Development of rapid and highly accurate method to measure concentration of fibers in atmosphere using artificial intelligence and scanning electron microscopy.

Aim: We aimed to develop a measurement method that can count fibers rapidly by scanning electron microscopy equipped with an artificial intelligence image recognition system (AI-SEM), detecting thin fibers which cannot be observed by a conventional phase contrast microscopy (PCM) method.

Methods: We created a simulation sampling filter of airborne fibers using water-filtered chrysotile (white asbestos). A total of 108 images was taken of the samples at a 5 kV accelerating voltage with 10 000X magnification scanning electron microscopy (SEM). Each of three expert analysts counted 108 images and created a model answer for fibers. We trained the artificial intelligence (AI) using 25 of the 108 images. After the training, the AI counted fibers in 108 images again.

Results: There was a 12.1% difference between the AI counting results and the model answer. At 10 000X magnification, AI-SEM can detect 87.9% of fibers with a diameter of 0.06-3 μm, which is similar to a skilled analyst. Fibers with a diameter of 0.2 μm or less cannot be confirmed by phase-contrast microscopy (PCM). When observing the same area in 300 images with 1500X magnification SEM-as listed in the Asbestos Monitoring Manual (Ministry of the Environment)-with 10 000X SEM, the expected analysis time required for the trained AI is 5 h, whereas the expected time required for observation by an analyst is 251 h.

Conclusion: The AI-SEM can count thin fibers with higher accuracy and more quickly than conventional methods by PCM and SEM.