Comprehensive application of artificial intelligence in preserved ratio impaired spirometry: A systematic literature review
- 1Department of Medical Imaging Center, The Fourth Clinical Medical College of Xinjiang Medical University, Urumqi, Xinjiang, China
Abstract
Background: Artificial intelligence (AI) has expanded into respiratory disease diagnosis, subtyping, and prognosis, enabling early detection and precision care. However, AI applications in Preserved Ratio Impaired Spirometry (PRISm) remain nascent. This study analyzes this gap to guide future research. Methods: A systematic review was conducted to analyze the application of AI in PRISm, searching across PubMed, Cochrane Library, Web of Science, Ovid Medline, Scopus and Embase. Results: A total of eleven studies were included, all of which focused on diagnostic and classification tasks. Among these, three utilized radiomics models, four employed machine learning algorithms, two integrated machine learning with radiomics, and two applied deep learning approaches. Nine studies were published within the past two years, with results demonstrating the high performance and developmental potential of AI technologies in this domain. AI research on PRISm spans multiple disciplines, including exhaled metabolomics, environmental exposure assessment, radiomics, and deep learning. Conclusion: Existing studies have preliminarily validated the technical feasibility of artificial intelligence for the early identification of PRISm from multiple perspectives, including imaging, metabolism, and environmental exposure. Modeling strategies that integrate multi-source data have demonstrated superior discriminatory performance compared to single-modality approaches. In the future, with the integration and sharing of multicenter data under privacy-compliant conditions, coupled with the continuous evolution of algorithm architectures toward enhanced generalizability, AI applications for PRISm are expected to transition from static identification to dynamic early warning, thereby providing more robust technical support for precise risk stratification of this condition.
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BibTeX
@article{wu2026comprehensive,
title = {{Comprehensive application of artificial intelligence in preserved ratio impaired spirometry: A systematic literature review}},
author = {Qian Wu and Hui Guo and Ruihan Li and Jinhuan Han and Zhen Zhang and Ayajiang Jingesi},
journal = {PLOS One},
year = {2026},
month = sep,
doi = {10.1371/journal.pone.0353021},
url = {https://doi.org/10.1371/journal.pone.0353021},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Comprehensive application of artificial intelligence in preserved ratio impaired spirometry: A systematic literature review
AU - Qian Wu
AU - Hui Guo
AU - Ruihan Li
AU - Jinhuan Han
AU - Zhen Zhang
AU - Ayajiang Jingesi
PY - 2026
DA - 2026/09/16
JO - PLOS One
DO - 10.1371/journal.pone.0353021
UR - https://doi.org/10.1371/journal.pone.0353021
AB - Background: Artificial intelligence (AI) has expanded into respiratory disease diagnosis, subtyping, and prognosis, enabling early detection and precision care. However, AI applications in Preserved Ratio Impaired Spirometry (PRISm) remain nascent. This study analyzes this gap to guide future research. Methods: A systematic review was conducted to analyze the application of AI in PRISm, searching across PubMed, Cochrane Library, Web of Science, Ovid Medline, Scopus and Embase. Results: A total of eleven studies were included, all of which focused on diagnostic and classification tasks. Among these, three utilized radiomics models, four employed machine learning algorithms, two integrated machine learning with radiomics, and two applied deep learning approaches. Nine studies were published within the past two years, with results demonstrating the high performance and developmental potential of AI technologies in this domain. AI research on PRISm spans multiple disciplines, including exhaled metabolomics, environmental exposure assessment, radiomics, and deep learning. Conclusion: Existing studies have preliminarily validated the technical feasibility of artificial intelligence for the early identification of PRISm from multiple perspectives, including imaging, metabolism, and environmental exposure. Modeling strategies that integrate multi-source data have demonstrated superior discriminatory performance compared to single-modality approaches. In the future, with the integration and sharing of multicenter data under privacy-compliant conditions, coupled with the continuous evolution of algorithm architectures toward enhanced generalizability, AI applications for PRISm are expected to transition from static identification to dynamic early warning, thereby providing more robust technical support for precise risk stratification of this condition.
SN - 1932-6203
ER -