A New Approach for Chagas Disease Screening Using Serum Infrared Spectroscopy and Machine Learning Algorithms.

Area of Science:

• Spectroscopy and Analytical Chemistry
• Computational Biology and Bioinformatics
• Infectious Diseases and Epidemiology

Background:

• Chagas disease (CD) impacts millions globally, with diagnosis challenging due to low parasite levels and limitations of current serological tests.
• Geographic spread of CD is increasing due to human migration.
• There is a critical need for improved, accessible diagnostic tools for Chagas disease.

Purpose of the Study:

• To evaluate the diagnostic capability of attenuated total reflectance Fourier-transform infrared (ATR-FTIR) spectroscopy combined with machine learning (ML) for Chagas disease.
• To compare the performance of ATR-FTIR/ML under dry and wet serum sample analysis conditions.
• To assess the potential of this novel approach as a cost-effective diagnostic alternative.

Main Methods:

• Serum samples from 100 individuals (49 CD-positive, 51 controls) were analyzed using ATR-FTIR spectroscopy in both dry and wet formats.
• Spectral data were processed using various machine learning algorithms (logistic regression, PLS-DA, random forest, XGBoost) for classification.
• Model performance was evaluated using accuracy, F1-score, and area under the receiver operating characteristic curve (AUC).

Main Results:

• Logistic regression achieved 93% accuracy and F1-score on dry samples, while XGBoost yielded 87% accuracy and F1-score on wet samples.
• The area under the ROC curve was notably high: 0.99 for dry and 0.92 for wet analyses.
• Permutation tests confirmed the robustness and reliability of the developed classification models.

Conclusions:

• ATR-FTIR spectroscopy coupled with ML presents a highly promising diagnostic strategy for Chagas disease.
• This method demonstrates potential as an efficient and cost-effective alternative to conventional serological assays, particularly for resource-constrained settings.
• Further validation with larger cohorts is necessary to confirm clinical applicability and specificity for widespread adoption in Chagas disease management.