Perspectives on Machine Learning-Assisted Plasma Medicine: Toward Automated Plasma Treatment

Bonzanini, Angelo D.; Shao, Ketong; Stancampiano, Augusto; Graves, David B.; Mesbah, Ali

doi:10.1109/trpms.2021.3055727

Cited by 36 publications

(40 citation statements)

References 119 publications

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“…[ 34 ] Some teams refer to plasma treatment time as dose, but the treatment time is not the essence of plasma dose; it is also affected by a variety of factors, such as discharge type, working gas, power input, frequency, discharge interval, and so on. [ 35 ] Therefore, there is not a clear definition of plasma dose so far, but the importance of the plasma dose‐effect correspondence is self‐evident, and the dose‐effect of ROS is also investigated in this work.…”

Section: Resultsmentioning

confidence: 99%

Numerical study on interactions of atmospheric plasmas and peptides by reactive molecular dynamic simulations

Ding

Wang

Tian

et al. 2022

Plasma Processes & Polymers

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Section: Resultsmentioning

confidence: 99%

Numerical study on interactions of atmospheric plasmas and peptides by reactive molecular dynamic simulations

Ding

Wang

Tian

et al. 2022

Plasma Processes & Polymers

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“…The definition of "plasma dose" will be aided by standardization, which is both a crucial problem and a necessity in the field of plasma medicine. The issue of determining the "plasma dose" using Al is growing in significance despite the fact that there is numerous research for plasma dose assessment in the literature [82,83]. In this study, it is expected that the widespread use of Al in the medical and biomedical fields today will enable the targeted standardization for plasma medicine.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning to Predict the Antimicrobial Activity of Cold Atmospheric Plasma-Activated Liquids

Özdemir¹,

Özdemir²,

Gül³

et al. 2022

Preprint

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Plasma is defined as the fourth state of matter and non-thermal plasma can be produced at atmospheric pressure under a high electrical field. The strong and broadspectrum antimicrobial effect of plasma-activated liquids (PALs) is now well known. The antimicrobial effects of PALs depend on many different variables, which complicates the comparison of different studies and determines the most dominant parameters of the antimicrobial effect. The proven applicability of machine learning (ML) in the medical field is encouraging for its application in the field of plasma medicine as well. Thus, ML applications on PALs could present a new perspective to better understand the influences of various parameters on their antimicrobial effects. In this paper, comparative supervised ML models are presented by using previously obtained data to qualitatively predict the in vitro antimicrobial activity of PALs. A literature search was performed and data is collected from 33 relevant articles. After the required normalization, feature encoding, and resampling steps, two supervised ML methods, namely classification, and regression are applied to data to obtain microbial inactivation (MI) predictions. For classification, MI is labeled in four categories and for regression, MI is used as a continuous variable. 16 different classifiers and 14 different regressors are implemented to predict MI value. Two different robust cross-validation strategies are conducted for classification and regression models to evaluate the proposed method; repeated stratified k-fold cross-validation and k-fold cross-validation, respectively. We also investigate the effect of different features on models. The results demonstrated that the hyperparameter-optimized Random Forest Classifier (oRFC) and Random Forest Regressor (oRFR) provided better results than other models for the classification and regression, respectively. Finally, the best test accuracy of 82.68% for oRFC and R 2 of 0.75 for the oRFR are obtained. ML techniques could contribute to a better understanding of plasma parameters that have a dominant * Corresponding author.

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“…The rapid development of AI can be used to transform the mathematical modeling, real-time diagnostics, and optimal operation of LTP sources for applications in plasma medicine [59]. In addition, new oxidation-resistant and highstrength electrode materials, as well as new power semiconductor devices can enhance the efficiency of LTP sources and reduce the cost of their development and manufacturing.…”

Section: B Challenges and Proposed Solutionsmentioning

confidence: 99%

Low-Temperature Plasma for Biology, Hygiene, and Medicine: Perspective and Roadmap

Laroussi

Bekeschus

Keidar

et al. 2022

IEEE Trans. Radiat. Plasma Med. Sci.

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Plasma, the fourth and most pervasive state of matter in the visible universe, is a fascinating medium that is connected to the beginning of our universe itself. Man-made plasmas are at the core of many technological advances that include the fabrication of semiconductor devices, which enabled the modern computer and communication revolutions. The introduction of low temperature, atmospheric pressure plasmas to the biomedical field has ushered a new revolution in the healthcare arena that promises to introduce plasma-based therapies to combat some thorny and long-standing medical challenges. This article presents an overview of where research is at today and discusses innovative concepts and approaches to overcome present challenges and take the field to the next level. It is written by a team of experts who took an in-depth look at the various applications of plasma in hygiene, decontamination, and medicine, made critical analysis, and proposed ideas and concepts that should help the research community focus their efforts on clear and practical steps necessary to keep the field advancing for decades to come.

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Perspectives on Machine Learning-Assisted Plasma Medicine: Toward Automated Plasma Treatment

Cited by 36 publications

References 119 publications

Numerical study on interactions of atmospheric plasmas and peptides by reactive molecular dynamic simulations

Numerical study on interactions of atmospheric plasmas and peptides by reactive molecular dynamic simulations

Machine Learning to Predict the Antimicrobial Activity of Cold Atmospheric Plasma-Activated Liquids

Low-Temperature Plasma for Biology, Hygiene, and Medicine: Perspective and Roadmap

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