Objective:

To examine the barriers to clinical adoption of machine learning-enabled vibrational spectroscopy, focusing on the complete analytical pipeline.

Approach:

• Review of Analytical Pipeline: The review covers sample preparation, spectral acquisition, preprocessing, modeling, interpretation, and validation in vibrational spectroscopy.

Key Findings:

• Inconsistent analytical practices are a larger barrier to clinical adoption than model performance.
• High classification accuracy on small datasets does not guarantee model effectiveness in diverse clinical settings.
• Preprocessing decisions can significantly affect classification results and are often inadequately reported.
• Small, institution-specific datasets increase the risk of overfitting and confounding signals.
• Portable instruments may require recalibration and adaptation due to lower performance compared to benchtop systems.

Interpretation:

Hybrid approaches combining machine learning with chemically meaningful inputs may improve model reliability and interpretability.

Limitations:

• Lack of standardized reporting in spectral analysis.
• Limited availability of openly accessible reference datasets.
• Need for external validation across diverse patient cohorts and clinical conditions.

Conclusion:

Clinical progress in vibrational spectroscopy will rely more on reproducibility and transparency than on complex algorithms.

Sources:

• Microchemical Journal