Open AccessData consistency and classification model transferability across biomedical Raman spectroscopy systems
Author(s) -
Picot Fabien,
Daoust François,
Sheehy Guillaume,
Dallaire Frédérick,
Chaikho Layal,
Bégin Théophile,
Kadoury Samuel,
Leblond Frédéric
Publication year - 2021
Publication title -
translational biophotonics
Language(s) - English
Resource type - Journals
ISSN - 2627-1850
DOI - 10.1002/tbio.202000019
Subject(s) - standardization , raman spectroscopy , transferability , computer science , imaging phantom , artificial intelligence , consistency (knowledge bases) , data mining , medical physics , machine learning , nuclear medicine , optics , medicine , physics , logit , operating system
Surgical guidance applications using Raman spectroscopy are being developed at a rapid pace in oncology to ensure safe and complete tumor resection during surgery. Clinical translation of these approaches relies on the acquisition of large spectral and histopathological data sets to train classification models. Data calibration must ensure compatibility across Raman systems and predictive model transferability to allow multi‐centric studies to be conducted. This paper addresses issues relating to Raman measurement standardization by first comparing Raman spectral measurements made on an optical phantom and acquired with nine distinct point probe systems and one wide‐field imaging instrument. Data standardization method led to normalized root‐mean‐square deviations between instruments of 2%. A classification model discriminating between white and gray matter was trained with one point probe system. When used to classify independent data sets acquired with the other systems, model predictions led to >95% accuracy, preliminarily demonstrating model transferability across different biomedical Raman spectroscopy instruments.