Artificial intelligence-augmented, label-free molecular imaging method for tissue identification, cancer diagnosis, and cancer margin detection
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 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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Aug 13, 2021
- Source ID
- 10.1364/boe.428738
Entities
People
- Feibi Zheng
- Hong Zhao
- Jiasong Li
- Jun Liu
- Kai Liu
- Raksha Raghunathan
- Rebecca Danforth
- Stephen T C Wong
- Steven S. Shen
- Tiancheng He
- Xiaohui Yu
- Ye Wang
- Yunjie He
Organizations
- Houston Methodist Hospital
- John S. Dunn Foundation
- Shanghai Jiao Tong University
- United States Department of Defense