VUNO Inc.

Disclosed is a method for analyzing a thickness of a cortical region, performed by a computing device. The method may include: extracting a plurality of interfaces included in a cortical region based on a mask generated from a medical image including at least one brain region; and estimating a thickness of the cortical region based on the plurality of interfaces.

Disclosed is a portable electrocardiogram measuring device for calculating one or more electrocardiogram leads according to an embodiment of the present disclosure. The device may include: a main measurement unit comprising a first electrode, a second electrode, and one or more processors; and a sub measurement unit comprising a third electrode, in which the one or more processors measure an electrocardiogram, by receiving electrical signals from at least two electrodes in a measurable state and by calculating different types of electrocardiogram leads based on the number of electrodes in the measurable state and an attachment position of electrodes.

According to an embodiment of the present disclosure, a computer program stored in a computer readable storage medium is disclosed. The computer program includes instructions for causing one or more processors to estimate bone age from a bone image, and the instructions include: estimating a RUS score for each of one or more partial bone images using a partial bone RUS score estimation model comprising one or more layers, and wherein the one or more partial bone images are generated from a whole bone image; and estimating bone age corresponding to the whole bone image using one or more RUS scores estimated for each of the one or more partial bone images, in which the partial bone RUS score estimation model is trained by using a labeled partial bone image as training data, and is trained by adjusting feature values calculated from the one or more layers.

According to an exemplary embodiment of the present disclosure, a medical image processing method performed by a computing device is disclosed. The medical image processing method includes: detecting a region of interest in a medical image by using a pre-trained deep learning model; determining contour information for the region of interest; and generating, based on the contour information, format information defining elements that determine representation of the medical image.

According to an embodiment of the present disclosure, a method of assessing bone age by using a neural network performed by a computing device is disclosed. The method includes receiving an analysis image which is a target of bone age assessment; and assessing bone age of the target by inputting the analysis image into a bone age analysis model comprising one or more neural networks. The bone age analysis model, which is trained by supervised learning based on an attention guide label, includes at least one attention module for intensively analyzing a main region of the analysis image.

The present invention relates to a method for classifying a fundus image and a device using same. Specifically, according to the method of the present invention, a computing device acquires a fundus image of a subject, generates classification information of the fundus image, generates an interpretation text on the basis of the classification information, and provides the interpretation text to an external entity.

Disclosed is a bio-signal analysis method performed by computing device according to an embodiment of the present disclosure. The method may include: acquiring at least one lead-wise bio-signal from a plurality of leads: and deriving an analysis value by inputting the at least one acquired lead-wise bio-signal into a neural network model.

Disclosed is a method for generating a prediction result by using static data and dynamic data according to an exemplary embodiment of the present disclosure. Specifically, according to the present disclosure, a computing device generates an integrated feature vector from static data and dynamic data of input data by using an artificial neural network model. The computing device generates a dynamic feature vector from the dynamic data of the input data by using the artificial neural network model. The computing device generates a final prediction result of the artificial neural network model based on the integrated feature vector and the dynamic feature vector.

Disclosed is a portable electrocardiogram measuring device for calculating one or more electrocardiogram leads according to an embodiment of the present disclosure. The device may include: a main measurement unit comprising a first electrode, a second electrode, and one or more processors; and a sub measurement unit comprising a third electrode, in which the one or more processors measure an electrocardiogram, by receiving electrical signals from at least two electrodes in a measurable state and by calculating different types of electrocardiogram leads based on the number of electrodes in the measurable state and an attachment position of electrodes.