US 12,390,089 B2
Processor for endoscope, endoscope system, information processing apparatus, non- transitory computer-readable storage medium, and information processing method using learning models
Kenta Kosugi, Tokyo (JP)
Assigned to HOYA CORPORATION, Tokyo (JP)
Appl. No. 17/634,389
Filed by HOYA CORPORATION, Tokyo (JP)
PCT Filed Aug. 16, 2019, PCT No. PCT/JP2019/032134
§ 371(c)(1), (2) Date Feb. 10, 2022,
PCT Pub. No. WO2021/033216, PCT Pub. Date Feb. 25, 2021.
Prior Publication US 2022/0322915 A1, Oct. 13, 2022
This patent is subject to a terminal disclaimer.
Int. Cl. A61B 1/00 (2006.01)
CPC A61B 1/00057 (2013.01) [A61B 1/00006 (2013.01); A61B 1/000096 (2022.02); A61B 1/00055 (2013.01)] 16 Claims
OG exemplary drawing
 
11. An endoscope system comprising:
a processor for an endoscope; and
an endoscope that is connected to the processor for an endoscope,
wherein the processor for an endoscope includes a controller executing program code to perform operation of:
acquiring, by the controller, an endoscopic image captured using first system information including a value of an intensity of one of three color tones of light emitted by a light source of the endoscope, a value of an aperture of the light source, or a value of a voltage or a current provided to the light source;
calculating, by the controller, parameter on the basis of the endoscopic image acquired by the controller;
discriminating a part of a subject using a first learning model that outputs a discrimination result of discriminating the part of the subject in a case in which the calculated parameter is input;
outputting second system information including the value of the intensity of one of three color tones of light emitted by the light source of the endoscope, the value of the aperture of the light source, or the value of the voltage or the current provided to the light source using a second learning model that outputs the second system information in a case in which the parameter and the discriminated part of the subject are input; and
determining, by the controller, a difference between the second system information output by the second learning model and the first system information.