Artificial intelligence-augmented, label-free molecular imaging method for tissue identification, cancer diagnosis, and cancer margin detection

Jiasong Li, Jun Liu, Ye Wang, Yunjie He, Kai Liu, Raksha Raghunathan, Steven S. Shen, Tiancheng He, Xiaohui Yu, Rebecca Danforth, Feibi Zheng, Hong Zhao, Stephen T.C. Wong

Research output: Contribution to journalArticlepeer-review

Abstract

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 subcellularre solution 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.

Original languageEnglish (US)
Pages (from-to)5559-5582
Number of pages24
JournalBiomedical Optics Express
Volume12
Issue number9
DOIs
StatePublished - Sep 1 2021

ASJC Scopus subject areas

  • Biotechnology
  • Atomic and Molecular Physics, and Optics

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