Abstract: 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.

Abstract: The present invention relates to a method for reconstructing an image and an apparatus using the same Particularly, according to the method of the present invention, when a series of first slice images of a subject are inputted in a computing device, the computing device generates, from the first slice images, second slice images having a second slice thickness different from a first thickness, which is the slice thickness of the first slice image, and provides the generated second slice images.

Abstract: The present invention relates to a method for supporting reading of a fundus image of a subject, and a computing device using the same. Specifically, the computing device according to the present invention acquires the fundus image of the subject, extracts attribute information from the fundus image on the basis of a machine learning model for extracting the attribute information of the fundus image, and provides the extracted attribute information to an external entity. In addition, when evaluation information on the extracted attribute information or modification information on the attribute information is acquired, the computing device according to the present invention can also update the machine learning model on the basis of the acquired evaluation information or modification information.

Abstract: A computing apparatus for aiding visualization of lesions in a medical image includes a communicator and a processor. The processor is configured to receive a user input for selecting a single point in the medical image to modify a lesion mask representing a lesion area in them medical image, determine a modified lesion mask corresponding to the received user input among a plurality of pre-generated plurality of candidate lesion masks, and provide the modified lesion mask with the medical image.

Abstract: The present invention relates to a method for generating a prediction result for predicting an occurrence of fatal symptoms of a subject in advance, a method for performing data classification by using data augmentation in mechanical learning for the same, and a computing device using the same. Particularly, the computing device according to the present invention acquires vital signs of the subject, converts the same into individuated data, generates analysis information from the individuated data on the basis of a machine learning model, generates a prediction result by referring to the analysis information, and provides the prediction result to an external entity.

Abstract: An image providing method performed by a computing apparatus includes acquiring a first image group including at least a portion of a series of images generated for continuous volumes with a first slice thickness belonging to a subject, providing, as a current viewing image, one image of the first image group or one image of a second image group including images generated for continuous volumes with a second slice thickness belonging to the subject, and in response to a first specific input of an input device, repeatedly updating an image provided as the current viewing image with an individual image provided for a subsequent viewing based on a directivity given for the first specific input and, in response to a second specific input of the input device, switching the current viewing image between an image of the first image group and an image of the second image group.

Abstract: Provided are a method for determining whether an examinee is infected by microorganism, and a determination apparatus using the same. Specifically, the determination apparatus according to the present invention obtains an microphotographed image of a biological sample of the examinee; receives the obtained microphotographed image and generates analysis information on the microorganism based on a deep learning model of the examinee; visualize the generated analysis information to provide it, so as to perform at least one of (i) a process of supporting a remote reading on whether the microorganism corresponding to the analysis information exists or not, and (ii) a process of supporting a user of the computing apparatus to read whether the microorganism corresponding to the analysis information exists or not; and provides a final result as its result.

Abstract: The present disclosure relates to a method for improving reproduction performance of a deep neural network model trained using a group of learning data so that the deep neural network model can exhibit excellent reproduction performance even for target data having a quality pattern different from that of the group, and a device using same. According to the method of the present disclosure, a computing device acquires the target data, retrieves at least one piece of candidate data having a highest similarity to the target data from a learning data representative group including reference data selected from the learning data, performs adaptive pattern transformation on the target data to enable adaptation to the candidate data, and supports transfer of transformed data, which is a result of the adaptive pattern transformation, to the deep neural network model so as to acquire an output value from the deep neural network model.

Abstract: A deep neural network pre-training method for classifying electrocardiogram (ECG) data and a device for the same are disclosed. A method for training an ECG feature extraction model may include receiving a ECG signal, extracting one or more first features related to the ECG signal by inputting the ECG signal to a rule-based feature extractor or a neural network model, extracting at least one second feature corresponding to the at least one first feature by inputting the ECG signal to an encoder, and pre-training the ECG feature extraction model by inputting the at least one second feature into at least one of a regression function and a classification function to calculate at least one output value. The pre-training of the ECG feature extraction model may include training the encoder to minimize a loss function that is determined based on the at least one output value and the at least one first feature.

Abstract: A method of predicting a medical event based on a pre-trained artificial neural network by a computing apparatus, and an apparatus therefor are disclosed. The method includes receiving an electronic medical record vector including a plurality of vital sign components, and outputting the medical event corresponding to the electronic medical record vector using the acritical neural network. The artificial neural network is pre-trained based on learning data, and the learning data includes augmentation electronic medical record vectors which are reconstructed using original electronic medical record vectors pre-acquired at an earlier time point than a first time point based on a mask vector for losing at least one of the plurality of vital sign components of the first time point.

Abstract: The present disclosure provides a method for assessing the degree of risk of a subject and classifying the subject according to the degree of risk, and a computing device using same. Specifically, by a method according to the present invention, a computer device: obtains integrated data of the subject, wherein the integrated data, which is patient data relating to the subject or data obtained by processing the patient data, is numerical data; then applies the integrated data to a machine learning model for assessing the degree of risk of the subject to produce a result obtained by the classification, as a result obtained by assessing the degree of risk; and provides the produced classification result to an external entity.

Abstract: A method for a computing device to predict an action mechanism of a drug from medical images of a subject is disclosed. The method includes, from a plurality of medical images obtained in time series, outputting first compressed data corresponding to the plurality of medical images, each of the first compressed data having a smaller size than a corresponding medical image, estimating second compressed data corresponding to a medical image at a next time point to time points at which the plurality of medical images have been captured, based on the first compressed data, and predicting the action mechanism of the drug for the subject by inputting the second compressed data to a neural network predicting the action mechanism of the drug.

Abstract: According to one embodiment, a method for increasing the reading efficiency of a medical image is provided. The method of increasing the reading efficiency of a medical image comprises of: receiving the gaze information of a user, acquiring a gaze tracking device, during a medical image reading process; determining a region of interest of the user with respect to the medical image by using the gaze information; determining a type of service corresponding to the region of interest; and providing the determined service.

Abstract: A content-based medical image retrieval method and a retrieval system using the same include: obtaining m (2?m?n) number of unit images from a three-dimensional (3D) medical image including n (n?2) number of unit images and extracting features per unit image from each of the m (2?m?n) number of unit images through a feature extraction unit, wherein the 3D medical image is voxel data including a plurality of slices and each of the plurality of slices is defined as a unit image; inputting features of each unit image extracted from the m (2?m?n) number of unit images to a recurrent neural network to generate an output value; and performing medical image retrieval using the output value through an input processing unit, wherein a plurality of 3D medical images to be compared with the output value include a 3D medical image having p (p?2, p?n) number of unit images.

Abstract: According to an embodiment, a method of providing a medical information service is provided. The method for providing a medical information service comprises the steps of: receiving a target image; extracting feature data of the target image; discovering a relative position of the feature data in a disease classification map in which a pre-trained reference image has been quantified; and providing a user with the disease classification map in which the relative position of the feature data has been discovered.

Abstract: Provided are a method and program for computing a bone age using a deep neural network. The method for computing a bone age using a deep neural network, including: receiving an analysis target image that is a specific medical image to compute the bone age; and analyzing the analysis target image by at least one computer using the deep neural network to compute the bone age. According to the present disclosure, since the bone age is computed by accumulating medical images of a specific race (particularly, Korean) and analyzing the same, it is possible to compute an accurate bone age that conforms to race.