Real-time intraoperative glioma diagnosis using fluorescence imaging and deep convolutional neural networks.

ABSTRACT:

Purpose

Surgery is the predominant treatment modality of human glioma but suffers difficulty on clearly identifying tumor boundaries in clinic. Conventional practice involves neurosurgeon's visual evaluation and intraoperative histological examination of dissected tissues using frozen section, which is time-consuming and complex. The aim of this study was to develop fluorescent imaging coupled with artificial intelligence technique to quickly and accurately determine glioma in real-time during surgery.

Methods

Glioma patients (N = 23) were enrolled and injected with indocyanine green for fluorescence image-guided surgery. Tissue samples (N = 1874) were harvested from surgery of these patients, and the second near-infrared window (NIR-II, 1000-1700 nm) fluorescence images wer

SUBMITTER: Shen B 

PROVIDER: S-EPMC8440289 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature