Abstract
Necrotizing enterocolitis (NEC) is a devastating disease affecting premature infants. Broadband optical spectroscopy (BOS) is a method of noninvasive optical data collection from intra-abdominal organs in premature infants, offering potential for disease detection. Herein, a novel machine learning approach, iterative principal component analysis (iPCA), is developed to select optimal wavelengths from BOS data collected in vivo from neonatal intensive care unit (NICU) patients for NEC classification. Neural network models were trained for classification, with a reduced-feature model distinguishing NEC with an accuracy of 88%, a sensitivity of 89%, and a specificity of 88%. While whole-spectrum models performed the best for accuracy and specificity, a reduced feature model excelled in sensitivity, with minimal cost to other metrics. This research supports the hypothesis that the analysis of human tissue via BOS may permit noninvasive disease detection. Furthermore, a medical device optimized with these models may potentially screen for NEC with as few as seven wavelengths.