Surface enhanced Raman spectroscopy and machine learning as an accurate and rapid diagnostic tool for hydrocephalus
Jorge Servert Lerdo De Tejada1, Derren J Heyes2, Shumaila Chaudhry3
1Division of Neuroscience, Faculty of Biology, Medicine & Health, The University of Manchester, Manchester, UK. jorge.servertlerdodetejada@manchester.ac.uk.
Scientific Reports
|December 16, 2025
Summary
A new diagnostic method uses Surface-Enhanced Raman Spectroscopy (SERS) and machine learning to rapidly detect hydrocephalus in cerebrospinal fluid (CSF). This portable technique achieves high accuracy, offering a promising tool for early diagnosis, especially in resource-limited settings.
Area of Science:
- Neurology
- Biotechnology
- Data Science
Background:
- Hydrocephalus, a neurological disorder, involves cerebrospinal fluid (CSF) buildup, leading to severe deficits and high costs.
- Current diagnostic methods for hydrocephalus in children often detect the condition late, delaying critical interventions.
- Diverse causes of hydrocephalus necessitate innovative and rapid diagnostic approaches.
Purpose of the Study:
- To develop and validate a novel, rapid, and portable diagnostic tool for hydrocephalus using SERS and machine learning.
- To assess the accuracy and interpretability of the proposed molecular diagnostic technique for hydrocephalus detection.
Main Methods:
- Analysis of cerebrospinal fluid (CSF) samples from 117 patients (47 hydrocephalus, 70 controls) using silver nanoparticle-layered-cellulose strips for Surface-Enhanced Raman Spectroscopy (SERS).
- Application of an optimized Random Forest machine learning algorithm to SERS spectral data for hydrocephalus classification.
- Development of a grid search-based machine learning workflow for model interpretability and overfitting prediction.
Main Results:
- The integrated SERS and machine learning approach achieved 97% accuracy, 100% specificity, and 95% sensitivity in blind testing within 5 minutes.
- The portable Raman spectrometer and standardized SERS strips demonstrated clinical utility, particularly for resource-limited environments.
- The machine learning workflow provided insights into feature importance and model interpretability.
Conclusions:
- Surface-Enhanced Raman Spectroscopy (SERS) combined with machine learning presents an accurate, rapid, and portable diagnostic method for hydrocephalus.
- This technique holds potential for improving early detection and management of hydrocephalus, especially in underserved areas.
- Further validation in larger cohorts is recommended to refine predictive models and enhance clinical applicability for neurological conditions.
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