Abstract: Provided are a system and a computer program for managing and predicting thyrotoxicosis using a wearable device. The system for predicting thyrotoxicosis is a system for predicting thyrotoxicosis using a resting heart rate, the system including a wearable device for measuring the heart rate of a patient at regular intervals, and a bio-signal computing device for receiving heart rate information from the wearable device, the bio-signal computing device outputting a warning alarm when a resting heart rate is greater than a reference heart rate when the patient is in a normal state.

Abstract: According to the present application, a computer-implemented method of predicting thyroid eye disease is disclosed. The method comprising: preparing a conjunctival hyperemia prediction model, a conjunctival edema prediction model, a lacrimal edema prediction model, an eyelid redness prediction model, and an eyelid edema prediction model, obtaining a facial image of an object, obtaining a first processed image and a second processed image from the facial image, wherein the first processed image is different from the second processed image, obtaining predicted values for each of a conjunctival hyperemia, a conjunctival edema and a lacrimal edema by applying the first processed image to the conjunctival hyperemia prediction model, the conjunctival edema prediction model, and the lacrimal edema prediction model, and obtaining predicted values for each of an eyelid redness and an eyelid edema by applying the second processed image to the eyelid redness prediction model and the eyelid edema prediction model.

Abstract: Proposed is a method of monitoring diseases of a user on the basis of heart rate information obtained by a wearable device. The method includes obtaining type information of the wearable device and the heart rate information, generating disease monitoring information using the heart rate information through a disease monitoring module when the type information corresponds to a reference type, generating standardized heart rate information using the heart rate information through a heart rate information standardization module and generating the disease monitoring information using the standardized heart rate information through the disease monitoring module when the type information does not correspond to the reference type, and outputting the disease monitoring information.

Abstract: Proposed is an eye-related sign prediction method that includes obtaining an image representing at least one eye, obtaining two or more individual predicted values and one consensus predicted value based on the image and a single diagnostic model, and determining a presence of the eye-related sign in the eye based on the consensus predicted value. The single diagnostic model is trained using training data to output predictions of two or more individual determination values and a prediction of one consensus value in response to an input image. The training data includes a clinical image multi-labeled with two or more individual determination values and one consensus value.

Abstract: According to the present application, a computer-implemented method of predicting thyroid eye disease is disclosed. The method comprising: preparing a conjunctival hyperemia prediction model, a conjunctival edema prediction model, a lacrimal edema prediction model, an eyelid redness prediction model, and an eyelid edema prediction model, obtaining a facial image of an object, obtaining a first processed image and a second processed image from the facial image, wherein the first processed image is different from the second processed image, obtaining predicted values for each of a conjunctival hyperemia, a conjunctival edema and a lacrimal edema by applying the first processed image to the conjunctival hyperemia prediction model, the conjunctival edema prediction model, and the lacrimal edema prediction model, and obtaining predicted values for each of an eyelid redness and an eyelid edema by applying the second processed image to the eyelid redness prediction model and the eyelid edema prediction model.

Abstract: Provided is a diagnostic system including: a user terminal configured to take an image; and a server configured to obtain diagnosis assistance information on the basis of the image. The user terminal is configured to obtain a first photographing parameter, determine whether a pre-stored condition is satisfied, and transmit the first photographing parameter and a captured image to the server when it is determined that the pre-stored condition is satisfied. The server is configured to obtain a first verification parameter, determine whether to use the captured image as a diagnostic image which includes comparing the first verification parameter with the first photographing parameter, and obtain the diagnosis assistance information by using the diagnostic image when it is determined to use the captured image as the diagnostic image.

Abstract: Provided is a diagnostic system including: a user terminal configured to take an image; and a server configured to obtain diagnosis assistance information on the basis of the image. The user terminal is configured to obtain a first photographing parameter, determine whether a pre-stored condition is satisfied, and transmit the first photographing parameter and a captured image to the server when it is determined that the pre-stored condition is satisfied. The server is configured to obtain a first verification parameter, determine whether to use the captured image as a diagnostic image which includes comparing the first verification parameter with the first photographing parameter, and obtain the diagnosis assistance information by using the diagnostic image when it is determined to use the captured image as the diagnostic image.

Abstract: According to an embodiment of the present application, as a method of determining whether to output an alert of a user's thyroid dysfunction, there may be provided a thyroid dysfunction monitoring method including receiving a user's medication information from an external device; selecting a monitoring algorithm to be used to determine whether to output the alert on the basis of the medication information; and determining whether to output the alert on the basis of the selected monitoring algorithm.

Abstract: Provided are a system and a computer program for managing and predicting thyrotoxicosis using a wearable device. The system for predicting thyrotoxicosis is a system for predicting thyrotoxicosis using a resting heart rate, the system including a wearable device for measuring the heart rate of a patient at regular intervals, and a bio-signal computing device for receiving heart rate information from the wearable device, the bio-signal computing device outputting a warning alarm when a resting heart rate is greater than a reference heart rate when the patient is in a normal state.

Abstract: According to the present application, provided is a computer-implemented method of predicting a clinical activity score for conjunctival hyperemia. The method described in the present application includes: training a conjunctival hyperemia prediction model using a training set; acquiring a first image include at least one eye of a subject and an outer region of an outline of the at least one eye; outputting, by the conjunctival hyperemia prediction model executing on a processor, a first predicted value for a conjunctival hyperemia, a first predicted value for the conjunctival edema, a first predicted value for an eyelid redness, a first predicted value for an eyelid edema, and a first predicted value for a lacrimal edema; to and generating a score for the conjunctival hyperemia based on the selected first predicted value for a conjunctival hyperemia.

Abstract: According to the present application, a computer-implemented method of predicting thyroid eye disease is disclosed. The method comprising: preparing a conjunctival hyperemia prediction model, a conjunctival edema prediction model, a lacrimal edema prediction model, an eyelid redness prediction model, and an eyelid edema prediction model, obtaining a facial image of an object, obtaining a first processed image and a second processed image from the facial image, wherein the first processed image is different from the second processed image, obtaining predicted values for each of a conjunctival hyperemia, a conjunctival edema and a lacrimal edema by applying the first processed image to the conjunctival hyperemia prediction model, the conjunctival edema prediction model, and the lacrimal edema prediction model, and obtaining predicted values for each of an eyelid redness and an eyelid edema by applying the second processed image to the eyelid redness prediction model and the eyelid edema prediction model.

Abstract: A method of obtaining user information to analyze a Clinical Activity Score (CAS) of a user is provided. The method includes providing a guidance on a photographing device to indicate a proper position of at least one eye of the user, obtaining a facial image of the user in response to satisfying predetermined conditions. The facial image comprises at least one eye of the user. The predetermined conditions includes 1) whether a position of at least one eye is on a predetermined region, 2) whether a degree of rotation of a face of the user is within a predetermined range, 3) whether a degree of the user's smile is the same or less than a first predetermined value, and 4) whether a degree of ambient brightness is the same or less than a second predetermined value. The method further includes outputting inquiries on the photographing device, and obtaining user input.

Abstract: Provided is a diagnostic system including: a user terminal configured to take an image; and a server configured to obtain diagnosis assistance information on the basis of the image. The user terminal is configured to obtain a first photographing parameter, determine whether a pre-stored condition is satisfied, and transmit the first photographing parameter and a captured image to the server when it is determined that the pre-stored condition is satisfied. The server is configured to obtain a first verification parameter, determine whether to use the captured image as a diagnostic image which includes comparing the first verification parameter with the first photographing parameter, and obtain the diagnosis assistance information by using the diagnostic image when it is determined to use the captured image as the diagnostic image.

Abstract: According to the present application, a computer-implemented method of predicting thyroid eye disease is disclosed. The method comprising: preparing a conjunctival hyperemia prediction model, a conjunctival edema prediction model, a lacrimal edema prediction model, an eyelid redness prediction model, and an eyelid edema prediction model, obtaining a facial image of an object, obtaining a first processed image and a second processed image from the facial image, wherein the first processed image is different from the second processed image, obtaining predicted values for each of a conjunctival hyperemia, a conjunctival edema and a lacrimal edema by applying the first processed image to the conjunctival hyperemia prediction model, the conjunctival edema prediction model, and the lacrimal edema prediction model, and obtaining predicted values for each of an eyelid redness and an eyelid edema by applying the second processed image to the eyelid redness prediction model and the eyelid edema prediction model.

Abstract: According to the present application, provided is a computer-implemented method of predicting a clinical activity score for conjunctival hyperemia. The method described in the present application includes: training a conjunctival hyperemia prediction model using a training set; acquiring a first image include at least one eye of a subject and an outer region of an outline of the at least one eye; outputting, by the conjunctival hyperemia prediction model executing on a processor, a first predicted value for a conjunctival hyperemia, a first predicted value for the conjunctival edema, a first predicted value for an eyelid redness, a first predicted value for an eyelid edema, and a first predicted value for a lacrimal edema; and generating a score for the conjunctival hyperemia based on the selected first predicted value for a conjunctival hyperemia.

Abstract: The present application relates to a method of acquiring a side image for analyzing the degree of ocular proptosis. According to an embodiment, an image acquisition method is provided which is including: acquiring a front image of the subject's face while guidance is given to satisfy a first photographing condition; generating panorama guidance on the basis of position information of a first point and a second point extracted from the front image; providing guidance on movement of a photographing device to acquire a preview image corresponding to the panorama guidance; and acquiring a side image of the subject's face while guidance is given to satisfy a second photographing condition. The first captured image shows iris areas of the subject, and the second captured image shows an outer canthus and a cornea of one of the subject's eyes.

Abstract: According to the present application, a computer-implemented method of predicting thyroid eye disease is disclosed. The method comprising: preparing a conjunctival hyperemia prediction model, a conjunctival edema prediction model, a lacrimal edema prediction model, an eyelid redness prediction model, and an eyelid edema prediction model, obtaining a facial image of an object, obtaining a first processed image and a second processed image from the facial image, wherein the first processed image is different from the second processed image, obtaining predicted values for each of a conjunctival hyperemia, a conjunctival edema and a lacrimal edema by applying the first processed image to the conjunctival hyperemia prediction model, the conjunctival edema prediction model, and the lacrimal edema prediction model, and obtaining predicted values for each of an eyelid redness and an eyelid edema by applying the second processed image to the eyelid redness prediction model and the eyelid edema prediction model.

Abstract: According to the present application, a computer-implemented method of predicting thyroid eye disease is disclosed. The method comprising: preparing a conjunctival hyperemia prediction model, a conjunctival edema prediction model, a lacrimal edema prediction model, an eyelid redness prediction model, and an eyelid edema prediction model, obtaining a facial image of an object, obtaining a first processed image and a second processed image from the facial image, wherein the first processed image is different from the second processed image, obtaining predicted values for each of a conjunctival hyperemia, a conjunctival edema and a lacrimal edema by applying the first processed image to the conjunctival hyperemia prediction model, the conjunctival edema prediction model, and the lacrimal edema prediction model, and obtaining predicted values for each of an eyelid redness and an eyelid edema by applying the second processed image to the eyelid redness prediction model and the eyelid edema prediction model.

Abstract: A method for predicting thyroid dysfunction for a subject is disclosed. The method includes determining a target data based on a trigger signal; obtaining interval heart rates corresponding to the determined target date; obtaining, for the subject, a first pre-processing result for the obtained interval heart rates corresponding to the determined target date; obtaining, for the subject, at least one of concentration of hormone related to a thyroid corresponding to a reference date; obtaining, for the subject, a second pre-processing result for interval heart rates corresponding to the reference date; obtaining a difference of the first pre-processing result with respect to the second pre-processing result; and obtaining a prediction result for thyroid dysfunction obtained based on values including the at least one of concentration of hormone related to a thyroid corresponding to a reference date and the difference.

Abstract: Provided are a system and a computer program for managing and predicting thyrotoxicosis using a wearable device. The system for predicting thyrotoxicosis is a system for predicting thyrotoxicosis using a resting heart rate, the system including a wearable device for measuring the heart rate of a patient at regular intervals, and a bio-signal computing device for receiving heart rate information from the wearable device, the bio-signal computing device outputting a warning alarm when a resting heart rate is greater than a reference heart rate when the patient is in a normal state.