Proof-of-concept study of noninvasive, rapid, machine learning–enhanced, color-based CSF diagnostics: a novel approach to external ventricular drain infection screening
Journal of Neurosurgery, cilt.144, sa.1, ss.228-238, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 144 Sayı: 1
- Basım Tarihi: 2026
- Doi Numarası: 10.3171/2025.5.jns25628
- Dergi Adı: Journal of Neurosurgery
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, BIOSIS, EMBASE, MEDLINE
- Sayfa Sayıları: ss.228-238
- Anahtar Kelimeler: cerebrospinal fluid, colorimetry, diagnostic technique, hydrocephalus, infection, machine learning, physiological monitoring
- Süleyman Demirel Üniversitesi Adresli: Hayır
Özet
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.