Recent Advancements and Future Prospects on E-Nose Sensors Technology and Machine Learning Approaches for Non-Invasive Diabetes Diagnosis: A Review
Abstract
Diabetes mellitus, commonly measured through an invasive process which although is accurate, has manifold drawbacks especially when multiple reading are required at regular intervals. Accordingly, there is a need to develop a dependable non-invasive diabetes detection technique. Recent studies have observed that other human serums such as tears, saliva, urine and breath indicate the presence of glucose in them. These parameters open quite a few ways for non-invasive blood glucose level prediction. The analysis of a persons breath poses as a good non-invasive technique to monitor the glucose levels. It is seen that in breath, there are many bio-markers and monitoring the levels of these bio-markers indicate the possibility of various chronic diseases. Among these bio-markers, acetone a volatile organic compound found in breath has shown a good correlation to the glucose levels present in blood. Therefore, by evaluating the acetone levels in breath samples it is possible to monitor diabetes non-invasively. This paper reviews the various approaches and sensory techniques used to monitor diabetes though human breath samples.
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AI-assisted design systems efficiently handle critical and repetitive optimization procedures, leading to more effective and efficient design outcomes.
“AI-assisted design systems efficiently handle these tasks, leading to more effective and efficient design outcomes.”
From Artificial intelligence enhanced sensors - enabling technologies to next-generation healthcare and biomedical platform (2023) · cites this paper as Lekha and Suchetha 2021
AI algorithms are becoming increasingly important for VOC gas detection because of their benefits for VOC sensors.
“AI algorithms are becoming increasingly important for VOC gas detection because of their benefits for VOC sensors (Haripriya et al. 2023; Lekha and Suchetha 2021; Lotsch et al. 2019; Mahmood et al. 2023; Thrift et al. 2019; Zhou et al. 2021; Zhou, H. et al. 2023a).”
From Artificial intelligence enhanced sensors - enabling technologies to next-generation healthcare and biomedical platform (2023) · cites this paper as Lekha and Suchetha 2021
Use of machine learning algorithms facilitates automated design of VOC sensors, eliminating the need for arduous and time-consuming design processes.
“First, the use of machine learning algorithms facilitates the automated design of volatile organic compound sensors, thereby eliminating the need for arduous and time-consuming design processes.”
From Artificial intelligence enhanced sensors - enabling technologies to next-generation healthcare and biomedical platform (2023) · cites this paper as Lekha and Suchetha 2021
Cites
A Convolutional Neural Network for Modelling Sentences
Kalchbrenner, N., Grefenstette, E., & Blunsom, P. (2014) · Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.3115/v1/p14-1062 · Cited by 3,601 · see citing works
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10578-9_23 · Cited by 3,034 · see citing works
Real-Time Patient-Specific ECG Classification by 1-D Convolutional Neural Networks
Kıranyaz, S., İnce, T., & Gabbouj, M. (2015) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2015.2468589 · Cited by 1,925 · see citing works
Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification
Salamon, J., & Bello, J. P. (2017) · IEEE Signal Processing Letters · doi:10.1109/lsp.2017.2657381 · Cited by 1,602 · see citing works
Real-Time Motor Fault Detection by 1-D Convolutional Neural Networks
İnce, T., Serkan Kiranyaz, B., Eren, L., Aşkar, M., & Gabbouj, M. (2016) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2016.2582729 · Cited by 1,419 · see citing works
Diagnostic potential of breath analysis—focus on volatile organic compounds
Miekisch, W., Schubert, J. K., & Noeldge‐Schomburg, G. (2004) · Clinica Chimica Acta · doi:10.1016/j.cccn.2004.04.023 · Cited by 1,100 · see citing works
Kaufman, E., & Lamster, I. B. (2002) · Critical Reviews in Oral Biology & Medicine · doi:10.1177/154411130201300209 · Cited by 887 · see citing works
Peng, G., Hakim, M., Broza, Y. Y., Billan, S., Abdah‐Bortnyak, R., Kuten, A., Tisch, U., & Haick, H. (2010) · British Journal of Cancer · doi:10.1038/sj.bjc.6605810 · Cited by 776 · see citing works
Electronic noses for food quality: A review
Loutfi, A., Coradeschi, S., Mani, G. K., Shankar, P., & Rayappan, J. B. B. (2014) · Journal of Food Engineering · doi:10.1016/j.jfoodeng.2014.07.019 · Cited by 742 · see citing works
Righettoni, M., Tricoli, A., & Pratsinis, S. E. (2010) · Analytical Chemistry · doi:10.1021/ac902695n · Cited by 631 · see citing works
Deng, C., ZHANG, J., Xiang, Y., Zhang, W., & ZHANG, X. (2004) · Journal of Chromatography B · doi:10.1016/s1570-0232(04)00657-9 · Cited by 591 · see citing works
Highly Sensitive WO3 Hollow-Sphere Gas Sensors
Li, X., Lou, T.-J., Sun, X., & Li, Y. (2004) · Inorganic Chemistry · doi:10.1021/ic049522w · Cited by 589 · see citing works
Correlation Between Sweat Glucose and Blood Glucose in Subjects with Diabetes
Moyer, J., Wilson, D., Finkelshtein, I., Wong, B., & Potts, R. (2012) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2011.0262 · Cited by 570 · see citing works
Feature selection and analysis on correlated gas sensor data with recursive feature elimination
Yan, K., & Zhang, D. D. (2015) · Sensors and Actuators B Chemical · doi:10.1016/j.snb.2015.02.025 · Cited by 528 · see citing works
Time series forecasting using a deep belief network with restricted Boltzmann machines
Kuremoto, T., Kimura, S., Kobayashi, K., & Obayashi, M. (2014) · Neurocomputing · doi:10.1016/j.neucom.2013.03.047 · Cited by 526 · see citing works
A contact lens with embedded sensor for monitoring tear glucose level
Yao, H., Shum, A. J., Cowan, M., Lähdesmäki, I., & Parviz, B. A. (2011) · Biosensors and Bioelectronics · doi:10.1016/j.bios.2010.12.042 · Cited by 513 · see citing works
Colorimetric Detection of Urine Glucose Based ZnFe2O4 Magnetic Nanoparticles
Li, S., Feng, J., Zhou, X., et al. (2012) · Analytical Chemistry · doi:10.1021/ac300939z · Cited by 502 · see citing works
The selected ion flow tube (SIFT); A technique for studying ion-neutral reactions
Adams, N. G., & Smith, D. C. (1976) · International Journal of Mass Spectrometry and Ion Physics · doi:10.1016/0020-7381(76)80133-7 · Cited by 397 · see citing works
Ferroelectric WO3 Nanoparticles for Acetone Selective Detection
Wang, L., Teleki, A., Pratsinis, S. E., & Gouma, P. (2008) · Chemistry of Materials · doi:10.1021/cm800761e · Cited by 358 · see citing works
Karpova, E. V., Shcherbacheva, E. V., Galushin, A. A., Vokhmyanina, D. V., Karyakina, E. E., & Karyakin, A. A. (2019) · Analytical Chemistry · doi:10.1021/acs.analchem.8b05928 · Cited by 246 · see citing works
Srivastava, A. K. (2003) · Sensors and Actuators B Chemical · doi:10.1016/s0925-4005(03)00477-5 · Cited by 240 · see citing works
Wang, C., Mbi, A., & Shepherd, M. D. (2009) · IEEE Sensors Journal · doi:10.1109/jsen.2009.2035730 · Cited by 236 · see citing works
Highly sensitive ZnO nanowire CO sensors with the adsorption of Au nanoparticles
Chang, S., Hsueh, T., Chen, I., et al. (2008) · Nanotechnology · doi:10.1088/0957-4484/19/17/175502 · Cited by 207 · see citing works
Trends in Nanomaterial-Based Non-Invasive Diabetes Sensing Technologies
Makaram, P., Owens, D., & Aceros, J. C. (2014) · Diagnostics · doi:10.3390/diagnostics4020027 · Cited by 202 · see citing works
Wearable-band type visible-near infrared optical biosensor for non-invasive blood glucose monitoring
Rachim, V. P., & Chung, W.-Y. (2019) · Sensors and Actuators B Chemical · doi:10.1016/j.snb.2019.01.121 · Cited by 200 · see citing works
Non-invasive Breath Analysis of Pulmonary Nodules
Peled, N., Hakim, M., Bunn, P. A., Miller, Y. E., Kennedy, T. C., Mattei, J., Mitchell, J. D., Hirsch, F. R., & Haick, H. (2012) · Journal of Thoracic Oncology · doi:10.1097/jto.0b013e3182637d5f · Cited by 194 · see citing works
Lin, Y., Bariya, M., Nyein, H. Y. Y., Kivimäki, L., Uusitalo, S., Jansson, E., Ji, W., Yuan, Z., Happonen, T., Liedert, C., et al. (2019) · Advanced Functional Materials · doi:10.1002/adfm.201902521 · Cited by 187 · see citing works
Turner, C., Walton, C., Hoashi, S., & Evans, M. L. (2009) · Journal of Breath Research · doi:10.1088/1752-7155/3/4/046004 · Cited by 183 · see citing works
A Novel Breath Analysis System Based on Electronic Olfaction
Guo, D., Zhang, D. D., Li, N., Lei Zhang, A., & Yang, J. (2010) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2010.2055864 · Cited by 178 · see citing works
BREATH-ACETONE AND BLOOD-SUGAR MEASUREMENTS IN DIABETES
Tassopoulos, C. N., Barnett, D., & Fraser, T. R. (1969) · The Lancet · doi:10.1016/s0140-6736(69)92222-3 · Cited by 171 · see citing works
Gutiérrez‐Osuna, R., & Nagle, H. T. (1999) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/3477.790446 · Cited by 165 · see citing works
A Sub-ppm Acetone Gas Sensor for Diabetes Detection Using 10 nm Thick Ultrathin InN FETs
Kao, K.-W., Hsu, M.-C., Chang, Y.-H., et al. (2012) · Sensors · doi:10.3390/s120607157 · Cited by 158 · see citing works
A novel method for diabetes diagnosis based on electronic nose
Wang, P., Yi, T., Haibao, X., & Farong, S. (1997) · Biosensors and Bioelectronics · doi:10.1016/s0956-5663(97)00059-6 · Cited by 146 · see citing works
Exhaled breath analysis by electronic nose in airways disease. Established issues and key questions
Fens, N., van der Schee, M. P., Brinkman, P., & Sterk, P. J. (2012) · Clinical & Experimental Allergy · doi:10.1111/cea.12052 · Cited by 145 · see citing works
Decision tree approach for classification and dimensionality reduction of electronic nose data
Cho, J. H., & Kurup, P. (2011) · Sensors and Actuators B Chemical · doi:10.1016/j.snb.2011.08.027 · Cited by 142 · see citing works
Rosias, P. P. R., Dompeling, E., Dentener, M. A., et al. (2004) · Pediatric Pulmonology · doi:10.1002/ppul.20056 · Cited by 137 · see citing works
Design of a Breath Analysis System for Diabetes Screening and Blood Glucose Level Prediction
Yan, K., Zhang, D. D., Wu, D., Wei, H., & Lu, G. (2014) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2014.2329753 · Cited by 137 · see citing works
Ueta, I., Saito, Y., Hosoe, M., et al. (2009) · Journal of Chromatography B · doi:10.1016/j.jchromb.2009.06.039 · Cited by 136 · see citing works
Salehi, S., Nikan, E., Khodadadi, A. A., et al. (2014) · Sensors and Actuators B Chemical · doi:10.1016/j.snb.2014.08.082 · Cited by 133 · see citing works
The sensing mechanism and detection of low concentration acetone using chitosan-based sensors
Nasution, T. I., Nainggolan, I., Hutagalung, S. D., et al. (2012) · Sensors and Actuators B Chemical · doi:10.1016/j.snb.2012.11.063 · Cited by 132 · see citing works
Correlations between blood glucose and breath components from portable gas sensors and PTR-TOF-MS
Righettoni, M., Schmid, A., Amann, A., & Pratsinis, S. E. (2013) · Journal of Breath Research · doi:10.1088/1752-7155/7/3/037110 · Cited by 127 · see citing works
Xiong, J., Zhang, Q., Sun, G., et al. (2015) · IEEE Sensors Journal · doi:10.1109/jsen.2015.2497545 · Cited by 116 · see citing works
Understanding the Potential of WO3 Based Sensors for Breath Analysis
Staerz, A., Weimar, U., & Bârsan, N. (2016) · Sensors · doi:10.3390/s16111815 · Cited by 115 · see citing works
*The Diabetes Research in Children Network (DirecNet) Study Group (2004) · Diabetes Care · doi:10.2337/diacare.27.3.722 · Cited by 114 · see citing works
Lekha, S., & Suchetha, M. (2017) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2017.2757510 · Cited by 108 · see citing works
Self-citationNon-invasive blood glucose monitoring for diabetics by means of breath signal analysis
Guo, D., Zhang, D. D., Lei Zhang, A., & Lu, G. (2012) · Sensors and Actuators B Chemical · doi:10.1016/j.snb.2012.06.025 · Cited by 105 · see citing works
Sensor Data Fusion by Support Vector Regression Methodology—A Comparative Study
Shamshirband, S., Petković, D., Javidnia, H., & Gani, A. (2014) · IEEE Sensors Journal · doi:10.1109/jsen.2014.2356501 · Cited by 97 · see citing works
Crofford, O. B., Mallard, R., Winton, R. E., et al. (1977) · PubMed · Cited by 95 · see citing works
Detection of Volatile Organic Compounds Using Microwave Sensors
Zarifi, M. H., Sohrabi, A., Shaibani, P. M., et al. (2014) · IEEE Sensors Journal · doi:10.1109/jsen.2014.2345477 · Cited by 93 · see citing works
A Deep Learning Approach for Blind Drift Calibration of Sensor Networks
Wang, Y., Yang, A., Chen, X., et al. (2017) · IEEE Sensors Journal · doi:10.1109/jsen.2017.2703885 · Cited by 93 · see citing works
Application of conducting polymer technology in microsystems
Gardner, J. W., & Bartlett, P. N. (1995) · Sensors and Actuators A Physical · doi:10.1016/0924-4247(95)85004-x · Cited by 90 · see citing works
Chen, Z., Zheng, Y., Chen, K., Li, H., & Jian, J. (2017) · IEEE Sensors Journal · doi:10.1109/jsen.2017.2653400 · Cited by 88 · see citing works
Pathologies currently identified by exhaled biomarkers
Mazzatenta, A., Giulio, C. D., & Pokorski, M. (2013) · Respiratory Physiology & Neurobiology · doi:10.1016/j.resp.2013.02.016 · Cited by 71 · see citing works
Analysis of Human Breath by Millimeter-Wave/Terahertz Spectroscopy
Rothbart, N., Holz, O., Koczulla, A. R., et al. (2019) · Sensors · doi:10.3390/s19122719 · Cited by 71 · see citing works
Wang, C., & Surampudi, A. B. (2008) · Measurement Science and Technology · doi:10.1088/0957-0233/19/10/105604 · Cited by 67 · see citing works
Robust face detection using local CNN and SVM based on kernel combination
Tao, Q., Zhan, S., Li, X.-H., & Kurihara, T. (2016) · Neurocomputing · doi:10.1016/j.neucom.2015.10.139 · Cited by 66 · see citing works
Sun, M., Chen, Z., Gong, Z., Zhao, X., Jiang, C., Yuan, Y., Wang, Z., Li, Y., & Wang, C. (2015) · Analytical and Bioanalytical Chemistry · doi:10.1007/s00216-014-8401-8 · Cited by 61 · see citing works
Saraoğlu, H. M., Selvı, A. O., Ebeoğlu, M. A., & Taşaltın, C. (2013) · IEEE Sensors Journal · doi:10.1109/jsen.2013.2265233 · Cited by 57 · see citing works
Trefz, P., Obermeier, J., Lehbrink, R., Schubert, J. K., Miekisch, W., & Fischer, D. (2019) · Scientific Reports · doi:10.1038/s41598-019-52165-x · Cited by 56 · see citing works
Monitoring breath markers under controlled conditions
Righettoni, M., Ragnoni, A., Güntner, A. T., et al. (2015) · Journal of Breath Research · doi:10.1088/1752-7155/9/4/047101 · Cited by 55 · see citing works
Yin, Z., & Zhang, J. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.05.002 · Cited by 55 · see citing works
Singh, A. K., & Jha, S. K. (2019) · IEEE Sensors Journal · doi:10.1109/jsen.2019.2920296 · Cited by 50 · see citing works
Breath Acetone Concentration Measured Using a Palm-Size Enzymatic Sensor System
Landini, B. E., & Bravard, S. T. (2009) · IEEE Sensors Journal · doi:10.1109/jsen.2009.2033305 · Cited by 40 · see citing works
Kim, I., Choi, S., Kim, S., et al. (2015) · KAIST research series · doi:10.1007/978-94-017-9981-2_2 · Cited by 39 · see citing works
UV photo-ionization based asymmetric field differential ion mobility sensor for trace gas detection
Suresh, M., Vasa, N. J., Agarwal, V., et al. (2014) · Sensors and Actuators B Chemical · doi:10.1016/j.snb.2014.01.008 · Cited by 38 · see citing works
Electronic nose system and principal component analysis technique for gases identification
Bedoui, S., Faleh, R., Samet, H., et al. (2013) · doi:10.1109/ssd.2013.6564152 · Cited by 37 · see citing works
CURRENT STATUS OF CLINICAL BREATH ANALYSIS
Risby, T. H. (2005) · doi:10.1142/9789812701954_0017 · Cited by 35 · see citing works
Blaikie, T., Couper, J. H., Hancock, G., et al. (2016) · Analytical Chemistry · doi:10.1021/acs.analchem.6b02837 · Cited by 35 · see citing works
A Novel 1-D Convolution Neural Network With SVM Architecture for Real-Time Detection Applications
Lekha, S., & Suchetha, M. (2017) · IEEE Sensors Journal · doi:10.1109/jsen.2017.2780178 · Cited by 33 · see citing works
Self-citationClassifying algorithms for SIFT-MS technology and medical diagnosis
Moorhead, K. T., Lee, D., Chase, J. G., et al. (2008) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2007.11.011 · Cited by 26 · see citing works
Bur, C., Andersson, M., Spetz, A. L., et al. (2014) · IEEE Sensors Journal · doi:10.1109/jsen.2014.2326693 · Cited by 23 · see citing works
Blood glucose prediction by breath analysis system with feature selection and model fusion
Yan, K., & Zhang, D. D. (2014) · doi:10.1109/embc.2014.6945094 · Cited by 22 · see citing works
Convolution neutral network enhanced binary sensor network for human activity recognition
Liu, G., Liang, J., Lan, G., Hao, Q., & Chen, M. (2016) · Proceedings of IEEE Sensors/Proceedings of IEEE Sensors ... · doi:10.1109/icsens.2016.7808519 · Cited by 21 · see citing works
Zia, A. I., Mukhopadhyay, S. C., Yu, P. L., et al. (2014) · IEEE Sensors Journal · doi:10.1109/jsen.2014.2355224 · Cited by 20 · see citing works
Current Status of Clinical Breath Analysis
Risby, T. H. (2006) · doi:10.1364/lacsea.2006.tud1 · Cited by 20 · see citing works
A Gaussian-shaped AT-cut quartz crystal resonator
Abe, T., & Kishi, H. (2010) · Sensors and Actuators A Physical · doi:10.1016/j.sna.2010.04.005 · Cited by 18 · see citing works
Design and Analysis of an Intelligent Flow Transmitter Using Artificial Neural Network
Sinha, S., & Mandal, N. (2017) · IEEE Sensors Letters · doi:10.1109/lsens.2017.2701409 · Cited by 17 · see citing works
A Principal Component Analysis Based Data Fusion Method for Estimation of Respiratory Volume
Liu, G., Zhou, G., Chen, W., & Jiang, Q. Y. (2015) · IEEE Sensors Journal · doi:10.1109/jsen.2015.2411288 · Cited by 13 · see citing works
The role of nanotechnology in diabetes treatment: current and future perspectives
Samuel, D., Bharali, D. J., & Mousa, S. A. (2010) · International Journal of Nanotechnology · doi:10.1504/ijnt.2011.037170 · Cited by 12 · see citing works
Applying Taguchi dynamic characteristics to the robust design of a piezoelectric sensor
Wu, D. H., Chien, W. T., & Tsai, Y. J. (2005) · IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control · doi:10.1109/tuffc.2005.1417270 · Cited by 12 · see citing works
Gases identification with Support Vector Machines technique (SVMs)
Bedoui, S., Samet, H., Samet, M., et al. (2014) · International Conference on Advanced Technologies for Signal and Image Processing · doi:10.1109/atsip.2014.6834620 · Cited by 9 · see citing works
Mecea, V. M., Carlsson, J., Alm, O., et al. (2005) · Sensors and Actuators A Physical · doi:10.1016/j.sna.2005.06.016 · Cited by 9 · see citing works
Design, modeling and simulation of CMOS-MEMS resonator for biomedical application
Rabih, A. A. S., Dennis, J. O., Khir, M. H. M., et al. (2014) · International Conference on Intelligent and Advanced Systems · doi:10.1109/icias.2014.6869455 · Cited by 8 · see citing works
Development of a non-invasive blood glucose monitor based on impedance measurements
Malinin, L. (2012) · International Journal of Biomedical Engineering and Technology · doi:10.1504/ijbet.2012.045358 · Cited by 7 · see citing works
Design of a noninvasive diabetes detector based on acetone recognition
Jiang, Y., Sun, M., & Xiong, R. J. (2018) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1026/1/012009 · Cited by 7 · see citing works
[Fast and accurate extraction of ring-down time in cavity ring-down spectroscopy].
Wang, D., Hu, R.-Z., Xie, P.-H., et al. (2014) · PubMed · Cited by 6 · see citing works
Tin Oxide Based Gas Sensor Array in Electronic Nose to Monitor Aroma of Black Tea
Dutta, D., Ghosh, S., Narjinary, M. B., et al. (2016) · Sensor Letters · doi:10.1166/sl.2016.3558 · Cited by 6 · see citing works
Using TD-GC-MS to analyze coffee beans aromas of different roast levels
Li, C., Chieng, L.-H., Thimmaraju, I. S., & Yao, D. (2014) · IEEE International Conference on Nano/Micro Engineered and Molecular Systems · doi:10.1109/nems.2014.6908860 · Cited by 5 · see citing works
Characterization of CMOS-MEMS device for acetone vapor detection in exhaled breath
Rabih, A. A. S., Khir, M. H. M., Ahmed, A. Y., et al. (2015) · IEEE Regional Symposium on Micro and Nanoelectronics · doi:10.1109/rsm.2015.7354966 · Cited by 5 · see citing works
A Portable Spectrophotometer-Based Breathalyzer for Point-of-Care Testing of Diabetic Patients
Banach, N. M., Rust, M. J., & Priefer, R. (2013) · Northeast Bioengineering Conference · doi:10.1109/nebec.2013.44 · Cited by 4 · see citing works
Lekha, S., & Suchetha, M. (2017) · IET Circuits Devices & Systems · doi:10.1049/iet-cds.2017.0002 · Cited by 4 · see citing works
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Wang, C., He, T., Zhou, H., Zhang, Z., & Lee, C. (2023) · Bioelectronic Medicine · doi:10.1186/s42234-023-00118-1 · Cited by 295 · see citing works
Li, Y., Jin, N., Zhan, Q., Huang, Y., Sun, A., Yin, F., Li, Z., Hu, J., & Liu, Z. (2025) · Frontiers in Endocrinology · doi:10.3389/fendo.2025.1495306 · Cited by 22 · see citing works
- Authors
- Lekha, S., & M, S.
- Affiliations
- Vellore Institute of Technology University (IN)
- ORCID
- Venue
- IEEE Reviews in Biomedical Engineering
- Published
- 2021
- Volume
- 14
- Pages
- 127-138
- ISSN
- 1937-3333 (ISSN-L)
- 1941-1189
- OpenAlex
- W3024571102
- Cited by
- 105
- References
- 91
- Citations by year
- 2026: 7 · 2025: 27 · 2024: 25 · 2023: 25 · 2022: 10 · 2021: 10 · 2020: 1
- FWCI
- 0.96
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Reviews in Biomedical Engineering (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Advanced Chemical Sensor Technologies
- Physical Sciences › Chemistry › Spectroscopy › Spectroscopy and Laser Applications
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- electronic nose, breath analysis, acetone, volatile organic compounds, blood glucose monitoring, machine learning, breath biomarkers, diabetes mellitus
- MeSH terms
- Machine Learning, Acetone, Breath Tests, Diabetes Mellitus, Glucose, Humans, Monitoring, Physiologic, Signal Processing, Computer-Assisted, Biosensing Techniques, Biomarkers, Electronic Nose
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