US 12,349,969 B2
Biometric ocular measurements using deep learning
Robert Dimitri Angelopoulos, San Jose, CA (US); and Bryan Stanfill, Mansfield, TX (US)
Assigned to Alcon Inc., Fribourg (CH)
Filed by Alcon Inc., Fribourg (CH)
Filed on Oct. 14, 2021, as Appl. No. 17/501,868.
Claims priority of provisional application 63/113,860, filed on Nov. 14, 2020.
Prior Publication US 2022/0151482 A1, May 19, 2022
Int. Cl. A61B 3/00 (2006.01); A61B 3/10 (2006.01); G06N 3/08 (2023.01)
CPC A61B 3/0025 (2013.01) [A61B 3/0058 (2013.01); A61B 3/102 (2013.01); G06N 3/08 (2013.01)] 1 Claim
OG exemplary drawing
 
1. A method for estimating biometric landmark dimensional measurements of a human eye, the method comprising:
receiving one or more ultrasonic images of the human eye via a host computer;
in response to receiving the one or more ultrasonic images, generating a preliminary set of landmark point locations in the one or more images via the host computer using a convolution neural network (CNN);
refining an image pixel intensity, contrast, and/or sharpness level of the preliminary set of landmark point locations to emphasize at least one landmark point location, using a post-hoc processing routine of the host computer, and to thereby generate a final set of estimated landmark point locations;
automatically measuring respective linear distances between different estimated landmark point locations in the final set of estimated landmark point locations to thereby generate the biometric landmark dimensional measurements, including an anterior chamber depth, a lens diameter, and/or a lens thickness of the human eye; and
outputting an annotated image and a data table inclusive of the linear distances.