Proof-of-concept study of noninvasive, rapid, machine learning-enhanced, color-based CSF diagnostics: a novel approach to external ventricular drain infection screening.

IF 3.6 2区 医学 Q1 CLINICAL NEUROLOGY
Bilal B Akbulut, Barış O Gürses, Semiha Özgül, Mustafa S Bölük, Taşkın Yurtseven, Hüseyin Biçeroğlu
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引用次数: 0

Abstract

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.

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。
目的:目的是开发和验证一种概念验证,低成本,无创设备,能够连续监测脑脊液外脑室引流系统,以便能够早期发现感染。方法:作者设计了BOSoMetre (CSF-o- meter),这是一种使用微控制器和3d打印腔内的TCS3200颜色传感器的设备,用于连续监测CSF。该系统通过外心室漏(EVD)管捕获红、绿、蓝和透明通道的实时光学测量。在2024年10月至2025年1月期间,作者前瞻性地招募了20例因梗阻性脑积水或感染而需要植入EVD的患者,其中15例纳入最终分析。脑脊液样本根据美国传染病学会2017年指南进行分类。作者处理了大约480万个传感器读取,并使用两种验证方法应用机器学习算法:具有80-20分裂验证的子空间k-近邻(KNN)分类器和具有留一名患者交叉验证(LOOCV)的随机森林。结果:经80-20分割验证的子空间KNN分类器准确率为90.4%,灵敏度为92%,特异度为90.4%(曲线下面积[AUC] 0.968)。更严格的随机森林LOOCV方法准确率为81.1%,灵敏度为71.5%,特异性为89.2% (AUC 0.736)。该装置成功地区分了干净和感染的脑脊液样本,在识别未感染样本方面具有特别高的特异性。结论:BOSoMetre有望成为一种低成本(< 100欧元)的开源工具,用于脑脊液持续监测和早期感染检测,特别是在资源有限的环境中。高特异性可以潜在地减少不必要的脑脊液采样和相关的医源性感染风险。虽然最初的结果令人鼓舞,但需要在更大的队列中进一步验证,以确认临床实用性,并克服该概念验证研究中确定的技术限制。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of neurosurgery
Journal of neurosurgery 医学-临床神经学
CiteScore
7.20
自引率
7.30%
发文量
1003
审稿时长
1 months
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.
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