Unsupervised data analysis for virus detection with a surface plasmon resonance sensor

Siedhoff, Dominic; Strauch, Martin; Shpacovitch, Victoria; Merhof, Dorit

doi:10.1109/ipta.2017.8310145

Cited by 6 publications

(4 citation statements)

References 8 publications

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“…Unsupervised training is expected to attract more research interest and application opportunities as the AI field progresses, as it requires less effort in data collection and learns at a high level of autonomy. An unsupervised approach is not commonly seen in plasmonics research, but it is found useful in the analysis of spectroscopic data and time-domain electromagnetic simulations [69][70][71]. Reinforcement learning (RL) is a reward-based training method that stochastically improves the candidate's performance while being guided by environmental feedback [72].…”

Section: Overview Of Machine Learning Techniquesmentioning

confidence: 99%

Instantaneous Property Prediction and Inverse Design of Plasmonic Nanostructures Using Machine Learning: Current Applications and Future Directions

Aggarwal

Shankar

2022

Nanomaterials

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Advances in plasmonic materials and devices have given rise to a variety of applications in photocatalysis, microscopy, nanophotonics, and metastructures. With the advent of computing power and artificial neural networks, the characterization and design process of plasmonic nanostructures can be significantly accelerated using machine learning as opposed to conventional FDTD simulations. The machine learning (ML) based methods can not only perform with high accuracy and return optical spectra and optimal design parameters, but also maintain a stable high computing efficiency without being affected by the structural complexity. This work reviews the prominent ML methods involved in forward simulation and inverse design of plasmonic nanomaterials, such as Convolutional Neural Networks, Generative Adversarial Networks, Genetic Algorithms and Encoder–Decoder Networks. Moreover, we acknowledge the current limitations of ML methods in the context of plasmonics and provide perspectives on future research directions.

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Section: Overview Of Machine Learning Techniquesmentioning

confidence: 99%

Instantaneous Property Prediction and Inverse Design of Plasmonic Nanostructures Using Machine Learning: Current Applications and Future Directions

Aggarwal

Shankar

2022

Nanomaterials

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“…Enhancement of the plasma assisted nano-object microscopy (PAMONO) sensor used a deep neural network (DNN) technique for the detection of nanoparticles at a low signal noise ratio (SNR) [20]. The PAMONO sensor technique was used with the connected SPR platform to detect viruses without supervision [21]. The biosensor LSPR device coupling SPR platform was improved by using a graphene oxide/silver coating to identify viruses [22].…”

Section: Background Virus Detectionmentioning

confidence: 99%

An Analysis Review of Detection Coronavirus Disease 2019 (COVID-19) Based on Biosensor Application

Taha

Mashhadany

Mokhtar

et al. 2020

Sensors

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Timely detection and diagnosis are essentially needed to guide outbreak measures and infection control. It is vital to improve healthcare quality in public places, markets, schools and airports and provide useful insights into the technological environment and help researchers acknowledge the choices and gaps available in this field. In this narrative review, the detection of coronavirus disease 2019 (COVID-19) technologies is summarized and discussed with a comparison between them from several aspects to arrive at an accurate decision on the feasibility of applying the best of these techniques in the biosensors that operate using laser detection technology. The collection of data in this analysis was done by using six reliable academic databases, namely, Science Direct, IEEE Xplore, Scopus, Web of Science, Google Scholar and PubMed. This review includes an analysis review of three highlights: evaluating the hazard of pandemic COVID-19 transmission styles and comparing them with Severe Acute Respiratory Syndrome (SARS) and Middle East Respiratory Syndrome (MERS) to identify the main causes of the virus spreading, a critical analysis to diagnose coronavirus disease 2019 (COVID-19) based on artificial intelligence using CT scans and CXR images and types of biosensors. Finally, we select the best methods that can potentially stop the propagation of the coronavirus pandemic.

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“…Other modified SPR sensors were employed to detect testosterone [48,49], gonadotropic hormones and luteinizing hormone [50], pituitary hormones such as human thyroid-stimulating, growth, follicle-stimulating [51], and insulin [52]. There are other reviews and articles that focus on the importance and the application of SPR biosensors for the diagnosis of medically important entities such as viruses, neurotransmitters, proteins, hormones, nucleic acids, cells, drugs, and disease biomarkers [53][54][55][56][57].…”

Section: The Importance Of Spr Biosensors In the Medical Diagnosismentioning

confidence: 99%

The Principle of Nanomaterials Based Surface Plasmon Resonance Biosensors and Its Potential for Dopamine Detection

Eddin

Fen

2020

Molecules

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For a healthy life, the human biological system should work in order. Scheduled lifestyle and lack of nutrients usually lead to fluctuations in the biological entities levels such as neurotransmitters (NTs), proteins, and hormones, which in turns put the human health in risk. Dopamine (DA) is an extremely important catecholamine NT distributed in the central nervous system. Its level in the body controls the function of human metabolism, central nervous, renal, hormonal, and cardiovascular systems. It is closely related to the major domains of human cognition, feeling, and human desires, as well as learning. Several neurological disorders such as schizophrenia and Parkinson’s disease are related to the extreme abnormalities in DA levels. Therefore, the development of an accurate, effective, and highly sensitive method for rapid determination of DA concentrations is desired. Up to now, different methods have been reported for DA detection such as electrochemical strategies, high-performance liquid chromatography, colorimetry, and capillary electrophoresis mass spectrometry. However, most of them have some limitations. Surface plasmon resonance (SPR) spectroscopy was widely used in biosensing. However, its use to detect NTs is still growing and has fascinated impressive attention of the scientific community. The focus in this concise review paper will be on the principle of SPR sensors and its operation mechanism, the factors that affect the sensor performance. The efficiency of SPR biosensors to detect several clinically related analytes will be mentioned. DA functions in the human body will be explained. Additionally, this review will cover the incorporation of nanomaterials into SPR biosensors and its potential for DA sensing with mention to its advantages and disadvantages.

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Unsupervised data analysis for virus detection with a surface plasmon resonance sensor

Cited by 6 publications

References 8 publications

Instantaneous Property Prediction and Inverse Design of Plasmonic Nanostructures Using Machine Learning: Current Applications and Future Directions

Instantaneous Property Prediction and Inverse Design of Plasmonic Nanostructures Using Machine Learning: Current Applications and Future Directions

An Analysis Review of Detection Coronavirus Disease 2019 (COVID-19) Based on Biosensor Application

The Principle of Nanomaterials Based Surface Plasmon Resonance Biosensors and Its Potential for Dopamine Detection

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