costs [5]. The prospects for using ANN can potentially provide almost limitless technical possibilities. Considering the possibilities of using these technologies in clinical practice, we came to the conclusion that the development and implementation of forecasting systems based on the construction of a model of an intelligent decision support system based on the apparatus of artificial neural networks is able to analyze clinical and laboratory indicators of patients with diabetes mellitus (DM) in order to predict the values of qualitative and quantitative indicators assessing the state of bone tissue.

17. Boundaries or overlap: An examination of the community-oriented clinical practice of community medicine specialists and family/general practitioners

2007 ◽  
Vol 30 (4) ◽  
pp. 36
Author(s):  
M. L. Russell ◽  
L. McIntyre
Keyword(s):  
Public Health ◽  
Primary Care ◽  
Clinical Practice ◽  
Community Health ◽  
Royal College ◽  
Community Medicine ◽  
Work Setting ◽  
Public Health Medicine ◽  
Work Settings ◽  
Main Work

We compared the work settings and “community-oriented clinical practice” of Community Medicine (CM) specialists and family physicians/general practitioners (FP). We conducted secondary data analysis of the 2004 National Physician Survey (NPS) to examine main work setting and clinical activity reported by 154 CM (40% of eligible CM in Canada) and 11,041 FP (36% of eligible FP in Canada). Text data from the specialist questionnaire related to “most common conditions that you treat” were extracted from the Master database for CM specialists, and subjected to thematic analysis and coded. CM specialists were more likely than FP to engage in “community medicine/public health” (59.7% vs 15.3%); while the opposite was found for primary care (13% vs. 78.2%). CM specialists were less likely to indicate a main work setting of private office/clinic/community health centre/community hospital than were FP (13.6% vs. 75.6%). Forty-five percent of CM provided a response to “most common conditions treated” with the remainder either leaving the item blank or indicating that they did not treat individual patients. The most frequently named conditions in rank order were: psychiatric disorders; public health program/activity; respiratory problems; hypertension; and metabolic disorders (diabetes). There is some overlap in the professional activities and work settings of CM specialists and FP. The “most commonly treated conditions” suggest that some CM specialists may be practicing primary care as part of the Royal College career path of “community-oriented clinical practice.” However the “most commonly treated conditions” do not specifically indicate an orientation of that practice towards “an emphasis on health promotion and disease prevention” as also specified by the Royal College for that CM career path. This raises questions about the appropriateness of the current training requirements and career paths as delineated for CM specialists by the Royal College of Physicians & Surgeons of Canada. Bhopal R. Public health medicine and primary health care: convergent, divergent, or parallel paths? J Epidemiol Community Health 1995; 49:113-6. Pettersen BJ, Johnsen R. More physicians in public health: less public health work? Scan J Public Health 2005; 33:91-8. Stanwell-Smith R. Public health medicine in transition. J Royal Society of Medicine 2001; 94(7):319-21.

Using neural networks and artificial intelligence as a modern method of glaucoma diagnosis

2020 ◽  
pp. 27-28
Author(s):  
A.B. Movsisyan ◽  
◽  
A.V. Kuroyedov ◽  
G.A. Ostapenko ◽  
S.V. Podvigin ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Modern Method ◽  
Glaucoma Diagnosis

Актуальность. Определяется увеличением заболеваемости глаукомой во всем мире как одной из основных причин снижения зрения и поздней постановкой диагноза при имеющихся выраженных изменений со стороны органа зрения. Цель. Повысить эффективность диагностики глаукомы на основании оценки диска зрительного нерва и перипапиллярной сетчатки нейросетью и искусственным интеллектом. Материал и методы. Для обучения нейронной сети были выделены четыре диагноза: первый – «норма», второй – начальная глаукома, третий – развитая стадия глаукомы, четвертый – глаукома далеко зашедшей стадии. Классификация производилась на основе снимков глазного дна: область диска зрительного нерва и перипапиллярной сетчатки. В результате классификации входные данные разбивались на два класса «норма» и «глаукома». Для целей обучения и оценки качества обучения, множество данных было разбито на два подмножества: тренировочное и тестовое. В тренировочное подмножество были включены 8193 снимка с глаукомными изменениями диска зрительного нерва и «норма» (пациенты без глаукомы). Стадии заболевания были верифицированы согласно действующей классификации первичной открытоугольной глаукомы 3 (тремя) экспертами со стажем работы от 5 до 25 лет. В тестовое подмножество были включены 407 снимков, из них 199 – «норма», 208 – с начальной, развитой и далекозашедшей стадиями глаукомы. Для решения задачи классификации на «норма»/«глаукома» была выбрана архитектура нейронной сети, состоящая из пяти сверточных слоев. Результаты. Чувствительность тестирования дисков зрительных нервов с помощью нейронной сети составила 0,91, специфичность – 0,93. Анализ полученных результатов работы показал эффективность разработанной нейронной сети и ее преимущество перед имеющимися методами диагностики глаукомы. Выводы. Использование нейросетей и искусственного интеллекта является современным, эффективным и перспективным методом диагностики глаукомы.

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