Automated counting of airborne asbestos fibers by a high-throughput microscopy (HTM) method

Our plain-English summary of Automated counting of airborne asbestos fibers by a high-throughput microscopy (HTM) method, by M.O. Cho, S. Yoon, H. Han, et al (Sensors, 2011).

The study

• Authors: M.O. Cho, S. Yoon, H. Han, J.K. Kim
• Journal: Sensors
• Published: 2011;11(7):7231-42
• Cited by: 9 articles, as counted by NIH iCite
• Read it: PubMed · Publisher · Free full text

Abstract

Inhalation of airborne asbestos causes serious health problems such as lung cancer and malignant mesothelioma. The phase-contrast microscopy (PCM) method has been widely used for estimating airborne asbestos concentrations because it does not require complicated processes or high-priced equipment. However, the PCM method is time-consuming and laborious as it is manually performed off-site by an expert. We have developed a high-throughput microscopy (HTM) method that can detect fibers distinguishable from other spherical particles in a sample slide by image processing both automatically and quantitatively. A set of parameters for processing and analysis of asbestos fiber images was adjusted for standard asbestos samples with known concentrations. We analyzed sample slides containing airborne asbestos fibers collected at 11 different workplaces following PCM and HTM methods, and found a reasonably good agreement in the asbestos concentration. Image acquisition synchronized with the movement of the robotic sample stages followed by an automated batch processing of a stack of sample images enabled us to count asbestos fibers with greatly reduced time and labors. HTM should be a potential alternative to conventional PCM, moving a step closer to realization of on-site monitoring of asbestos fibers in air.

In plain English

This laboratory study tested an automated method for counting airborne asbestos fibers on microscope slides, high-throughput microscopy (HTM), as an alternative to manual phase-contrast microscopy (PCM). It did not study health effects. A camera synchronized with robotic stages that moved the slide captured 300 images per slide, covering about 95.5 mm2, a 120-fold larger area than PCM, according to the authors. Software then counted fiber-shaped particles. Settings were tuned using manual counts of standard chrysotile, amosite and crocidolite samples. Of five proficiency-testing samples, only 177-2 (HTM 587 fibers/mm2) exceeded its upper 95% confidence limit of 535. For 11 on-site airborne samples, nine HTM results differed from PCM by 0.909% to 37.9% of the PCM value. Two (E-1 and E-2) differed by 155% and 90%, which the authors attributed to dark, unclean backgrounds. Capturing images took about 5 minutes, plus a few minutes of analysis, versus about 90 minutes for typical PCM analysis. The authors noted HTM cannot tell asbestos from other fibers and did not fully handle crossed or bundled fibers. They concluded HTM could act as an alternative to PCM.

We wrote this plain-English summary from the full article with the help of AI, and a second AI model checked each sentence against the article. It is not the authors' wording; the abstract and the article are their own account.

What the study did and found

• Design: Laboratory methods-development study: an automated microscope and image-analysis system (HTM) was built, and its fiber counts were compared with manual counts and with PCM results
• Who was studied: No people were studied. The samples were ball-milled standard asbestos samples, five PAT (Proficiency Analytical Testing) standard samples, and 11 airborne asbestos samples collected on-site (labeled L, K, C-1, C-2, J-1, J-2, S, E-1, E-2, B-1 and B-2). The PAT and on-site slides, with their PCM results, came from the Korean Industrial Health Association. Collection dates were not given.
• Exposure: Standard samples of chrysotile, amosite and crocidolite from the HSL037 asbestos reference set (Health and Safety Laboratory), ball-milled and serially diluted. Also airborne fiber samples from workplaces, with sampled air volumes from 140 to 720 L.
• Main results:
  • HTM captured 300 images per slide covering about 95.5 mm2, which the authors described as a 120-fold larger area than conventional PCM.
  • HTM took about 5 minutes to capture 300 images, and analysis of the images took a few minutes.
  • A typical PCM analysis took about 90 minutes to examine 100 fields of view.
  • For the ball-milled standard samples, the authors reported a high correlation between manual counts and HTM counts with the best parameter set. No correlation coefficient is given in the text.
  • For the five PAT standard samples, only one HTM fiber density fell outside the 95% confidence limits: sample 177-2, with HTM 587 fibers/mm2 against an upper limit of 535 (PCM 316). The authors described this as exceeding the upper limit by 10%.
  • For the 11 on-site airborne samples, the authors reported that HTM concentrations were fairly close to PCM. For nine samples, the difference between HTM and PCM ranged from 0.909% (L: PCM 0.011, HTM 0.0109 fibers/cc) to 37.9% (S: PCM 0.029, HTM 0.018 fibers/cc) of the PCM value.
  • Two on-site samples differed much more: E-1 (PCM 0.020, HTM 0.051 fibers/cc, 155% difference) and E-2 (PCM 0.020, HTM 0.038 fibers/cc, 90% difference). The authors attributed this mainly to darker, unclean image backgrounds.
  • With different image-processing settings, the HTM result for sample E-1 was 0.013 fibers/cc, which the authors described as comparable to the PCM result.
• The authors' conclusion: The authors concluded that they had developed an automatic counting method for airborne asbestos fibers that acts as an alternative to conventional PCM, and that it reduced the time, labor and variability that come with PCM.
• Limitations the authors state:
  • HTM detects fiber-shaped particles but cannot distinguish asbestos from other fibers.
  • The NIOSH 7400 counting rules for crossed fibers and fiber bundles were not fully applied.
  • More PAT standard samples need to be analyzed to assure the quality of HTM analysis.
  • The image-processing settings do not yet adjust automatically to background noise, which affected results for noisy samples.

Topics

Medical terms in this study, explained in our Lexicon: Lung Cancer, Mesothelioma.

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