US 11,869,184 B2
Method and device for assisting heart disease diagnosis
Tae Geun Choi, Seoul (KR); Geun Yeong Lee, Seoul (KR); and Hyung Taek Rim, Seoul (KR)
Assigned to Medi Whale Inc., Seoul (KR)
Filed by MEDI WHALE INC., Seoul (KR)
Filed on Jun. 24, 2021, as Appl. No. 17/356,960.
Application 17/356,960 is a continuation of application No. 16/807,686, filed on Mar. 3, 2020, granted, now 11,164,313.
Application 16/807,686 is a continuation of application No. PCT/KR2018/016388, filed on Dec. 20, 2018.
Claims priority of provisional application 62/776,345, filed on Dec. 6, 2018.
Claims priority of provisional application 62/715,729, filed on Aug. 7, 2018.
Claims priority of provisional application 62/694,901, filed on Jul. 6, 2018.
Claims priority of application No. 10-2017-0175865 (KR), filed on Dec. 20, 2017; application No. 10-2018-0157559 (KR), filed on Dec. 7, 2018; application No. 10-2018-0157560 (KR), filed on Dec. 7, 2018; and application No. 10-2018-0157561 (KR), filed on Dec. 7, 2018.
Prior Publication US 2021/0327062 A1, Oct. 21, 2021
Int. Cl. G06T 7/00 (2017.01); A61B 5/02 (2006.01); A61B 5/00 (2006.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01); G06V 10/44 (2022.01); G06V 40/18 (2022.01); G06V 40/14 (2022.01)
CPC G06T 7/0012 (2013.01) [A61B 5/02 (2013.01); A61B 5/4842 (2013.01); A61B 5/7275 (2013.01); G06V 10/454 (2022.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01); G06V 40/18 (2022.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30041 (2013.01); G06T 2207/30048 (2013.01); G06T 2207/30204 (2013.01); G06V 40/14 (2022.01)] 27 Claims
OG exemplary drawing
 
1. A diagnosis assistance method, comprising:
obtaining a retinal image of a retina of a testee;
obtaining renal failure diagnosis assistance information for diagnosing a renal failure of the testee based on the retinal image and a renal failure diagnosis assistance deep neural network model, the renal failure diagnosis assistance deep neural network model storing correlations between retinal images and renal failure diagnosis assistance information and including at least one convolution neural network layer; and
outputting the renal failure diagnosis assistance information of the testee,
wherein the renal failure diagnosis assistance information includes at least one of (i) grade information which includes a grade selected from a plurality of grades indicating an extent of risk of the renal failure, (ii) score information for determining a risk of the renal failure and (iii) risk information which indicates whether the testee is included in a risk group for the renal failure or not,
wherein the outputting the renal failure diagnosis assistant information of the testee, further comprises, outputting instruction information determined based on the renal failure diagnosis assistance information,
wherein the instruction information is determined based on a pre-stored renal failure diagnosis assistant information-instruction information relation, and
wherein the renal failure diagnosis assistant information-instruction information relation includes a possible medical treatment for the testee corresponding to the renal failure diagnosis assistant information.