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Jiang M, Hu CJ, Rowe CL, Kang H, Gong X, Dagucon CP, Wang J, Lin Y, Sood A, Guo Y, Zhu Y, Alexis NE, Gilliland FD, Belinsky SA, Yu X, Leng S. Application of artificial intelligence in quantifying lung deposition dose of black carbon in people with exposure to ambient combustion particles. J Expo Sci Environ Epidemiol 2023:10.1038/s41370-023-00607-0. [PMID: 37848612 PMCID: PMC11021374 DOI: 10.1038/s41370-023-00607-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 09/19/2023] [Accepted: 10/04/2023] [Indexed: 10/19/2023]
Abstract
BACKGROUND Understanding lung deposition dose of black carbon is critical to fully reconcile epidemiological evidence of combustion particles induced health effects and inform the development of air quality metrics concerning black carbon. Macrophage carbon load (MaCL) is a novel cytology method that quantifies lung deposition dose of black carbon, however it has limited feasibility in large-scale epidemiological study due to the labor-intensive manual counting. OBJECTIVE To assess the association between MaCL and episodic elevation of combustion particles; to develop artificial intelligence based counting algorithm for MaCL assay. METHODS Sputum slides were collected during episodic elevation of ambient PM2.5 (n = 49, daily PM2.5 > 10 µg/m3 for over 2 weeks due to wildfire smoke intrusion in summer and local wood burning in winter) and low PM2.5 period (n = 39, 30-day average PM2.5 < 4 µg/m3) from the Lovelace Smokers cohort. RESULTS Over 98% individual carbon particles in macrophages had diameter <1 µm. MaCL levels scored manually were highly responsive to episodic elevation of ambient PM2.5 and also correlated with lung injury biomarker, plasma CC16. The association with CC16 became more robust when the assessment focused on macrophages with higher carbon load. A Machine-Learning algorithm for Engulfed cArbon Particles (MacLEAP) was developed based on the Mask Region-based Convolutional Neural Network. MacLEAP algorithm yielded excellent correlations with manual counting for number and area of the particles. The algorithm produced associations with ambient PM2.5 and plasma CC16 that were nearly identical in magnitude to those obtained through manual counting. IMPACT STATEMENT Understanding lung black carbon deposition is crucial for comprehending health effects of combustion particles. We developed "Machine-Learning algorithm for Engulfed cArbon Particles (MacLEAP)", the first artificial intelligence algorithm for quantifying airway macrophage black carbon. Our study bolstered the algorithm with more training images and its first use in air pollution epidemiology. We revealed macrophage carbon load as a sensitive biomarker for heightened ambient combustion particles due to wildfires and residential wood burning.
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Affiliation(s)
- Menghui Jiang
- School of Medicine, University of New Mexico, Albuquerque, NM, USA
| | - Chelin Jamie Hu
- College of Nursing, University of New Mexico College of Nursing, Albuquerque, NM, USA
| | - Cassie L Rowe
- School of Medicine, University of New Mexico, Albuquerque, NM, USA
| | - Huining Kang
- School of Medicine, University of New Mexico, Albuquerque, NM, USA
- University of New Mexico Comprehensive Cancer Center, Albuquerque, NM, USA
| | - Xi Gong
- Department of Geography & Environmental Studies, University of New Mexico, Albuquerque, NM, USA
| | | | - Jialiang Wang
- School of Medicine, University of New Mexico, Albuquerque, NM, USA
| | - Yan Lin
- Department of Geography & Environmental Studies, University of New Mexico, Albuquerque, NM, USA
| | - Akshay Sood
- School of Medicine, University of New Mexico, Albuquerque, NM, USA
- Miners Colfax Medical Center, Raton, NM, USA
| | - Yan Guo
- School of Medicine, University of New Mexico, Albuquerque, NM, USA
- University of New Mexico Comprehensive Cancer Center, Albuquerque, NM, USA
| | - Yiliang Zhu
- School of Medicine, University of New Mexico, Albuquerque, NM, USA
| | - Neil E Alexis
- Center for Environmental Medicine Asthma and Lung Biology, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Frank D Gilliland
- Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Steven A Belinsky
- University of New Mexico Comprehensive Cancer Center, Albuquerque, NM, USA
- Lung Cancer Program, Lovelace Biomedical Research Institute, Albuquerque, NM, USA
| | - Xiaozhong Yu
- College of Nursing, University of New Mexico College of Nursing, Albuquerque, NM, USA.
| | - Shuguang Leng
- School of Medicine, University of New Mexico, Albuquerque, NM, USA.
- University of New Mexico Comprehensive Cancer Center, Albuquerque, NM, USA.
- Lung Cancer Program, Lovelace Biomedical Research Institute, Albuquerque, NM, USA.
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