SERS & Machine Learning in Medicine

Machine Learning and SERS

A review by Alfred Chin Yen Tay and Liang Wang in TrAC Trends in Analytical Chemistry highlights how machine learning (ML) is turning surface-enhanced Raman spectroscopy (SERS) into a tool for medical diagnostics.

The review notes the increased use of artificial intelligence (AI), especially machine learning (ML), in clinical diagnostic applications. The study, led by Alfred Chin Yen Tay from the University of Western Australia and Liang Wang of Southern Medical University, details how it is improving the application of surface-enhanced Raman spectroscopy (SERS) in clinical diagnostics.

AI algorithms have been used to improve the accuracy of diagnosing diseases and to evaluate medical images to detect abnormalities, process diverse data types, and accelerate the diagnosis process.

SERS and AI Integration

SERS can detect trace levels of biological molecules and has been increasingly used in medical diagnostics in areas such as pathogen detection, biomarker discovery, and intraoperative guidance.

The complex data sets produced by modern SERS instruments have outpaced traditional linear data processing methods, requiring AI integration. The review outlines how different ML techniques are being integrated with SERS, covering signal acquisition and preprocessing to feature extraction, unsupervised clustering, and deep learning (DL).

ML algorithms can learn from and interpret vast and complex data sets, making them suitable for handling SERS signals. Some popular ML algorithms include principal component analysis (PCA), support vector machines (SVM), convolutional neural networks (CNN), and t-distributed stochastic neighbor embedding (t-SNE). These techniques have been used to classify spectral data, identify biomarkers, and predict disease states.

Key Takeaways

The authors break down the computational workflows and discuss how SERS and ML fit into them. The article emphasizes the computational strategies required to analyze Raman spectral data effectively and offers insight on the theoretical underpinnings of ML and DL, offering practical insights into model training, evaluation, and interpretation.

The authors provide examples of ML-assisted SERS applications, including rapid fluid diagnostics, where ML models help interpret complex bodily fluid spectra, and intraoperative cancer margin detection, where AI-integrated SERS systems provide real-time feedback to surgeons. Portable SERS devices, powered by ML algorithms, could be deployed in point-of-care settings to deliver diagnostics.

Challenges and Limitations

There is a lack of standardized SERS data format, difficulties in reproducing results across platforms, and the need for larger annotated data sets to train robust ML models. The authors suggest that interpretable ML models need to be developed further.

The continued evolution of both SERS instrumentation and AI methodologies could lead to many positive outcomes for healthcare diagnostics.

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