[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119122-en":3,"doc-seo-119122-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119122,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",8,"Research & Report","Machine Learning-Assisted 3D Flexible Organic Transistor for High-Accuracy Metabolites Analysis and Other Clinical Applications - Article","Integration of advanced diagnostic technologies in healthcare strengthens disease detection and management by improving accuracy and efficiency. This paper proposes machine learning-assisted 3D flexible fiber-based organic transistor (FOT) sensors for high-accuracy metabolite analysis and broader diagnostic use. Machine learning models process complex sensor data, extract patterns, and predict diagnostic outcomes with reported 100% high accuracy. The work investigates fabrication and operational mechanisms, then evaluates performance using practical human blood samples for hypernatremia syndrome, aiming to support personalized diagnostics and tailored treatment decisions based on individual metabolic profiles.","chemosensors  \nArticle  \nMachine Learning-Assisted 3D Flexible Organic Transistor for High-Accuracy Metabolites Analysis and Other Clinical Applications  \nCaizhi Liao 1,2,*, Huaxing Wu 2 and Luigi G. Occhipinti 1, *  \nCitation: Liao, C.; Wu, H.; Occhipinti, L.G. Machine Learning-Assisted 3D Flexible Organic Transistor for High-Accuracy Metabolites Analysis and Other Clinical Applications. Chemosensors 2024, 12, 174 . [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)chemosensors12090174  \nReceived: 22 July 2024  \nRevised: 26 August 2024  \nAccepted: 28 August 2024  \nPublished: 1 September 2024  \nCopyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Department of Engineering, The University of Cambridge, Cambridge CB2 1TN, UK  \n2 Department of Bioengineering, Sun Yat-Sen University, Guangzhou 510275, China  \n* Correspondence: [cl2006@cam.ac.uk](cl2006@cam.ac.uk) (C.L.); [lgo23@cam.ac.uk](lgo23@cam.ac.uk) (L.G.O.)  \nAbstract: The integration of advanced diagnostic technologies in healthcare is crucial for enhancing the accuracy and efficiency of disease detection and management. This paper presents an innovative approach combining machine learning-assisted 3D flexible fiber-based organic transistor (FOT) sensors for high-accuracy metabolite analysis and potential diagnostic applications. Machine learning algorithms further enhance the analytical capabilities of FOT sensors by effectively processing complex data, identifying patterns, and predicting diagnostic outcomes with 100% high accuracy. We explore the fabrication and operational mechanisms of these transistors, the role of machine learning in metabolite analysis, and their potential clinical applications by analyzing practical human blood samples for hypernatremia syndrome. This synergy not only improves diagnostic precision but also holds potential for the development of personalized diagnostics, tailoring treatments for individual metabolic profiles.  \nKeywords: machine learning (ML); fiber-based organic transistors (FOTs); metabolic analysis; electrolyte ions; clinical diagnostics  \n1. Introduction  \nThe precise and efficient diagnosis of metabolic disorders is pivotal in clinical medicine, as these conditions often have profound impacts on a patient’s overall health and wellbeing [1–3] . Metabolic ions, such as hydrogen ion (H+ ), sodium (Na+ ), potassium (K+ ), calcium (Ca2+ ), and magnesium (Mg2+ ), play critical roles in maintaining cellular function, signal transduction, and homeostasis [4–7] . Imbalances in these ions can indicate the occurrence of various metabolic disorders, including but not limited to, electrolyte imbalances, renal dysfunction, endocrine disorders, and acid–base disturbances. Traditional methods for diagnosing these metabolic ion imbalances often involve a combination of blood tests, urine tests, and sometimes even invasive procedures. However, these methods can be time-consuming, costly, and occasionally lack the sensitivity needed for early detection [8–10] .  \nRecent advancements in technology have led to the development of more sophisticated diagnostic tools that can provide rapid and accurate measurements of metabolic ions. Techniques such as advanced biosensors, mass spectrometry, and ion-selective electrodes are now being integrated into clinical practice [11–13] . These innovations not only improve the speed and accuracy of diagnoses but also enhance the ability to monitor the effectiveness of treatment regimes in real time [14–16] . Tubular and vertical organic transistors are threedimensional (3D) devices designed to optimize surface area and integration density inflexible electronics. Tubular transist","cbCaietkPAuHwexj","https://ap.wps.com/l/cbCaietkPAuHwexj","pdf",2851481,1,14,"English","en",105,"# Introduction\n## Metabolic disorder diagnosis and electrolyte ions\n## Existing diagnostic methods and limitations\n## Emerging diagnostic technologies and 3D organic transistors\n## Machine learning integration for metabolite analysis","[{\"question\":\"What problem does the sensor approach target?\",\"answer\":\"It targets accurate and efficient diagnosis of metabolic disorders by analyzing metabolite-related electrolyte ions from biological samples.\"},{\"question\":\"How does machine learning improve the 3D flexible FOT sensing results?\",\"answer\":\"Machine learning processes complex, high-dimensional sensor data to identify patterns and predict diagnostic outcomes with high reported accuracy.\"},{\"question\":\"What clinical application is discussed in the study?\",\"answer\":\"The study analyzes practical human blood samples for hypernatremia syndrome, showing how the sensor-and-ML system can support clinical decision-making.\"}]","Machine Learning-Assisted 3D Flexible Organic Transistor for High-Accuracy Metabolites Analysis and Other Clinical Applications - 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