{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T19:02:45Z","timestamp":1784314965757,"version":"3.55.0"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1014397","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T00:00:00Z","timestamp":1784246400000}}],"reference-count":31,"publisher":"Public Library of Science (PLoS)","issue":"7","license":[{"start":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T00:00:00Z","timestamp":1783987200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100020392","name":"Good Ventures Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100020392","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>\n                    Raman spectroscopy (RS) and surface-enhanced Raman spectroscopy (SERS) are promising technologies that have been applied across various fields, including clinical diagnostics. In the context of tuberculosis (TB) diagnosis, RS\/SERS offers significant potential for rapid, non-invasive, and sensitive biomolecular detection. However, no software currently exists that is specifically designed to analyze RS\/SERS data for TB diagnosis. Our goal is to develop such a tool by integrating machine learning (ML) and a one-dimensional convolutional neural network (1D-CNN) into a user-friendly graphical user interface (GUI). We introduce TB-SERS Analyzer, a Python-based tool with a GUI for tuberculosis prediction using SERS data. A reference database of 1,000 plasma samples (500 IGRA-positive, 500 IGRA-negative) was established using the interferon-gamma release assay (IGRA). TB-SERS Analyzer allows users to input spectral data and automatically generate TB diagnostic reports. ML and 1D-CNN models were trained and optimized via five-fold stratified cross-validation. We evaluated seven algorithms to identify the most effective method for TB classification. The 1D-CNN model achieved 82.00% sensitivity and 76.00% specificity in the validation set (n\u2009=\u2009200). In a blinded external test (n\u2009=\u200920), the model maintained 80.00% sensitivity with 100% specificity. The software comprises four integrated modules: (1) patient data extraction, (2) data preparation, (3) ML and 1D-CNN analysis, and (4) diagnostic report generation. TB-SERS Analyzer demonstrated high efficiency in TB screening, delivering results in under 10 seconds per sample. TB-SERS Analyzer is an effective and accessible tool for TB screening, combining RS\/SERS technologies with ML and 1D-CNN models. The software is freely available on GitHub at:\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/jkeisiri\/TB-SERS-Analyzer\" xlink:type=\"simple\">https:\/\/github.com\/jkeisiri\/TB-SERS-Analyzer<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1371\/journal.pcbi.1014397","type":"journal-article","created":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T17:36:42Z","timestamp":1784050602000},"page":"e1014397","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["TB-SERS analyzer: Analysis tool for tuberculosis prediction based on Raman spectroscopy with machine learning and convolutional neural network"],"prefix":"10.1371","volume":"22","author":[{"given":"Jukgarin","family":"Eisiri","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chadatan","family":"Juntagran","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kanwara","family":"Trisakul","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Benjawan","family":"Kaewseekhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Noppadon","family":"Nuntawong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chakchai","family":"So-In","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5022-4182","authenticated-orcid":true,"given":"Kiatichai","family":"Faksri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,7,14]]},"reference":[{"key":"pcbi.1014397.ref001","unstructured":"World Health Organization. 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