Mahtab Kokabi, Muhammad Tayyab, Gulam M. Rather, Arastou Pournadali Khamseh, Daniel Cheng, Edward P. DeMauro, Mehdi Javanmard
{"title":"Integrating optical and electrical sensing with machine learning for advanced particle characterization","authors":"Mahtab Kokabi, Muhammad Tayyab, Gulam M. Rather, Arastou Pournadali Khamseh, Daniel Cheng, Edward P. DeMauro, Mehdi Javanmard","doi":"10.1007/s10544-024-00707-0","DOIUrl":null,"url":null,"abstract":"<div><p>Particle classification plays a crucial role in various scientific and technological applications, such as differentiating between bacteria and viruses in healthcare applications or identifying and classifying cancer cells. This technique requires accurate and efficient analysis of particle properties. In this study, we investigated the integration of electrical and optical features through a multimodal approach for particle classification. Machine learning classifier algorithms were applied to evaluate the impact of combining these measurements. Our results demonstrate the superiority of the multimodal approach over analyzing electrical or optical features independently. We achieved an average test accuracy of 94.9% by integrating both modalities, compared to 66.4% for electrical features alone and 90.7% for optical features alone. This highlights the complementary nature of electrical and optical information and its potential for enhancing classification performance. By leveraging electrical sensing and optical imaging techniques, our multimodal approach provides deeper insights into particle properties and offers a more comprehensive understanding of complex biological systems.</p><h3>Graphical abstract</h3>\n<div><figure><div><div><picture><source><img></source></picture></div></div></figure></div></div>","PeriodicalId":490,"journal":{"name":"Biomedical Microdevices","volume":"26 2","pages":""},"PeriodicalIF":3.0000,"publicationDate":"2024-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11116188/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biomedical Microdevices","FirstCategoryId":"5","ListUrlMain":"https://link.springer.com/article/10.1007/s10544-024-00707-0","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
引用次数: 0
Abstract
Particle classification plays a crucial role in various scientific and technological applications, such as differentiating between bacteria and viruses in healthcare applications or identifying and classifying cancer cells. This technique requires accurate and efficient analysis of particle properties. In this study, we investigated the integration of electrical and optical features through a multimodal approach for particle classification. Machine learning classifier algorithms were applied to evaluate the impact of combining these measurements. Our results demonstrate the superiority of the multimodal approach over analyzing electrical or optical features independently. We achieved an average test accuracy of 94.9% by integrating both modalities, compared to 66.4% for electrical features alone and 90.7% for optical features alone. This highlights the complementary nature of electrical and optical information and its potential for enhancing classification performance. By leveraging electrical sensing and optical imaging techniques, our multimodal approach provides deeper insights into particle properties and offers a more comprehensive understanding of complex biological systems.
期刊介绍：
Biomedical Microdevices: BioMEMS and Biomedical Nanotechnology is an interdisciplinary periodical devoted to all aspects of research in the medical diagnostic and therapeutic applications of Micro-Electro-Mechanical Systems (BioMEMS) and nanotechnology for medicine and biology.
General subjects of interest include the design, characterization, testing, modeling and clinical validation of microfabricated systems, and their integration on-chip and in larger functional units. The specific interests of the Journal include systems for neural stimulation and recording, bioseparation technologies such as nanofilters and electrophoretic equipment, miniaturized analytic and DNA identification systems, biosensors, and micro/nanotechnologies for cell and tissue research, tissue engineering, cell transplantation, and the controlled release of drugs and biological molecules.
Contributions reporting on fundamental and applied investigations of the material science, biochemistry, and physics of biomedical microdevices and nanotechnology are encouraged. A non-exhaustive list of fields of interest includes: nanoparticle synthesis, characterization, and validation of therapeutic or imaging efficacy in animal models; biocompatibility; biochemical modification of microfabricated devices, with reference to non-specific protein adsorption, and the active immobilization and patterning of proteins on micro/nanofabricated surfaces; the dynamics of fluids in micro-and-nano-fabricated channels; the electromechanical and structural response of micro/nanofabricated systems; the interactions of microdevices with cells and tissues, including biocompatibility and biodegradation studies; variations in the characteristics of the systems as a function of the micro/nanofabrication parameters.
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