US 12,482,102 B2
Electronic device for predicting sarcopenia and operation method thereof
Ik Hee Ryu, Seoul (KR); Jin Kuk Kim, Seoul (KR); Tae Keun Yoo, Seoul (KR); and Eok Soo Han, Daejeon (KR)
Assigned to VISUWORKS Inc., Seoul (KR)
Filed by VISUWORKS Inc., Seoul (KR)
Filed on Apr. 26, 2023, as Appl. No. 18/139,679.
Claims priority of application No. 10-2023-0048181 (KR), filed on Apr. 12, 2023.
Prior Publication US 2024/0346656 A1, Oct. 17, 2024
Int. Cl. G06T 7/00 (2017.01); A61B 3/00 (2006.01); G06T 7/73 (2017.01); G16H 50/50 (2018.01)
CPC G06T 7/0014 (2013.01) [A61B 3/0025 (2013.01); G06T 7/74 (2017.01); G16H 50/50 (2018.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30041 (2013.01)] 9 Claims
OG exemplary drawing
 
1. An electronic device for predicting sarcopenia, the electronic device comprising:
a memory; and
a processor connected with the memory and configured to execute instructions included in the memory,
wherein the processor extracts a first result value as output data for a first machine learning model by using a fundus image of a subject as input data for the first machine learning model and determines whether sarcopenia of the subject occurs based on the first result value, and
wherein the first result value includes a value for whether macular degeneration corresponding to the fundus image occurs and a value for whether retinopathy corresponding to the fundus image occurs,
wherein the processor extracts a second result value as output data for a second machine learning model by using an eye image of the subject as input data for the second machine learning model and determines whether the sarcopenia of the subject occurs based on the first result value and the second result value,
wherein the second result value includes a Marginal Reflex Distance 1 (“MRD1”) value of the subject corresponding to the eye image, an upper eyelid edge location change value, an eye closing speed value, and an eye opening speed value, the MRD1 value being a distance where light is reflected from an upper eyelid edge to a cornea of the subject,
wherein the processor extracts a third result value as output data for a third machine learning model by using a slit lamp examination image of the subject as input data for the third machine learning model and determines whether the sarcopenia of the subject occurs based on the first result value, the second result value, and the third result value, and
wherein the third result value includes a value for whether pterygium corresponding to the slit lamp examination image occurs and a value for whether cataract corresponding to the slit lamp examination image occurs.