Enhancing Vibrational Spectroscopy-Based Diagnosis through Bottom-Up Modeling: The Case of Infrared Absorption...

Area of Science:

• Biomedical Spectroscopy
• Computational Chemistry
• Machine Learning

Background:

• Vibrational spectroscopy offers valuable biomedical insights but requires extensive data for machine learning model development.
• Generating high-quality, representative spectral datasets for algorithm validation is a significant challenge.
• Diabetic kidney disease (DKD) diagnosis using spectroscopy lacks readily available, validated datasets.

Purpose of the Study:

• To develop an in silico framework for creating and validating spectroscopic diagnostic models.
• To demonstrate the framework's utility using infrared urine analysis for DKD diagnosis.
• To enable prediction of diagnostic performance using literature-derived parameters and simulated experimental conditions.

Main Methods:

• A bottom-up approach using a priori biological information to construct a synthetic spectral model of urine.
• Virtual simulation and optimization of experimental variables (e.g., protein preconcentration, measurement time, instrument settings).
• Generation of large, diverse synthetic datasets for machine learning model construction and validation.

Main Results:

• Machine learning models for predicting albumin and creatinine concentrations were developed using simulated data.
• Models built with the in silico approach showed no significant difference in performance compared to those built with artificial and real urine samples.
• The framework facilitated rapid, application-specific optimization of spectroscopic workflows.

Conclusions:

• The proposed in silico framework eliminates the need for resource-intensive empirical datasets.
• It enables systematic performance prediction and exploration of critical parameters for spectroscopic methods.
• This approach accelerates the development and optimization of vibrational spectroscopy for biomedical diagnostics like DKD.