volume | PIER Journals

Abstract

Severe pneumonia poses a significant threat to public health. Delayed diagnosis is a core challenge in treatment. This study uses two rapid, low-cost spectroscopic fingerprinting techniques - Raman spectroscopy and attenuated total reflectance Fourier transform infrared (ATR-FTIR) absorption spectroscopy - to analyze biofluids such as blood and bronchoalveolar lavage fluid (BALF). In contrast to our earlier work which combined infrared spectra with clinical biochemical test results, this paper focuses solely on the spectral data to validate a fast and label-free diagnostic method. We used a spectral transformer network (STNetwork) to perform AI-based classification of severe pneumonia from the spectral fingerprints of blood and BALF. While both modalities are effective, FTIR spectroscopy exhibits superior diagnostic precision (97.78% test accuracy) and stability (SD < 0.0139) for blood samples. BALF offers a unique window into the local lung microenvironment, and both metabolomic analysis and spectral fingerprint classification were performed. The classification results for BALF Raman spectra (enhanced with surface-enhanced Raman spectroscopy) gave a training accuracy of 96.71%±1.86% and a testing accuracy of 90.62%±3.95%, better than the classification results for BALF FTIR spectra. The present study provides a reliable technical foundation for developing rapid and high-accuracy screening solutions for severe pneumonia.

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Prof. Sailing He

Dr. Jialun Li

Dr. Anqi Yang

Dr. Chenhui Wang

Ms. Chuan Zhang

Dr. Xinyue Li

Dr. Ke Cui

Dr. Youzu Xu

Dr. Julian Samuel Goodwin Evans

Dr. Yinghe Xu