US 11,918,178 B2
Detecting deficient coverage in gastroenterological procedures
Daniel Freedman, Zikhron Yaaqov (IL); Yacob Yochai Blau, Haifa (IL); Liran Katzir, Tel Aviv (IL); Amit Aides, Haifa (IL); Ilan Moshe Shimshoni, Haifa (IL); Ehud Benyamin Rivlin, Haifa (IL); and Yossi Matias, Tel Aviv (IL)
Assigned to Verily Life Sciences LLC, South San Francisco, CA (US)
Filed by Verily Life Sciences LLC, South San Francisco, CA (US)
Filed on Feb. 26, 2021, as Appl. No. 17/187,382.
Claims priority of provisional application 63/059,328, filed on Jul. 31, 2020.
Claims priority of provisional application 62/986,325, filed on Mar. 6, 2020.
Prior Publication US 2021/0280312 A1, Sep. 9, 2021
Int. Cl. G06T 7/50 (2017.01); A61B 1/00 (2006.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01); G06V 20/64 (2022.01); G16H 30/40 (2018.01); G16H 50/20 (2018.01)
CPC A61B 1/000096 (2022.02) [G06T 7/50 (2017.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01); G06V 20/647 (2022.01); G16H 30/40 (2018.01); G16H 50/20 (2018.01); G06T 2207/10068 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30032 (2013.01)] 20 Claims
OG exemplary drawing
 
12. A system for measuring coverage of a gastroenterological procedure, the system comprising:
a computing system comprising one or more processors and a non-transitory computer-readable memory;
wherein the non-transitory computer-readable memory stores instructions that, when executed by the processor, cause the computing system to perform operations, the operations comprising:
obtaining a plurality of images captured by an endoscopic device during a gastroenterological procedure for a patient, wherein the plurality of images depict respective portions of an anatomical structure viewed by the endoscopic device;
processing, using a machine-learned depth estimation model, the plurality of images to obtain a plurality of depth maps respectively for the plurality of images, wherein the depth map obtained for each image describes one or more depths of the respective portions of the anatomical structure from the endoscopic device; and
determining, using a machine-learned coverage estimation model, a coverage output of the anatomical structure based on the plurality of depth maps, wherein the coverage output indicates an amount of the anatomical structure which has been depicted by the plurality of images.