Open AccessArtificial intelligence-augmented, label-free molecular imaging method for tissue identification, cancer diagnosis, and cancer margin detection
Author(s) -
Jiasong Li,
Jun Li,
Ye Wang,
Yunjie He,
Kai Li,
Raksha Raghunathan,
Steven S. Shen,
Tingchao He,
Xiaohui Yu,
Rebecca L. Danforth,
Feibi Zheng,
Hong Zhao,
Stephen T.C. Wong
Publication year - 2021
Publication title -
biomedical optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.362
H-Index - 86
ISSN - 2156-7085
DOI - 10.1364/boe.428738
Subject(s) - computer science , magnetic resonance imaging , artificial intelligence , cancer , molecular imaging , raman scattering , medical imaging , identification (biology) , medicine , radiology , raman spectroscopy , optics , biology , physics , microbiology and biotechnology , in vivo , botany
Label-free high-resolution molecular and cellular imaging strategies for intraoperative use are much needed, but not yet available. To fill this void, we developed an artificial intelligence-augmented molecular vibrational imaging method that integrates label-free and subcellular-resolution coherent anti-stokes Raman scattering (CARS) imaging with real-time quantitative image analysis via deep learning (artificial intelligence-augmented CARS or iCARS). The aim of this study was to evaluate the capability of the iCARS system to identify and differentiate the parathyroid gland and recurrent laryngeal nerve (RLN) from surrounding tissues and detect cancer margins. This goal was successfully met.