Abstract: Disclosed is an electronic device for predicting sarcopenia, which includes a memory and a processor connected with the memory to execute instructions included in the memory. The processor extracts a first result value as output data for a first machine learning model by using an eye 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. The first result value includes an 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.

Abstract: A method of predicting the anterior chamber angle of the eye of a surgical candidate for lens implantation according to an embodiment of the present invention includes obtaining input data including a lens size, obtaining a predicted postoperative anterior chamber angle based on a learning model using the input data. The learning model may be trained based on the lens size and the measured postoperative anterior chamber angle of a plurality of patients who have undergone the lens implantation in the past.

Abstract: An electronic device for predicting myopia regression, which includes a memory and a processor connected with the memory to execute instructions included in the memory. The processor collects first target data of a subject and second target data of the subject, extracts a first result value as output data for a first machine learning model by using the first target data as input data for the first machine learning model, and determines whether there is a possibility of myopia regression of the subject based on the first result value.