Artificial intelligence in diagnosis of obstructive lung disease: current status and future potential

Artificial intelligence in diagnosis of obstructive lung disease: current status and future... Purpose of reviewThe application of artificial intelligence in the diagnosis of obstructive lung diseases is an exciting phenomenon. Artificial intelligence algorithms work by finding patterns in data obtained from diagnostic tests, which can be used to predict clinical outcomes or to detect obstructive phenotypes. The purpose of this review is to describe the latest trends and to discuss the future potential of artificial intelligence in the diagnosis of obstructive lung diseases.Recent findingsMachine learning has been successfully used in automated interpretation of pulmonary function tests for differential diagnosis of obstructive lung diseases. Deep learning models such as convolutional neural network are state-of-the art for obstructive pattern recognition in computed tomography. Machine learning has also been applied in other diagnostic approaches such as forced oscillation test, breath analysis, lung sound analysis and telemedicine with promising results in small-scale studies.SummaryOverall, the application of artificial intelligence has produced encouraging results in the diagnosis of obstructive lung diseases. However, large-scale studies are still required to validate current findings and to boost its adoption by the medical community. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Current Opinion in Pulmonary Medicine Wolters Kluwer Health

Artificial intelligence in diagnosis of obstructive lung disease: current status and future potential

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Publisher
Wolters Kluwer
Copyright
Copyright © 2018 Wolters Kluwer Health, Inc. All rights reserved.
ISSN
1070-5287
eISSN
1531-6971
D.O.I.
10.1097/MCP.0000000000000459
Publisher site
See Article on Publisher Site

Abstract

Purpose of reviewThe application of artificial intelligence in the diagnosis of obstructive lung diseases is an exciting phenomenon. Artificial intelligence algorithms work by finding patterns in data obtained from diagnostic tests, which can be used to predict clinical outcomes or to detect obstructive phenotypes. The purpose of this review is to describe the latest trends and to discuss the future potential of artificial intelligence in the diagnosis of obstructive lung diseases.Recent findingsMachine learning has been successfully used in automated interpretation of pulmonary function tests for differential diagnosis of obstructive lung diseases. Deep learning models such as convolutional neural network are state-of-the art for obstructive pattern recognition in computed tomography. Machine learning has also been applied in other diagnostic approaches such as forced oscillation test, breath analysis, lung sound analysis and telemedicine with promising results in small-scale studies.SummaryOverall, the application of artificial intelligence has produced encouraging results in the diagnosis of obstructive lung diseases. However, large-scale studies are still required to validate current findings and to boost its adoption by the medical community.

Journal

Current Opinion in Pulmonary MedicineWolters Kluwer Health

Published: Mar 1, 2018

References

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