	US 12,444,044 B2
	Artificial intelligence prediction of prostate cancer outcomes
Craig Mermel, Los Gatos, CA (US); Yun Liu, Mountain View, CA (US); Naren Manoj, Mountain View, CA (US); Matthew Symonds, Mountain View, CA (US); Martin Stumpe, Mountain View, CA (US); Lily Peng, Mountain View, CA (US); Kunal Nagpal, Mountain View, CA (US); Ellery Wulczyn, Mountain View, CA (US); Davis Foote, Mountain View, CA (US); David F. Steiner, Mountain View, CA (US); and Po-Hsuan Cameron Chen, Palo Alto, CA (US)
Assigned to Verily Life Sciences LLC, Dallas, TX (US)
Filed by Verily Life Sciences LLC, South San Francisco, CA (US)
Filed on Nov. 8, 2021, as Appl. No. 17/453,953.
Claims priority of provisional application 63/110,786, filed on Nov. 6, 2020.
Prior Publication US 2022/0148169 A1, May 12, 2022
Int. Cl. G06K 9/00 (2022.01); A61B 5/00 (2006.01); G06F 18/21 (2023.01); G06T 7/00 (2017.01)

CPC G06T 7/0012 (2013.01) [A61B 5/7275 (2013.01); G06F 18/21 (2023.01); G06T 2207/20081 (2013.01); G06T 2207/30024 (2013.01); G06T 2207/30081 (2013.01); G06T 2207/30096 (2013.01); G06V 2201/031 (2022.01)]

20 Claims

1. A computer-implemented method of assessing a prognosis of a prostate cancer patient, comprising the steps of:

receiving an image of prostate tissue;

assigning Gleason pattern values to one or more regions within the image using an artificial intelligence Gleason grading model, the model trained to identify Gleason patterns on a patch-by-patch basis in a prostate tissue image;

determining relative areal proportions of the Gleason patterns within the image;

generating a continuous risk score for the entire image based on the determined relative areal proportions;

assigning at least one of the continuous risk score or risk group value, the risk group value based on the continuous risk score; and

outputting at least one of the continuous risk score or the risk group value.