ARTIFICIAL INTELLIGENCE IN THE STUDY OF PHYSIOLOGICAL PARAMETERS OF THE HUMAN RESPIRATORY SYSTEM

Authors

  • Avezova Salomat Makhmudjonovna Tashkent State Medical University

DOI:

https://doi.org/10.55640/

Keywords:

artificial intelligence, respiratory system, vital capacity, respiratory rate, minute volume, machine learning, physiological parameters.

Abstract

Modern artificial intelligence technologies offer new opportunities for analyzing physiological parameters of the human respiratory system. Machine learning algorithms enable processing large data sets, identifying patterns, and predicting lung function based on standard respiratory parameters. This study analyzed data from 60 volunteers across three age groups using an AI system to assess vital capacity, respiratory rate, and minute volume. Results showed that the AI system can accurately classify age-related respiratory function characteristics and identify functional decline in elderly participants. The use of AI can improve diagnostic accuracy, reduce analysis time, and improve the risk prediction of respiratory disorders.

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References

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Published

2026-03-13

Issue

Vol. 5 No. 03 (2026): Journal of Multidisciplinary Sciences and Innovations

Section

Articles

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors retain the copyright of their manuscripts, and all Open Access articles are disseminated under the terms of the Creative Commons Attribution License 4.0 (CC-BY), which licenses unrestricted use, distribution, and reproduction in any medium, provided that the original work is appropriately cited. The use of general descriptive names, trade names, trademarks, and so forth in this publication, even if not specifically identified, does not imply that these names are not protected by the relevant laws and regulations.

How to Cite

ARTIFICIAL INTELLIGENCE IN THE STUDY OF PHYSIOLOGICAL PARAMETERS OF THE HUMAN RESPIRATORY SYSTEM. (2026). Journal of Multidisciplinary Sciences and Innovations, 5(03), 637-640. https://doi.org/10.55640/

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