Dataset Information

Near-Infrared Spectroscopy with Supervised Machine Learning as a Screening Tool for Neutropenia.

ABSTRACT: The use of non-invasive tools in conjunction with artificial intelligence (AI) to detect diseases has the potential to revolutionize healthcare. Near-infrared spectroscopy (NIR) is a technology that can be used to analyze biological samples in a non-invasive manner. This study evaluated the use of NIR spectroscopy in the fingertip to detect neutropenia in solid-tumor oncologic patients. A total of 75 patients were enrolled in the study. Fingertip NIR spectra and complete blood counts were collected from each patient. The NIR spectra were pre-processed using Savitzky-Golay smoothing and outlier detection. The pre-processed data were split into training/validation and test sets using the Kennard-Stone method. A toolbox of supervised machine learning classification algorithms was applied to the training/validation set using a stratified 5-fold cross-validation regimen. The algorithms included linear discriminant analysis (LDA), logistic regression (LR), random forest (RF), multilayer perceptron (MLP), and support vector machines (SVMs). The SVM model performed best in the validation step, with 85% sensitivity, 89% negative predictive value (NPV), and 64% accuracy. The SVM model showed 67% sensitivity, 82% NPV, and 57% accuracy on the test set. These results suggest that NIR spectroscopy in the fingertip, combined with machine learning methods, can be used to detect neutropenia in solid-tumor oncology patients in a non-invasive and timely manner. This approach could help reduce exposure to invasive tests and prevent neutropenic patients from inadvertently undergoing chemotherapy.

SUBMITTER: Raposo-Neto JJ

PROVIDER: S-EPMC10817549 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature

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Publications

Near-Infrared Spectroscopy with Supervised Machine Learning as a Screening Tool for Neutropenia.

Raposo-Neto José Joaquim JJ Kowalski-Neto Eduardo E Luiz Wilson Barros WB Fonseca Estherlita Almeida EA Cedro Anna Karla Costa Logrado AKCL Singh Maneesh N MN Martin Francis L FL Vassallo Paula Frizera PF Campos Luciene Cristina Gastalho LCG Barauna Valerio Garrone VG

Journal of personalized medicine 20231221 1

The use of non-invasive tools in conjunction with artificial intelligence (AI) to detect diseases has the potential to revolutionize healthcare. Near-infrared spectroscopy (NIR) is a technology that can be used to analyze biological samples in a non-invasive manner. This study evaluated the use of NIR spectroscopy in the fingertip to detect neutropenia in solid-tumor oncologic patients. A total of 75 patients were enrolled in the study. Fingertip NIR spectra and complete blood counts were collec ...[more]

PMID: 38276224

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Dataset Information

Near-Infrared Spectroscopy with Supervised Machine Learning as a Screening Tool for Neutropenia.

Publications

Near-Infrared Spectroscopy with Supervised Machine Learning as a Screening Tool for Neutropenia.

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