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.