Rapid fiber-detection technique by artificial intelligence in phase-contrast microscope images of simulated atmospheric samples.
Since the manufacture, import, and use of asbestos products have been completely abolished in Japan, the main cause of asbestos emissions into the atmosphere is the demolition and removal of buildings built with asbestos-containing materials. To detect and correct asbestos emissions from inappropria...
| Publicado en: | Annals of Work Exposures & Health Vol. 68; no. 4; pp. 420 - 427 |
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| Autores principales: | , , , , , , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Oxford University Press / USA
May2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=177085076&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 177085076 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23987308 KJ1E jtl: Annals of Work Exposures & Health issn: 23987308 maglogo: N pubinfo: dt: May2024 vid: 68 iid: 4 pid: 622 pub: Oxford University Press / USA artinfo: ui: 177085076 177085076 177085076 10.1093/annweh/wxae014 177085076 ppf: 420 ppct: 7 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Rapid fiber-detection technique by artificial intelligence in phase-contrast microscope images of simulated atmospheric samples. aug: au: Yamamoto, Takashi Iwasaki, Kazuharu Iida, Yukiko Yuki, Ken-ichi Nakaji, Fumihiro Yamashiro, Hayato Toyoguchi, Toshiyuki Terazono, Atsushi affil: National Institute for Environmental Studies , 16-2 Onogawa, Tsukuba, Ibaraki 305-8506 , Japan sug: subj: Asbestos Analysis Artificial Intelligence Microscopy Simulations Image Processing, Computer Assisted Environmental Monitoring Human Environmental Exposure Air Pollutants Neural Networks (Computer) Precision Descriptive Statistics Construction Materials ab: Since the manufacture, import, and use of asbestos products have been completely abolished in Japan, the main cause of asbestos emissions into the atmosphere is the demolition and removal of buildings built with asbestos-containing materials. To detect and correct asbestos emissions from inappropriate demolition and removal operations at an early stage, a rapid method to measure atmospheric asbestos fibers is required. The current rapid measurement method is a combination of short-term atmospheric sampling and phase-contrast microscopy counting. However, visual counting takes a considerable amount of time and is not sufficiently fast. Using artificial intelligence (AI) to analyze microscope images to detect fibers may greatly reduce the time required for counting. Therefore, in this study, we investigated the use of AI image analysis for detecting fibers in phase-contrast microscope images. A series of simulated atmospheric samples prepared from standard samples of amosite and chrysotile were observed using a phase-contrast microscope. Images were captured, and training datasets were created from the counting results of expert analysts. We adopted 2 types of AI models—an instance segmentation model, namely the mask region-based convolutional neural network (Mask R-CNN), and a semantic segmentation model, namely the multi-level aggregation network (MA-Net)—that were trained to detect asbestos fibers. The accuracy of fiber detection achieved with the Mask R-CNN model was 57% for recall and 46% for precision, whereas the accuracy achieved with the MA-Net model was 95% for recall and 91% for precision. Therefore, satisfactory results were obtained with the MA-Net model. The time required for fiber detection was less than 1 s per image in both AI models, which was faster than the time required for counting by an expert analyst. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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