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 PM_2.5 (n = 49, daily PM_2.5 > 10 µg/m³ for over 2 weeks due to wildfire smoke intrusion in summer and local wood burning in winter) and low PM_2.5 period (n = 39, 30-day average PM_2.5 < 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 PM_2.5 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 PM_2.5 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.

中文翻译：

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人工智能在量化暴露于环境燃烧颗粒的人的黑碳肺部沉积剂量中的应用

背景

了解黑碳的肺部沉积剂量对于充分协调燃烧颗粒引起的健康影响的流行病学证据并为制定有关黑碳的空气质量指标至关重要。巨噬细胞碳负荷（MaCL）是一种定量黑碳肺沉积剂量的新型细胞学方法，但由于人工计数劳动密集型，其在大规模流行病学研究中的可行性有限。

客观的

评估 MaCL 与燃烧颗粒的间歇性升高之间的关联；开发基于人工智能的 MaCL 检测计数算法。

方法

痰标本收集于环境 PM _2.5间歇性升高期间（n  = 49，由于夏季野火烟雾入侵和冬季当地木材燃烧，每日 PM _2.5  > 10 µg/m ^{3持续两周以上）和 PM} _2.5较低时期（n = 39，来自 Lovelace 吸烟者队列的 30 天平均 PM _2.5  < 4 µg/m ^{3 )。}

结果

巨噬细胞中超过 98% 的单个碳颗粒的直径 <1 µm。_{手动评分的 MaCL 水平对环境 PM 2.5}的间歇性升高高度敏感，并且还与肺损伤生物标志物血浆 CC16 相关。当评估重点关注碳负荷较高的巨噬细胞时，与 CC16 的关联变得更加牢固。基于掩模区域的卷积神经网络开发了一种用于吞噬碳粒子文章的机器学习算法（MacLEAP ）。MacLEAP 算法与手动计数颗粒的数量和面积产生了极好的相关性。该算法产生了与环境 PM _2.5和血浆 CC16 的关联，其幅度与通过手动计数获得的关联几乎相同。

影响报告

了解肺部黑碳沉积对于理解燃烧颗粒对健康的影响至关重要。我们开发了“吞没碳颗粒的机器学习算法（MacLEAP）”，这是第一个用于量化气道巨噬细胞黑碳的人工智能算法。我们的研究通过更多的训练图像支持了该算法，并首次在空气污染流行病学中使用。我们发现巨噬细胞碳负荷是野火和住宅木材燃烧导致的环境燃烧颗粒增加的敏感生物标志物。