[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126186-en":3,"doc-seo-126186-105":31,"detail-sidebar-cat-0-en-105":93},{"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126186,549768072016,"River Wang","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Integration of FTIR Spectroscopy and Machine Learning for Kidney Allograft Rejection - A Complementary Diagnostic Tool","Kidney transplantation restores kidney function for end-stage kidney disease, yet allograft rejection remains a critical barrier requiring accurate, timely diagnosis. This retrospective study integrates Fourier Transform Infrared (FTIR) spectroscopy with machine learning to detect kidney allograft rejection using pre-biopsy serum, and to differentiate T cell-mediated rejection (TCMR) from antibody-mediated rejection (AMR). Using Naïve Bayes models and spectral preprocessing/feature selection, performance is evaluated by AUC-ROC, sensitivity, specificity, and accuracy. Results show high AUC-ROC for both classification tasks, supporting minimally invasive decision support.","Article  \nIntegration of FTIR Spectroscopy and Machine Learning for Kidney Allograft Rejection: A Complementary Diagnostic Tool  \nLuís Ramalhete 1,2,3, *, Rúben Araújo 2, Miguel Bigotte Vieira 2,4, Emanuel Vigia 2,5, Inês Aires 4, Aníbal Ferreira 2,4 and Cecília R. C. Calado 6,7  \nAcademic Editor: Peter Schnuelle  \nReceived: 17 December 2024  \nRevised: 21 January 2025  \nAccepted: 25 January 2025  \nPublished: 27 January 2025  \nCitation: Ramalhete, L.; Araújo, R.; Vieira, M.B.; Vigia, E.; Aires, I.; Ferreira, A.; Calado, C.R.C. Integration of FTIR Spectroscopy and Machine Learning for Kidney Allograft Rejection: A Complementary Diagnostic Tool. J. Clin. Med. 2025, 14, 846. [https://](https://)[ ](https://)[doi.org/10.3390/jcm14030846](doi.org/10.3390/jcm14030846)  \n[Copyright:](Copyright:) © 2025 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://creativecommons.org/](https://creativecommons.org/)[ ](https://creativecommons.org/)[licenses/by/4.0/](licenses/by/4.0/)) .  \n1 Blood and Transplantation Center of Lisbon, Instituto Português do Sangue e da Transplantação, Alameda das Linhas de Torres, No. 117, 1769-001 Lisbon, Portugal  \n2 NOVA Medical School, Universidade NOVA de Lisboa, 1169-056 Lisbon, Portugal; [rubenalexandredinisaraujo@gmail.com](rubenalexandredinisaraujo@gmail.com) (R.A.)  \n3 iNOVA4Health—Advancing Precision Medicine, RG11: Reno-Vascular Diseases Group, NOVA Medical School, Faculdade de Ciências Médicas, Universidade NOVA de Lisboa, 1169-056 Lisbon, Portugal  \n4 Nephrology Department, Hospital Curry Cabral, Unidade Local de Saúde São José, 1049-001 Lisbon, Portugal  \n5 Centro Hospitalar Universitário de Lisboa Central, Hepatobiliopancreatic and Transplantation Center—Curry Cabral Hospital, 1069-166 Lisbon, Portugal  \n6 ISEL—Instituto Superior de Engenharia de Lisboa, Instituto Politécnico de Lisboa, R. Conselheiro Emídio Navarro 1, 1959-007 Lisbon, Portugal  \n7 Institute for Bioengineering and Biosciences (iBB), The Associate Laboratory Institute for Health and Bioeconomy–i4HB, Instituto Superior Técnico (IST), Universidade de Lisboa (UL), Av. Rovisco Pais, 1049-001 Lisbon, Portugal  \n* Correspondence: [luis.m.ramalhete@edu.nms.unl.pt](luis.m.ramalhete@edu.nms.unl.pt)  \nAbstract: Background: Kidney transplantation is a life-saving treatment for end-stage kidney disease, but allograft rejection remains a critical challenge, requiring accurate and timely diagnosis. The study aims to evaluate the integration of Fourier Transform Infrared (FTIR) spectroscopy and machine learning algorithms as a minimally invasive method to detect kidney allograft rejection and differentiate between T Cell-Mediated Rejection (TCMR) and Antibody-Mediated Rejection (AMR) . Additionally, the goal is to discriminate these rejection types aiming to develop a reliable decision-making support tool. Methods: This retrospective study included 41 kidney transplant recipients and analyzed 81 serum samples matched to corresponding allograft biopsies. FTIR spectroscopy was applied to pre-biopsy serum samples, and Naïve Bayes classification models were developed to distinguish rejection from non-rejection and classify rejection types. Data preprocessing involved, e.g., atmospheric compensation, second derivative, and feature selection using Fast Correlation-Based Filter for spectral regions 600–1900 cm−1 and 2800–3400 cm −1 . Model performance was assessed via area under the receiver operating characteristic curve (AUC-ROC), sensitivity, specificity, and accuracy. Results: The Naïve Bayes model achieved an AUC-ROC of 0.945 in classifying rejection versus non-rejection and AUC-ROC of 0.989 in distinguishing TCMR from AMR. Feature selection significantly improved model performance, identifying key spectral wavenumbers associated with rejection mechanisms. This approach demonstrated high sensitivity and speci","cbCaivkL4RwIljoB","https://ap.wps.com/l/cbCaivkL4RwIljoB","pdf",2005859,13,1,19,"English","en",105,"# Abstract\n# Introduction\n## Kidney transplantation and rejection challenge\n## Mechanisms of kidney allograft rejection\n## TCMR vs AMR\n# Methods\n## Study design and samples\n## FTIR preprocessing and feature selection\n## Classification models and evaluation metrics\n# Results\n## Rejection vs non-rejection performance\n## TCMR vs AMR performance\n# Conclusions","[{\"question\":\"What problem does the study address in kidney transplantation?\",\"answer\":\"It targets the challenge of accurately and promptly diagnosing kidney allograft rejection to support better clinical decision-making.\"},{\"question\":\"How is FTIR spectroscopy used in the diagnostic workflow?\",\"answer\":\"FTIR spectroscopy is applied to pre-biopsy serum samples to extract spectral information relevant to rejection status.\"},{\"question\":\"Which machine learning approach and evaluation metrics are used?\",\"answer\":\"Naïve Bayes classification models are trained, and performance is assessed using AUC-ROC, sensitivity, specificity, and accuracy.\"}]","Integration of FTIR Spectroscopy and Machine Learning for Kidney Allograft Rejection - 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