Point-of-care detection of fibrosis in liver transplant surgery using near-infrared spectroscopy and machine learning

Feasibility of Near-Infrared Spectroscopy in the Classification of Pig Lung Lesions

Respiratory diseases significantly affect intensive pig farming, causing production losses and increased antimicrobial use. Accurate classification of lung lesions is crucial for effective diagnostics and disease management. The integration of non-destructive and rapid techniques would be beneficial to enhance overall efficiency in addressing these challenges. This study investigates the potential of near-infrared (NIR) spectroscopy in classifying pig lung tissues. The NIR spectra (908-1676 nm) of 101 lungs from weaned pigs were analyzed using a portable instrument and subjected to multivariate analysis. Two distinct discriminant models were developed to differentiate normal (N), congested (C), and pathological (P) lung tissues, as well as catarrhal bronchopneumonia (CBP), fibrinous pleuropneumonia (FPP), and interstitial pneumonia (IP) patterns. Overall, the model tailored for discriminating among pathological lesions demonstrated superior classification performances. Major challenges arose in categorizing C lungs, which exhibited a misclassification rate of 30% with N and P tissues, and FPP samples, with 30% incorrectly recognized as CBP samples. Conversely, IP and CBP lungs were all identified with accuracy, precision, and sensitivity higher than 90%. In conclusion, this study provides a promising proof of concept for using NIR spectroscopy to recognize and categorize pig lungs with different pathological lesions, offering prospects for efficient diagnostic strategies.

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

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.