Digital pathological scanners transform traditional glass slides into whole slide images (WSIs), which significantly improve the efficiency of pathological diagnosis and promote the development of digital pathology. However, the huge economic burden limits the spread and application of general WSI scanners in relatively remote and backward regions. In this paper, we develop an automatic portable cytopathology scanner based on mobile internet, Landing-Smart, to avert the above problems. Landing-Smart is a tiny device with a size of 208 mm × 107 mm × 104 mm and a weight of 1.8 kg, which integrates four main components including a smartphone, a glass slide carrier, an electric controller, and an optical imaging unit. By leveraging a simple optical imaging unit to substitute the sophisticated but complex conventional light microscope, the cost of Landing-Smart is less than $3000, much cheaper than general WSI scanners. On the one hand, Landing-Smart utilizes the built-in camera of the smartphone to acquire field of views (FoVs) in the section one by one. On the other hand, it uploads the images to the cloud server in real time via mobile internet, where the image processing and stitching method is implemented to generate the WSI of the cytological sample. The practical assessment of 209 cervical cytological specimens has demonstrated that Landing-Smart is comparable to general digital scanners in cytopathology diagnosis. Landing-Smart provides an effective tool for preliminary cytological screening in underdeveloped areas.
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http://dx.doi.org/10.1007/s10278-022-00761-1 | DOI Listing |
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January 2025
Electrical and Computer Engineering Department, The University of Alabama, Tuscaloosa, AL 35487, USA.
Discretely monitoring traffic systems and tracking payloads on vehicle targets can be challenging when traversal occurs off main roads where overhead traffic cameras are not present. This work proposes a portable roadside vehicle detection system as part of a solution for tracking traffic along any path. Training semantic segmentation networks to automatically detect specific types of vehicles while ignoring others will allow the user to track payloads present only on certain vehicles of interest, such as train cars or semi-trucks.
View Article and Find Full Text PDFMicromachines (Basel)
December 2024
Department of Mechanical Engineering, Shibaura Institute of Technology, 3-7-5 Toyosu, Koto-ku, Tokyo 135-8548, Japan.
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View Article and Find Full Text PDFACS Sens
January 2025
Interdisciplinary Research Division Smart HealthCare, Indian Institute of Technology Jodhpur, Jodhpur 342030, India.
Electronic nose (e-nose) systems are well known in breath analysis because they combine breath printing with advanced and intelligent machine learning (ML) algorithms. This work demonstrates development of an e-nose system comprising gas sensors exposed to six different volatile organic compounds (VOCs). The change in the voltage of the sensors was recorded and analyzed through ML algorithms to achieve selectivity and predict the VOCs.
View Article and Find Full Text PDFAnal Chem
January 2025
School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China.
Miniaturized mass spectrometers offer significant potential for in situ analysis due to their high specificity and portability. In traditional data-dependent acquisition (DDA) mode, precursor ions for tandem analysis are selected based on the full-scan mass spectrum. However, in situ applications often require the direct analysis of complex samples without extensive sample pretreatment, making them susceptible to chemical noise that can result in false negatives.
View Article and Find Full Text PDFBiophys Rev (Melville)
March 2025
Department of Electrical and Electronic Engineering, Dhaka University of Engineering & Technology, Gazipur 1707, Bangladesh.
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