Ahmed, W;
Veluthandath, AV;
Rowe, DJ;
Madsen, J;
Clark, HW;
Postle, AD;
Wilkinson, JS;
(2022)
Prediction of Neonatal Respiratory Distress Biomarker Concentration by Application of Machine Learning to Mid-Infrared Spectra.
Sensors
, 22
(5)
, Article 1744. 10.3390/s22051744.
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Abstract
The authors of this study developed the use of attenuated total reflectance Fourier transform infrared spectroscopy (ATR–FTIR) combined with machine learning as a point-of-care (POC) diagnostic platform, considering neonatal respiratory distress syndrome (nRDS), for which no POC currently exists, as an example. nRDS can be diagnosed by a ratio of less than 2.2 of two nRDS biomarkers, lecithin and sphingomyelin (L/S ratio), and in this study, ATR–FTIR spectra were recorded from L/S ratios of between 1.0 and 3.4, which were generated using purified reagents. The calibration of principal component (PCR) and partial least squares (PLSR) regression models was performed using 155 raw baselined and second derivative spectra prior to predicting the concentration of a further 104 spectra. A three-factor PLSR model of second derivative spectra best predicted L/S ratios across the full range (R2: 0.967; MSE: 0.014). The L/S ratios from 1.0 to 3.4 were predicted with a prediction interval of +0.29, −0.37 when using a second derivative spectra PLSR model and had a mean prediction interval of +0.26, −0.34 around the L/S 2.2 region. These results support the validity of combining ATR–FTIR with machine learning to develop a point-of-care device for detecting and quantifying any biomarker with an interpretable mid-infrared spectrum.
| Type: | Article |
|---|---|
| Title: | Prediction of Neonatal Respiratory Distress Biomarker Concentration by Application of Machine Learning to Mid-Infrared Spectra |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.3390/s22051744 |
| Publisher version: | https://doi.org/10.3390/s22051744 |
| Language: | English |
| Additional information: | This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited |
| Keywords: | ATR–FTIR; spectroscopy; machine learning; neonatal respiratory distress syndrome; point-of-care devices |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL EGA Institute for Womens Health > Neonatology UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL EGA Institute for Womens Health UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10144888 |
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