ArticleJournal of exposure science & environmental epidemiology2024
Application of artificial intelligence in quantifying lung deposition dose of black carbon in people with exposure to ambient combustion particles.
Article in Journal of exposure science & environmental epidemiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed, 7 citations in OpenAlex.
- Associations between wildfire smoke exposure and health-related quality of life: findings from the Lovelace Smokers Cohort.Respiratory research · 2026Article
- Associations between Wildfire Smoke Exposure and Health-Related Quality of Life: Findings from the Lovelace Smokers Cohort.Research square · 2025Article
- Wildfire and asthma - The prospective interventions.The World Allergy Organization journal · 2025Review
Corrections and comments
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Authors and funding
16 authors at 4 institutions in 1 country.
Funding
Abstract
backgroundUnderstanding 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.
objectiveTo assess the association between MaCL and episodic elevation of combustion particles; to develop artificial intelligence based counting algorithm for MaCL assay.
methodsSputum slides were collected during episodic elevation of ambient PM
resultsOver 98% individual carbon particles in macrophages had diameter <1 µm. MaCL levels scored manually were highly responsive to episodic elevation of ambient PM 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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.