As the accuracy of body temperature measurement is especially critical in premature infants on admission to the neonatal intensive care unit (NICU), noninvasive measurement using infrared thermography (IRT) has not been widely adopted in the NICU due to a lack of evidence regarding its accuracy. We have established a new calibration method for IRT in an incubator, and evaluated its accuracy and reliability at different incubator settings using a variable-temperature blackbody furnace. This method improved the accuracy and reliability of IRT with an increase in percentage of data with mean absolute error (MAE) < 0.3 °C to 93.1% compared to 4.2% using the standard method. Two of three IRTs had MAE < 0.1 °C under all conditions examined. This method provided high accuracy not only for measurements at specific times but also for continuous monitoring. It will also contribute to avoiding the risk of neonates' skin trouble caused by attaching a thermistor. This study will facilitate the development of novel means of administering neonatal body temperature.
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http://dx.doi.org/10.1007/s10439-022-02937-w | DOI Listing |
Vet Sci
December 2024
Faculty of Veterinary Science, University of Thessaly, 43100 Karditsa, Greece.
This study aimed to investigate the incidence of subclinical mastitis (SCM), the implicated pathogens, and their impact on milk quality in dairy sheep in Greece. Furthermore, we preliminarily evaluated infrared thermography and the application of AI tools for the early, non-invasive diagnosis of relevant cases. In total, 660 milk samples and over 2000 infrared thermography images were obtained from 330 phenotypically healthy ewes.
View Article and Find Full Text PDFNanomaterials (Basel)
December 2024
Collaborative Innovation Center for Nanomaterials & Devices, College of Physics, Qingdao University, Qingdao 266071, China.
Irregularly shaped wounds cause severe chronic infections, which have attracted worldwide attention due to their high prevalence and poor treatment outcomes. In this study, we designed a new composite functional dressing consisting of traditional Chinese herb carbonized plant powder (CPP) and a polyacrylic acid (PAA)/polyethylenimine (PEI) gel. The rapid gelation of the dressing within 6-8 s allowed the gel to be firmly attached to an irregularly shaped wound surface and avoided powder detachment.
View Article and Find Full Text PDFJ Imaging
December 2024
Department of Computing, Electronics and Mechatronics, Universidad de las Américas Puebla, Sta. Catarina Martir, San Andrés Cholula 72810, Mexico.
Breast cancer is one of the leading causes of death for women worldwide, and early detection can help reduce the death rate. Infrared thermography has gained popularity as a non-invasive and rapid method for detecting this pathology and can be further enhanced by applying neural networks to extract spatial and even temporal data derived from breast thermographic images if they are acquired sequentially. In this study, we evaluated hybrid convolutional-recurrent neural network (CNN-RNN) models based on five state-of-the-art pre-trained CNN architectures coupled with three RNNs to discern tumor abnormalities in dynamic breast thermographic images.
View Article and Find Full Text PDFBiosensors (Basel)
December 2024
Academy for Engineering and Technology, Yiwu Research Institute, Fudan University, Shanghai 200433, China.
The prevention and early warning of foot ulcers are crucial in diabetic care; however, early microvascular lesions are difficult to detect and often diagnosed at later stages, posing serious health risks. Infrared thermal imaging, as a rapid and non-contact clinical examination technology, can sensitively detect hidden neuropathy and vascular lesions for early intervention. This review provides an informative summary of the background, mechanisms, thermal image datasets, and processing techniques used in thermal imaging for warning of diabetic foot ulcers.
View Article and Find Full Text PDFNatl Sci Rev
June 2024
International Center for Quantum Design of Functional Materials (ICQD), School of Emerging Technology, University of Science and Technology of China, Hefei 230026, China.
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