scholarly journals Artificial intelligence/machine learning in respiratory medicine and potential role in asthma and COPD diagnosis

Author(s):  
Alan Kaplan ◽  
Hui Cao ◽  
J. Mark FitzGerald ◽  
Nick Iannotti ◽  
Eric Yang ◽  
...  
Thorax ◽  
2020 ◽  
Vol 75 (8) ◽  
pp. 695-701 ◽  
Author(s):  
Sherif Gonem ◽  
Wim Janssens ◽  
Nilakash Das ◽  
Marko Topalovic

The past 5 years have seen an explosion of interest in the use of artificial intelligence (AI) and machine learning techniques in medicine. This has been driven by the development of deep neural networks (DNNs)—complex networks residing in silico but loosely modelled on the human brain—that can process complex input data such as a chest radiograph image and output a classification such as ‘normal’ or ‘abnormal’. DNNs are ‘trained’ using large banks of images or other input data that have been assigned the correct labels. DNNs have shown the potential to equal or even surpass the accuracy of human experts in pattern recognition tasks such as interpreting medical images or biosignals. Within respiratory medicine, the main applications of AI and machine learning thus far have been the interpretation of thoracic imaging, lung pathology slides and physiological data such as pulmonary function tests. This article surveys progress in this area over the past 5 years, as well as highlighting the current limitations of AI and machine learning and the potential for future developments.


2020 ◽  
Vol 14 (6) ◽  
pp. 559-564 ◽  
Author(s):  
Evgeni Mekov ◽  
Marc Miravitlles ◽  
Rosen Petkov

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 655-655
Author(s):  
Walter Boot

Abstract The Gerontological Society of America is celebrating its75th anniversary and in those75 years the world has undergone an amazing technological revolution. During this period, computers transformed from systems that once filled entire rooms to much more powerful devices that fit in our pockets. We have seen the introduction of wireless technologies, augmented and virtual reality, smart home devices, autonomous vehicles, and much more. This session focuses on a new technological advance that has the potential to support the health, wellbeing, and independence of older adults and caregivers: artificial intelligence (AI). This session will present applications of AI, Machine Learning (ML), and other novel analytic methods and how they have the potential to impact the lives of older adults in a variety of context. As AI is increasingly being involved in workplace hiring, the first talk focuses on older adults’ attitudes toward the role of AI in this decision making process. Next, novel ML approaches applied to social media are discussed in terms of understanding the needs of Alzheimer’s caregivers. Next, ML techniques are discussed in terms of developing biomarkers that can be applied in diagnosis and assessment of therapeutic responses by detecting mood, which may have important implications for older adults living with dementia. Then, the potential role of AI is discussed in terms of developing reminder systems to promote older adults’ adherence to technology-based health activities. Finally, novel analytic approaches are discussed in terms of harnessing digital metrics to detect the risk of cognitive decline.


Author(s):  
Matthew N. O. Sadiku ◽  
Chandra M. M Kotteti ◽  
Sarhan M. Musa

Machine learning is an emerging field of artificial intelligence which can be applied to the agriculture sector. It refers to the automated detection of meaningful patterns in a given data.  Modern agriculture seeks ways to conserve water, use nutrients and energy more efficiently, and adapt to climate change.  Machine learning in agriculture allows for more accurate disease diagnosis and crop disease prediction. This paper briefly introduces what machine learning can do in the agriculture sector.


Author(s):  
M. A. Fesenko ◽  
G. V. Golovaneva ◽  
A. V. Miskevich

The new model «Prognosis of men’ reproductive function disorders» was developed. The machine learning algorithms (artificial intelligence) was used for this purpose, the model has high prognosis accuracy. The aim of the model applying is prioritize diagnostic and preventive measures to minimize reproductive system diseases complications and preserve workers’ health and efficiency.


2018 ◽  
Vol 15 (1) ◽  
pp. 6-28 ◽  
Author(s):  
Javier Pérez-Sianes ◽  
Horacio Pérez-Sánchez ◽  
Fernando Díaz

Background: Automated compound testing is currently the de facto standard method for drug screening, but it has not brought the great increase in the number of new drugs that was expected. Computer- aided compounds search, known as Virtual Screening, has shown the benefits to this field as a complement or even alternative to the robotic drug discovery. There are different methods and approaches to address this problem and most of them are often included in one of the main screening strategies. Machine learning, however, has established itself as a virtual screening methodology in its own right and it may grow in popularity with the new trends on artificial intelligence. Objective: This paper will attempt to provide a comprehensive and structured review that collects the most important proposals made so far in this area of research. Particular attention is given to some recent developments carried out in the machine learning field: the deep learning approach, which is pointed out as a future key player in the virtual screening landscape.


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