Abstract: A method of identifying dementia is disclosed that includes causing a user terminal to display an N-th screen including a plurality of objects. The user terminal may further display an N+1-th screen with the objects rearranged at positions on the N+1-th screen which are different from positions of the objects included in the N-th screen when an N-th selection input of selecting any one from among the objects included in the N-th screen is received. When an N+1-th selection input for selecting any one from among the objects included in the N+1-th screen is received, a third task of determining whether an answer of the N+1-th selection input is correct is performed based on whether the object selected from the N+1-th selection input is the same as at least one object selected from at least one previous selection input including the N-th selection input.

Abstract: Disclosed is a method of training the dementia identification model. More particularly, the method may include obtaining test result data through at least one of a first task related to Stroop test, a second task related to a computational power test, and a third task related to a memory test; and labeling the test result data with a score value to train the dementia identification model.

Abstract: Disclosed is a method of identifying dementia using at least one processor of a device according to some embodiments of the present disclosure. More particularly, the method may include performing a first task of causing for a user terminal to display a first screen including a sentence; performing a second task of causing for the user terminal to acquire an image including user’s eyes in association with displaying a moving object instead of the first screen; and performing a third task of causing for the user terminal to acquire a recording file in association with displaying a second screen in which the sentence is hidden, wherein the first task includes a sub-task of causing color of at least one word constituting the sentence included in the first screen to be sequentially changed.

Abstract: Disclosed is a method of training the dementia identification model. More particularly, the method may include obtaining test result data through at least one of a first task related to Stroop test, a second task related to a computational power test, and a third task related to a memory test; and labeling the test result data with a score value to train the dementia identification model.

Abstract: The present invention relates to an electrode sheet cutting apparatus including an upper cutter and a lower cutter configured to apply force to opposite surfaces of an electrode sheet while moving at the same speed, wherein the electrode sheet is cut from one side to the other side, whereby deformation of the electrode sheet is prevented and generation of foreign matter is reduced, and an electrode sheet cutting method using the same.

Abstract: The present invention provides a rolling device for a secondary battery, the rolling device comprising: a jig member provided with a bottom plate and one or more correction frames disposed on a top surface of the bottom plate and into which the secondary battery is inserted; a pressing member provided above the jig member to press the secondary battery inserted into each of the correction frames, wherein the pressing member comprises: a support piece configured to uniformly support an entire top surface of the secondary battery inserted into the correction frame and press the entire top surface of the secondary battery inserted into the correction frame with uniform force by transmitted force; and a pressing piece configured to transmit force so that the support piece presses the entire top surface of the secondary battery inserted into the correction frame with the uniform force.

Abstract: According to an embodiment of the present disclosure, there is provided a method of predicting a brain tissue lesion distribution, the method including: a model learning operation of learning a prediction model for predicting a brain tissue lesion distribution of a subject by using brain image data of a plurality of previous patients; an input obtaining operation of obtaining input data from brain image data of the subject; and an output operation of generating output image data including information on the lesion distribution after recanalization treatment for the subject, by using the prediction model. The prediction model includes a success prediction model that is learned by using data of patients in which recanalization treatment is successful among the plurality of previous patients, and a failure prediction model that is learned by using data of patients in which recanalization treatment fails among the plurality of previous patients.

Abstract: The present invention relates to a disposal system of a mixed waste of a livestock carcass and a contaminated soil from a burial site of a livestock carcass which is killed and then buried due to infectious livestock diseases such as foot-and-mouth disease or avian influenza, and a disposal method using the same.