Bilal B Akbulut, Barış O Gürses, Semiha Özgül, Mustafa S Bölük, Taşkın Yurtseven, Hüseyin Biçeroğlu
{"title":"无创、快速、机器学习增强、基于颜色的脑脊液诊断的概念验证研究:一种新的脑室外漏感染筛查方法。","authors":"Bilal B Akbulut, Barış O Gürses, Semiha Özgül, Mustafa S Bölük, Taşkın Yurtseven, Hüseyin Biçeroğlu","doi":"10.3171/2025.5.JNS25628","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>The objective was to develop and validate a proof-of-concept, low-cost, noninvasive device capable of continuously monitoring CSF in external ventricular drainage systems in order to enable earlier detection of infections.</p><p><strong>Methods: </strong>The authors designed BOSoMetre (CSF-o-Meter), a device that uses a microcontroller and TCS3200 color sensor housed in a 3D-printed chamber for continuous CSF monitoring. The system captures real-time optical measurements across red, green, blue, and clear channels through the external ventricular drain (EVD) tube. Between October 2024 and January 2025, the authors prospectively enrolled 20 patients requiring EVD placement for obstructive hydrocephalus or infection, with 15 included in the final analysis. CSF samples were classified according to Infectious Diseases Society of America 2017 guidelines. The authors processed approximately 4.8 million sensor readings and applied machine learning algorithms using two validation approaches: the subspace k-nearest neighbors (KNN) classifier with 80-20 split validation, and random forest with leave-one-patient-out cross-validation (LOOCV).</p><p><strong>Results: </strong>The subspace KNN classifier with 80-20 split validation yielded 90.4% accuracy with 92% sensitivity and 90.4% specificity (area under the curve [AUC] 0.968). The more stringent random forest with LOOCV approach achieved 81.1% accuracy with 71.5% sensitivity and 89.2% specificity (AUC 0.736). The device successfully distinguished between clean and infected CSF samples, with particularly high specificity in identifying noninfected samples.</p><p><strong>Conclusions: </strong>BOSoMetre shows promise as a low-cost (< €100), open-source tool for continuous CSF monitoring and early infection detection, especially for resource-limited settings. The high specificity could potentially reduce unnecessary CSF sampling and associated iatrogenic infection risks. Although the initial results are encouraging, further validation in larger cohorts is needed to confirm clinical utility and overcome the technical limitations identified in this proof-of-concept study.</p>","PeriodicalId":16505,"journal":{"name":"Journal of neurosurgery","volume":" ","pages":"1-11"},"PeriodicalIF":3.6000,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Proof-of-concept study of noninvasive, rapid, machine learning-enhanced, color-based CSF diagnostics: a novel approach to external ventricular drain infection screening.\",\"authors\":\"Bilal B Akbulut, Barış O Gürses, Semiha Özgül, Mustafa S Bölük, Taşkın Yurtseven, Hüseyin Biçeroğlu\",\"doi\":\"10.3171/2025.5.JNS25628\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objective: </strong>The objective was to develop and validate a proof-of-concept, low-cost, noninvasive device capable of continuously monitoring CSF in external ventricular drainage systems in order to enable earlier detection of infections.</p><p><strong>Methods: </strong>The authors designed BOSoMetre (CSF-o-Meter), a device that uses a microcontroller and TCS3200 color sensor housed in a 3D-printed chamber for continuous CSF monitoring. The system captures real-time optical measurements across red, green, blue, and clear channels through the external ventricular drain (EVD) tube. Between October 2024 and January 2025, the authors prospectively enrolled 20 patients requiring EVD placement for obstructive hydrocephalus or infection, with 15 included in the final analysis. CSF samples were classified according to Infectious Diseases Society of America 2017 guidelines. The authors processed approximately 4.8 million sensor readings and applied machine learning algorithms using two validation approaches: the subspace k-nearest neighbors (KNN) classifier with 80-20 split validation, and random forest with leave-one-patient-out cross-validation (LOOCV).</p><p><strong>Results: </strong>The subspace KNN classifier with 80-20 split validation yielded 90.4% accuracy with 92% sensitivity and 90.4% specificity (area under the curve [AUC] 0.968). The more stringent random forest with LOOCV approach achieved 81.1% accuracy with 71.5% sensitivity and 89.2% specificity (AUC 0.736). The device successfully distinguished between clean and infected CSF samples, with particularly high specificity in identifying noninfected samples.</p><p><strong>Conclusions: </strong>BOSoMetre shows promise as a low-cost (< €100), open-source tool for continuous CSF monitoring and early infection detection, especially for resource-limited settings. The high specificity could potentially reduce unnecessary CSF sampling and associated iatrogenic infection risks. Although the initial results are encouraging, further validation in larger cohorts is needed to confirm clinical utility and overcome the technical limitations identified in this proof-of-concept study.</p>\",\"PeriodicalId\":16505,\"journal\":{\"name\":\"Journal of neurosurgery\",\"volume\":\" \",\"pages\":\"1-11\"},\"PeriodicalIF\":3.6000,\"publicationDate\":\"2025-09-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of neurosurgery\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.3171/2025.5.JNS25628\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CLINICAL NEUROLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of neurosurgery","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.3171/2025.5.JNS25628","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CLINICAL NEUROLOGY","Score":null,"Total":0}
Proof-of-concept study of noninvasive, rapid, machine learning-enhanced, color-based CSF diagnostics: a novel approach to external ventricular drain infection screening.
Objective: The objective was to develop and validate a proof-of-concept, low-cost, noninvasive device capable of continuously monitoring CSF in external ventricular drainage systems in order to enable earlier detection of infections.
Methods: The authors designed BOSoMetre (CSF-o-Meter), a device that uses a microcontroller and TCS3200 color sensor housed in a 3D-printed chamber for continuous CSF monitoring. The system captures real-time optical measurements across red, green, blue, and clear channels through the external ventricular drain (EVD) tube. Between October 2024 and January 2025, the authors prospectively enrolled 20 patients requiring EVD placement for obstructive hydrocephalus or infection, with 15 included in the final analysis. CSF samples were classified according to Infectious Diseases Society of America 2017 guidelines. The authors processed approximately 4.8 million sensor readings and applied machine learning algorithms using two validation approaches: the subspace k-nearest neighbors (KNN) classifier with 80-20 split validation, and random forest with leave-one-patient-out cross-validation (LOOCV).
Results: The subspace KNN classifier with 80-20 split validation yielded 90.4% accuracy with 92% sensitivity and 90.4% specificity (area under the curve [AUC] 0.968). The more stringent random forest with LOOCV approach achieved 81.1% accuracy with 71.5% sensitivity and 89.2% specificity (AUC 0.736). The device successfully distinguished between clean and infected CSF samples, with particularly high specificity in identifying noninfected samples.
Conclusions: BOSoMetre shows promise as a low-cost (< €100), open-source tool for continuous CSF monitoring and early infection detection, especially for resource-limited settings. The high specificity could potentially reduce unnecessary CSF sampling and associated iatrogenic infection risks. Although the initial results are encouraging, further validation in larger cohorts is needed to confirm clinical utility and overcome the technical limitations identified in this proof-of-concept study.
期刊介绍:
The Journal of Neurosurgery, Journal of Neurosurgery: Spine, Journal of Neurosurgery: Pediatrics, and Neurosurgical Focus are devoted to the publication of original works relating primarily to neurosurgery, including studies in clinical neurophysiology, organic neurology, ophthalmology, radiology, pathology, and molecular biology. The Editors and Editorial Boards encourage submission of clinical and laboratory studies. Other manuscripts accepted for review include technical notes on instruments or equipment that are innovative or useful to clinicians and researchers in the field of neuroscience; papers describing unusual cases; manuscripts on historical persons or events related to neurosurgery; and in Neurosurgical Focus, occasional reviews. Letters to the Editor commenting on articles recently published in the Journal of Neurosurgery, Journal of Neurosurgery: Spine, and Journal of Neurosurgery: Pediatrics are welcome.