Literature DB >> 32381498

Artificial intelligence techniques in asthma: a systematic review and critical appraisal of the existing literature.

Konstantinos P Exarchos1, Maria Beltsiou2, Chainti-Antonella Votti2, Konstantinos Kostikas2.   

Abstract

Artificial intelligence (AI) when coupled with large amounts of well characterised data can yield models that are expected to facilitate clinical practice and contribute to the delivery of better care, especially in chronic diseases such as asthma.The purpose of this paper is to review the utilisation of AI techniques in all aspects of asthma research, i.e. from asthma screening and diagnosis, to patient classification and the overall asthma management and treatment, in order to identify trends, draw conclusions and discover potential gaps in the literature.We conducted a systematic review of the literature using PubMed and DBLP from 1988 up to 2019, yielding 425 articles; after removing duplicate and irrelevant articles, 98 were further selected for detailed review.The resulting articles were organised in four categories, and subsequently compared based on a set of qualitative and quantitative factors. Overall, we observed an increasing adoption of AI techniques for asthma research, especially within the last decade.AI is a scientific field that is in the spotlight, especially the last decade. In asthma there are already numerous studies; however, there are certain unmet needs that need to be further elucidated.
Copyright ©ERS 2020.

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Year:  2020        PMID: 32381498     DOI: 10.1183/13993003.00521-2020

Source DB:  PubMed          Journal:  Eur Respir J        ISSN: 0903-1936            Impact factor:   16.671


  6 in total

1.  Cough Sound Detection and Diagnosis Using Artificial Intelligence Techniques: Challenges and Opportunities.

Authors:  Kawther S Alqudaihi; Nida Aslam; Irfan Ullah Khan; Abdullah M Almuhaideb; Shikah J Alsunaidi; Nehad M Abdel Rahman Ibrahim; Fahd A Alhaidari; Fatema S Shaikh; Yasmine M Alsenbel; Dima M Alalharith; Hajar M Alharthi; Wejdan M Alghamdi; Mohammed S Alshahrani
Journal:  IEEE Access       Date:  2021-07-15       Impact factor: 3.367

Review 2.  Where Is the Artificial Intelligence Applied in Dentistry? Systematic Review and Literature Analysis.

Authors:  Andrej Thurzo; Wanda Urbanová; Bohuslav Novák; Ladislav Czako; Tomáš Siebert; Peter Stano; Simona Mareková; Georgia Fountoulaki; Helena Kosnáčová; Ivan Varga
Journal:  Healthcare (Basel)       Date:  2022-07-08

3.  Predicting asthma attacks using connected mobile devices and machine learning: the AAMOS-00 observational study protocol.

Authors:  Kevin Cheuk Him Tsang; Hilary Pinnock; Andrew M Wilson; Dario Salvi; Syed Ahmar Shah
Journal:  BMJ Open       Date:  2022-10-03       Impact factor: 3.006

Review 4.  Translational precision medicine: an industry perspective.

Authors:  Dominik Hartl; Valeria de Luca; Anna Kostikova; Jason Laramie; Scott Kennedy; Enrico Ferrero; Richard Siegel; Martin Fink; Sohail Ahmed; John Millholland; Alexander Schuhmacher; Markus Hinder; Luca Piali; Adrian Roth
Journal:  J Transl Med       Date:  2021-06-05       Impact factor: 5.531

5.  The Application and Development of Deep Learning in Radiotherapy: A Systematic Review.

Authors:  Danju Huang; Han Bai; Li Wang; Yu Hou; Lan Li; Yaoxiong Xia; Zhirui Yan; Wenrui Chen; Li Chang; Wenhui Li
Journal:  Technol Cancer Res Treat       Date:  2021 Jan-Dec

Review 6.  Artificial Intelligence and Machine Learning in Chronic Airway Diseases: Focus on Asthma and Chronic Obstructive Pulmonary Disease.

Authors:  Yinhe Feng; Yubin Wang; Chunfang Zeng; Hui Mao
Journal:  Int J Med Sci       Date:  2021-06-01       Impact factor: 3.738

  6 in total

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