US 12,465,276 B2
Method and system for reflectance imaging of peripheral nerves
Jung Sun Yoo, Hong Kong (CN); and Ngai Nick Alex Wong, Hong Kong (CN)
Assigned to The Hong Kong Polytechnic University, Hong Kong (CN)
Filed by THE HONG KONG POLYTECHNIC UNIVERSITY, Hong Kong (CN)
Filed on Dec. 5, 2022, as Appl. No. 18/061,500.
Claims priority of provisional application 63/265,158, filed on Dec. 9, 2021.
Prior Publication US 2023/0181091 A1, Jun. 15, 2023
Int. Cl. A61B 5/00 (2006.01); G01N 21/27 (2006.01); G01N 21/64 (2006.01)
CPC A61B 5/4041 (2013.01) [A61B 5/0082 (2013.01); G01N 21/27 (2013.01); G01N 21/6428 (2013.01); G01N 2021/6439 (2013.01); G01N 2201/0612 (2013.01); G01N 2201/062 (2013.01)] 5 Claims
OG exemplary drawing
 
1. A system for real time imaging a peripheral nerve in a tissue sample in a surgery, the system comprising:
a light source configured to irradiate the tissue sample,
a photodetector configured to detect reflected light at a wavelength of 410-490 nm from the tissue sample, and
a computer configured to:
generate one or more images from the detected reflected light;
use a first deep learning model to classify the one or more images so as to identify one or more nerve-related extracted images, wherein each of the one or more nerve-related extracted images is an image with a presence of nerve;
use a second deep learning model to perform segmentation of the peripheral nerve in each of the one or more nerve-related extracted images for highlighting one or more specific anatomical structures of the peripheral nerve to a surgeon during the surgery;
additionally use the first deep learning model to determine if the second deep learning model for nerve segmentation provides one or more confusing nerve segments; and
if it is determined that the second deep learning model provides the one or more confusing nerve segments, alert the surgeon to conduct a surgical procedure with extra caution to prevent damage to nerve during the surgery.