US 12,136,483 B2
Apparatus and method for processing medical image using predicted metadata
Jong Chan Park, Seoul (KR); Dong Geun Yoo, Seoul (KR); Ki Hyun You, Seoul (KR); Hyeon Seob Nam, Seoul (KR); Hyun Jae Lee, Seoul (KR); and Sang Hyup Lee, Seoul (KR)
Assigned to LUNIT INC., Seoul (KR)
Filed by Lunit Inc., Seoul (KR)
Filed on Apr. 5, 2024, as Appl. No. 18/627,705.
Application 18/627,705 is a continuation of application No. 17/426,336, granted, now 11,978,548, previously published as PCT/KR2020/006712, filed on May 22, 2020.
Claims priority of application No. 10-2019-0059860 (KR), filed on May 22, 2019.
Prior Publication US 2024/0249824 A1, Jul. 25, 2024
Int. Cl. G16H 30/20 (2018.01); G06T 7/00 (2017.01); G06V 30/166 (2022.01); G16H 30/40 (2018.01)
CPC G16H 30/20 (2018.01) [G06T 7/0012 (2013.01); G06V 30/166 (2022.01); G16H 30/40 (2018.01); G06T 2207/30004 (2013.01)] 14 Claims
OG exemplary drawing
 
11. A medical image analysis method executed by one or more processors, the method comprising:
receiving a medical image;
obtaining metadata based on the medical image; and
determining, based on the metadata, whether the medical image is suitable for analysis by a machine learning model configured to detect abnormality,
wherein the determining comprises determining that the medical image is not suitable for the analysis by the machine learning model in response to information related to at least one item included in the metadata not satisfying a predetermined condition, and
wherein the method further comprises obtaining a new medical image of a patient corresponding to the medical image, or performing an operation for obtaining the new medical image of the patient, in response to the information related to at least one item included in the metadata not satisfying the predetermined condition.
 
12. A medical image analysis method executed by one or more processors, the method comprising:
receiving a medical image;
obtaining metadata based on the medical image;
determining a reference value related to determination of the machine learning model configured to detect abnormality based on the metadata;
obtaining result information by applying the medical image to the machine learning model; and
obtaining final result information by comparing the reference value with the result information.