Sensors

Our medical research library holds 9 studies published in Sensors (2011–2026). Each entry links to the study's record on PubMed and, where there is one, to the publisher's page and a free full text. 9 have an abstract, the authors' own summary as PubMed records it: open "Abstract" to read it.

About the journal

• Publisher: MDPI
• Published: 2001 to the present, Switzerland
• Language: English
• ISSN: 1424-8220 (electronic)
• Its record in the NLM Catalog

2020s

• An Automated Fluorescence Microscopy-Based Sensing System for Continuous Detection of Airborne Asbestos Fibers on a PM2.5 Monitoring Platform. A. Kuroda, K. Kaga, T. Nishimura, et al. 2026;26(10). PubMed · Publisher · Free full text

  Abstract

  Despite regulations restricting asbestos use in many developed countries, asbestos-containing materials (ACMs) persist in aging buildings and can release airborne fibers during demolition and renovation. Therefore, continuous monitoring of airborne asbestos fibers is essential for environmental safety and exposure assessment. Fluorescence microscopy (FM) with fluorescently labeled asbestos-binding proteins offers greater sensitivity and selectivity in detection compared with conventional phase contrast microscopy (PCM). However, its practical application is limited by manual sample preparation and microscopic observations. This study introduces the conceptual design and initial development of an automated FM-based sensing system for monitoring airborne asbestos fibers. The system was constructed by modifying a commercial PM2.5 continuous air sampling platform and integrating automated fluorescent staining, FM imaging, and AI-assisted image analysis for fiber recognition and counting. The system automatically reports airborne asbestos concentrations with corresponding fluorescence images and advances the membrane filter to enable continuous measurements. Performance evaluation using pulverized ACMs showed an overall agreement within 14.1% with PCM-scanning electron microscopy measurements at the group level. Although variability was observed at low fiber concentrations owing to stochastic sampling effects, the results validate the feasibility of automated FM-based sensing for continuous environmental monitoring of airborne asbestos fibers.

• Signal Detection by Sensors and Determination of Friction Coefficient During Brake Lining Movement. L. Hrabovský, V. Molnár, G. Fedorko, et al. 2024;24(24). PubMed · Publisher · Free full text

  Abstract

  This article presents a laboratory device by which the course of two signals can be detected using two types of sensors-strain gauges and the DEWESoft DS-NET measuring apparatus. The values of the coefficient of friction of the brake lining when moving against the rotating shell of the brake drum were determined from the physical quantities sensed by tensometric sensors and transformed into electrical quantities. The friction coefficient of the brake lining on the circumference of the rotating brake disc shell can be calculated from the known values measured by the sensors, the design dimensions of the brake, and the revolutions of the rotating parts system. The values of the friction coefficient were measured during brake lining movement. A woven asbestos-free material, Beral 1126, which contained brass fibers and resin additives, showed slightly higher values when rotating at previously tested speeds compared to the friction coefficient values obtained when the brake drum rotation was uniformly delayed. The methodology for determining the friction coefficient of the brake lining allowed the laboratory device to verify its magnitude for different friction materials under various operating conditions.

• Construction of Asbestos Slate Deep-Learning Training-Data Model Based on Drone Images. S.C. Baek, K.H. Lee, I.H. Kim, et al. 2023;23(19). PubMed · Publisher · Free full text

  Abstract

  The detection of asbestos roof slate by drone is necessary to avoid the safety risks and costs associated with visual inspection. Moreover, the use of deep-learning models increases the speed as well as reduces the cost of analyzing the images provided by the drone. In this study, we developed a comprehensive learning model using supervised and unsupervised classification techniques for the accurate classification of roof slate. We ensured the accuracy of our model using a low altitude of 100 m, which led to a ground sampling distance of 3 cm/pixel. Furthermore, we ensured that the model was comprehensive by including images captured under a variety of light and meteorological conditions and from a variety of angles. After applying the two classification methods to develop the learning dataset and employing the as-developed model for classification, 12 images were misclassified out of 475. Visual inspection and an adjustment of the classification system were performed, and the model was updated to precisely classify all 475 images. These results show that supervised and unsupervised classification can be used together to improve the accuracy of a deep-learning model for the detection of asbestos roof slate.

• Digital Pulmonology Practice with Phonopulmography Leveraging Artificial Intelligence: Future Perspectives Using Dual Microwave Acoustic Sensing and Imaging. A.K. Sethi, P. Muddaloor, P. Anvekar, et al. 2023;23(12). Review PubMed · Publisher · Free full text

  Abstract

  Respiratory disorders, being one of the leading causes of disability worldwide, account for constant evolution in management technologies, resulting in the incorporation of artificial intelligence (AI) in the recording and analysis of lung sounds to aid diagnosis in clinical pulmonology practice. Although lung sound auscultation is a common clinical practice, its use in diagnosis is limited due to its high variability and subjectivity. We review the origin of lung sounds, various auscultation and processing methods over the years and their clinical applications to understand the potential for a lung sound auscultation and analysis device. Respiratory sounds result from the intra-pulmonary collision of molecules contained in the air, leading to turbulent flow and subsequent sound production. These sounds have been recorded via an electronic stethoscope and analyzed using back-propagation neural networks, wavelet transform models, Gaussian mixture models and recently with machine learning and deep learning models with possible use in asthma, COVID-19, asbestosis and interstitial lung disease. The purpose of this review was to summarize lung sound physiology, recording technologies and diagnostics methods using AI for digital pulmonology practice. Future research and development in recording and analyzing respiratory sounds in real time could revolutionize clinical practice for both the patients and the healthcare personnel.

• Asbestos Detection with Fluorescence Microscopy Images and Deep Learning. C. Cai, T. Nishimura, J. Hwang, et al. 2021;21(13). PubMed · Publisher · Free full text

  Abstract

  Fluorescent probes can be used to detect various types of asbestos (serpentine and amphibole groups); however, the fiber counting using our previously developed software was not accurate for samples with low fiber concentration. Machine learning-based techniques (e.g., deep learning) for image analysis, particularly Convolutional Neural Networks (CNN), have been widely applied to many areas. The objectives of this study were to (1) create a database of a wide-range asbestos concentration (0-50 fibers/liter) fluorescence microscopy (FM) images in the laboratory; and (2) determine the applicability of the state-of-the-art object detection CNN model, YOLOv4, to accurately detect asbestos. We captured the fluorescence microscopy images containing asbestos and labeled the individual asbestos in the images. We trained the YOLOv4 model with the labeled images using one GTX 1660 Ti Graphics Processing Unit (GPU). Our results demonstrated the exceptional capacity of the YOLOv4 model to learn the fluorescent asbestos morphologies. The mean average precision at a threshold of 0.5 ([email protected]) was 96.1% ± 0.4%, using the National Institute for Occupational Safety and Health (NIOSH) fiber counting Method 7400 as a reference method. Compared to our previous counting software (Intec/HU), the YOLOv4 achieved higher accuracy (0.997 vs. 0.979), particularly much higher precision (0.898 vs. 0.418), recall (0.898 vs. 0.780) and F-1 score (0.898 vs. 0.544). In addition, the YOLOv4 performed much better for low fiber concentration samples (<15 fibers/liter) compared to Intec/HU. Therefore, the FM method coupled with YOLOv4 is remarkable in detecting asbestos fibers and differentiating them from other non-asbestos particles.

2010s

• Intrinsic Sensing Properties of Chrysotile Fiber Reinforced Piezoelectric Cement-Based Composites. J. Luo, C. Zhang, L. Li, et al. 2018;18(9). PubMed · Publisher · Free full text

  Abstract

  Lead-zirconate-titanate (PZT) nanoscale powder was first synthesized by the sol-gel method, then PZT and 0⁻3 type PZT/chrysotile fiber (CSF)/cement composite (PZTCC) wafers were fabricated after grind-mixing PZT powder with strontium carbonate and/or cement, ductile CSF in tandem with press-sintered process, respectively. The crystal structure (XRD), microstructure (SEM), piezoelectric properties after surface silver penetration, and polarization of the PZT and PZTCC wafer were investigated. Furthermore, self-sensing responses under either impulse or cyclic loading and micro-hardness toughness of PZTCC were also investigated. Results show that the incorporation of CSF and cement admixture weakens the perovskite crystalline peak of PZTCC; reduces the corresponding piezoelectric coefficient from 119.2 pC/N to 32.5 pC/N; but effectively bridges the gap on the toughness between PZTCC and concrete since the corresponding microhardness with 202.7 MPa of PZTCC is close to that of concrete. A good linear and fast electrical response against either impulse or cyclic loading of the PZTCC is achieved with their respective sensitivity, linearity, and repeatability to 1.505 mV/N, 2.42%, and 2.11%. The sensing responses and toughness of PZTCC is encouraging as an intrinsic piezoelectric sensor for real-time health monitoring of ductile concrete structures.

• Mapping asbestos-cement roofing with hyperspectral remote sensing over a large mountain region of the Italian Western Alps. F. Frassy, G. Candiani, M. Rusmini, et al. 2014;14(9):15900-13. PubMed · Publisher · Free full text

  Abstract

  The World Health Organization estimates that 100 thousand people in the world die every year from asbestos-related cancers and more than 300 thousand European citizens are expected to die from asbestos-related mesothelioma by 2030. Both the European and the Italian legislations have banned the manufacture, importation, processing and distribution in commerce of asbestos-containing products and have recommended action plans for the safe removal of asbestos from public and private buildings. This paper describes the quantitative mapping of asbestos-cement covers over a large mountainous region of Italian Western Alps using the Multispectral Infrared and Visible Imaging Spectrometer sensor. A very large data set made up of 61 airborne transect strips covering 3263 km2 were processed to support the identification of buildings with asbestos-cement roofing, promoted by the Valle d'Aosta Autonomous Region with the support of the Regional Environmental Protection Agency. Results showed an overall mapping accuracy of 80%, in terms of asbestos-cement surface detected. The influence of topography on the classification's accuracy suggested that even in high relief landscapes, the spatial resolution of data is the major source of errors and the smaller asbestos-cement covers were not detected or misclassified.

• Selective detection and automated counting of fluorescently-labeled chrysotile asbestos using a dual-mode high-throughput microscopy (DM-HTM) method. M.O. Cho, H.M. Chang, D. Lee, et al. 2013;13(5):5686-99. PubMed · Publisher · Free full text

  Abstract

  Phase contrast microscopy (PCM) is a widely used analytical method for airborne asbestos, but it is unable to distinguish asbestos from non-asbestos fibers and requires time-consuming and laborious manual counting of fibers. Previously, we developed a high-throughput microscopy (HTM) method that could greatly reduce human intervention and analysis time through automated image acquisition and counting of fibers. In this study, we designed a dual-mode HTM (DM-HTM) device for the combined reflection and fluorescence imaging of asbestos, and automated a series of built-in image processing commands of ImageJ software to test its capabilities. We used DksA, a chrysotile-adhesive protein, for selective detection of chrysotile fibers in the mixed dust-free suspension of crysotile and amosite prepared in the laboratory. We demonstrate that fluorescently-stained chrysotile and total fibers can be identified and enumerated automatically in a high-throughput manner by the DM-HTM system. Combined with more advanced software that can correctly identify overlapping and branching fibers and distinguish between fibers and elongated dust particles, the DM-HTM method should enable fully automated counting of airborne asbestos.

• Automated counting of airborne asbestos fibers by a high-throughput microscopy (HTM) method. M.O. Cho, S. Yoon, H. Han, et al. 2011;11(7):7231-42. 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.

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