AI Counts Airborne Asbestos Fibers 50 Times Faster Than Expert Analysts

Decades after asbestos was banned in Japan, the mineral still haunts the country’s aging buildings. Roughly 2.8 million private structures built with asbestos-containing materials were still standing in 2009, and as demolition of these buildings accelerates toward an expected peak of about 100,000 per year by 2028, the risk of accidental fiber release grows with every wrecking operation. Now, a team of Japanese researchers has developed an artificial intelligence system that can spot and count airborne fibers in microscope images of real atmospheric samples in seconds, a task that currently ties up skilled analysts for up to 100 minutes per sample.

The study, published in Frontiers in Analytical Science, addresses a persistent bottleneck in asbestos monitoring. When demolition or removal work on asbestos-containing building materials is performed improperly, fibers can leak into the surrounding air. In one survey at a demolition site, asbestos fiber concentrations at the entrance and exit of a security area reached 54 fibers per liter. Detecting such leaks early requires a rapid measurement method, but the current standard combines a short air collection time of 30 minutes or less with visual fiber counting under a phase contrast microscope, and the counting step alone takes 25 to 100 minutes depending on fiber density and analyst skill.

Visual counting is not only slow but also vulnerable to human error. According to the UK Health and Safety Executive’s analyst guide, fiber counting is subject to fatigue and proficiency-related mistakes. Analysts also tend to underestimate fiber numbers in samples with high dust density and overestimate them in cleaner samples, adding a layer of uncertainty that no amount of training fully eliminates. With the number of leak monitoring operations expected to rise alongside demolition activity, and with a limited pool of expert analysts whose training takes years, the demand for automation has become urgent.

The research team, led by Yukiko Iida of Environmental Control Center Co., Ltd. and Takashi Yamamoto of Japan’s National Institute for Environmental Studies, built their system on real-world data rather than laboratory simulations. Five laboratories belonging to the Japan Environmental Measurement and Chemical Analysis Association provided 98 phase contrast microscope images of ambient air or air collected near demolition sites. The samples were collected on mixed cellulose ester filters with 0.8 micrometer pores, made transparent with acetone vapor, and observed at 400x magnification. Expert analysts who had passed the most advanced rank of Japan’s asbestos fiber counting proficiency test manually counted the fibers in each image, following the criteria of the Asbestos Monitoring Manual: fiber length greater than 5 micrometers, width less than 3 micrometers, and an aspect ratio greater than 3.

Those expert counting results were used to annotate the images, creating 98 training datasets. The team then trained Segformer, a semantic segmentation model that uses a hierarchical Transformer encoder rather than the convolutional neural networks common in earlier segmentation models. Because Transformers can capture interactions between distant pixels, they are considered more accurate than purely convolutional approaches. In benchmark comparisons on the ADE20K dataset, Segformer achieved a mean Intersection over Union of 51.8 percent, well above the 40 to 44 percent achieved by traditional models such as FCN and DeeplabV3+. Of the 98 datasets, 77 were used to train the model and 21 were reserved for evaluation. Training took roughly 30 minutes on an NVIDIA GeForce RTX 3090 graphics processing unit, using the Dice Loss function and the Adam optimization algorithm.

The system operates in two stages. First, the AI model detects fibrous structures in the image. Second, an image processing component verifies whether each detected fiber meets the official counting criteria for length, width, and aspect ratio, judging fibers indistinguishable when overlapping shapes make accurate measurement impossible. The team evaluated performance on two levels: detection accuracy, which treats overlapping fiber clusters as single entities, and counting accuracy, which attempts to identify each individual fiber even when fibers overlap.

The results were strong on detection. The system achieved a recall of 0.90, meaning it found 90 percent of the fibers and clusters counted by expert analysts, with a precision of 0.68 and an F1 score of 0.77. Only 10 of the 97 fibers and clusters counted by experts were missed entirely, and 4 of those 10 oversights were actually detected by the AI but judged indistinguishable during the image processing stage because multiple fibers overlapped. When evaluated by counting accuracy, which is the harder task, the system achieved a recall of 0.78, a precision of 0.67, and an F1 score of 0.72, comparable to the manual counting performance of a skilled analyst.

The speed advantage was dramatic. Fiber detection took about 1.2 seconds per image on the GPU, allowing the system to scan 100 fields of view in roughly 2 minutes. The official Japanese method requires 25 to 100 minutes for the same task, making the automated system 12 to 50 times faster. This time saving does not yet include the manual work of adjusting focus and capturing images, which the team identifies as a target for future automation through synthesized depth of focus and continuous image capture as the field of view moves.

The study also revealed instructive failure modes. In some images, the system could not separate overlapping fiber clusters that an expert counted as two fibers, a limitation the team says cannot be resolved with current semantic segmentation models and requires analyst intervention. In others, the system misidentified impurities as fibers, sometimes caused by twisted filters or air bubbles introduced during slide preparation. One particularly bright fiber that an expert counted was missed by the AI, likely because the training images used positive contrast, with dark fibers against a bright background, making luminous fibers hard for the model to recognize. In one image, the system actually detected a fiber that the expert analyst overlooked because weak contrast made its length appear shorter than the 5 micrometer counting threshold, suggesting the AI could enable stricter judgment of sizes close to the criteria.

The accuracy was somewhat lower than the team’s previous work with simulated air samples, where a related model achieved a recall of 0.95 and precision of 0.91, a difference the researchers attribute to the complexity of real atmospheric samples containing particles other than asbestos fibers. Nevertheless, the team concludes that the system demonstrates reasonable accuracy on actual air samples and could prove useful for early detection and correction of asbestos fiber release during demolition work. They plan to expand the training dataset through ongoing collaboration with analytical companies and to update the fiber detection component as more advanced AI models become available, bringing fully automated asbestos monitoring closer to practical deployment at demolition and renovation sites.

Subject of Research: AI-based rapid asbestos fiber detection in phase-contrast microscope images

Article Title: Development of a rapid fiber-detection system using artificial intelligence in phase-contrast microscope images of actual atmospheric samples

Article References: Iida, Y., Yamamoto, T., Iwasaki, K., Yuki, K.-I., Kiri, K., Yamashiro, H., Toyoguchi, T., & Terazono, A. (2025). Development of a rapid fiber-detection system using artificial intelligence in phase-contrast microscope images of actual atmospheric samples. Frontiers in Analytical Science, 5, Article 1571840. https://doi.org/10.3389/frans.2025.1571840

Image Credits: AI Generated

DOI: 10.3389/frans.2025.1571840

Keywords: asbestos, artificial intelligence, SegFormer, phase-contrast microscopy, fiber detection, semantic segmentation, air quality monitoring, demolition sites, deep learning, environmental analysis, Japan, occupational health