Method, program, and device for diagnosing thyroid dysfunction based on electrocardiography

Abstract

To provide a method for diagnosing a thyroid dysfunction based on an electrocardiography.SOLUTION: The method for diagnosing a thyroid dysfunction based on an electrocardiography includes: a step S100 for acquiring an electrocardiographic data; and a step S110 for estimating a disease rate of a thyroid dysfunction on a measurement target of the electrocardiographic data based on the electrocardiographic data using a pre-learnt neural network model. The neural network model is learnt based on a correlation between the thyroid function and a change in the electrocardiographic performance.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] The present disclosure relates to a method for diagnosing thyroid dysfunction, and more particularly to a method for diagnosing thyroid dysfunction based on an electrocardiogram using a neural network model. [Background technology]

[0002] An electrocardiogram (ECG) is a signal that measures the electrical signals generated in the heart, and can be used to determine whether or not there is a disease by checking for abnormalities in the conduction system from the heart to the electrodes.

[0003] Heartbeats, which are the cause of an electrocardiogram, begin with an impulse that starts in the sinus node in the right atrium, depolarizing the right and left atria, and then after a short delay in the atrioventricular node, activates the ventricles.

[0004] The septum is the first to activate, and the right ventricle, with its thin walls, activates before the left ventricle, which has a thicker wall. The depolarization wave, transmitted to the Purkinje fibers, propagates like a wavefront through the myocardium, from the endocardium to the epicardium, causing ventricular contraction. As electrical impulses are normally conducted through the heart, the heart contracts approximately 60 to 100 times per minute. Each contraction is represented by one heartbeat.

[0005] Such electrocardiograms can be detected using bipolar leads, which record the potential difference between two sites, and unipolar leads, which record the potential at the site where an electrode is attached. Methods for measuring electrocardiograms include standard limb leads, which are bipolar leads, unipolar limb leads, and precordial leads, which are unipolar leads.

[0006] The cardiac electrical activity stages are broadly divided into atrial depolarization, ventricular depolarization, and ventricular repolarization, and each of these stages is reflected by several waveforms: P, Q, R, S, and T waves, as shown in Figure 1.

[0007] When these waves have a standard morphology, the cardiac electrical activity can be considered normal. To determine whether a wave has a standard morphology, it is necessary to check whether characteristics such as the duration of each wave, the interval between waves, the amplitude of each wave, and kurtosis are within the normal range.

[0008] Such electrocardiograms are measured using expensive measuring equipment and are used as an auxiliary tool to measure the health status of patients. Generally, the measuring equipment only displays the measurement results, and diagnosis is entirely the responsibility of the doctor.

[0009] Currently, in order to reduce dependency on doctors, ongoing research is being conducted into the use of artificial intelligence to rapidly and accurately diagnose diseases based on electrocardiograms. In addition, with the development of wearable and lifestyle electrocardiogram measuring devices, it is becoming increasingly possible to diagnose and monitor not only heart diseases but also various other diseases based on electrocardiograms.

[0010] In particular, in the case of thyroid function-related diseases, early detection is difficult because routine checkups are not performed and symptoms are not obvious, but there is an increasing possibility that thyroid dysfunction can be diagnosed early by detecting subtle changes in the electrocardiogram. Summary of the Invention [Problem to be solved by the invention]

[0011] The present disclosure has been devised in response to the above-mentioned background art, and aims to provide a method for diagnosing thyroid dysfunction according to one embodiment of the present disclosure, which uses a neural network model to estimate the probability of thyroid dysfunction in a subject whose electrocardiogram data is measured, based on the electrocardiogram data.

[0012] However, the problems to be solved by the present disclosure are not limited to those mentioned above, and other problems not mentioned will be clearly understood from the description below. [Means for solving the problem]

[0013] In order to achieve the above-mentioned object, one embodiment of the present disclosure provides a method for diagnosing thyroid dysfunction based on an electrocardiogram, executed by a computing device including at least one processor, the method including: acquiring electrocardiogram data; and estimating, based on the electrocardiogram data, a probability of thyroid dysfunction for a subject measured in the electrocardiogram data using a pre-trained neural network model, wherein the neural network model is trained based on a correlation between thyroid function and changes in electrocardiogram characteristics.

[0014] Alternatively, the method may provide that the neural network model includes a first sub-neural network model trained on electrocardiogram data measured on 12 multiple leads.

[0015] Alternatively, a method can be provided in which the neural network model includes a second sub-neural network model trained based on at least one of six limb leads or six precordial leads.

[0016] Alternatively, a method can be provided in which the neural network model includes a third sub-neural network model trained on electrocardiogram data measured in a single lead.

[0017] Alternatively, the neural network model may include a neural network composed of a plurality of residual blocks, and the neural network composed of the residual blocks may provide a method for receiving the electrocardiogram data and outputting the probability of developing overt hyperthyroidism.

[0018] Alternatively, the method can provide that the overt hyperthyroidism is present when the free thyroxine level is higher than a predetermined reference range or the thyroid stimulating hormone level is lower than a predetermined reference range.

[0019] Alternatively, the neural network model may include a neural network corresponding to each of multiple leads of electrocardiogram data, and the outputs of the neural networks may be concatenated together to derive the probability of thyroid dysfunction.

[0020] Alternatively, the correlation between thyroid function and changes in electrocardiogram characteristics can be based on electrocardiogram characteristics including at least one of the frequency of tachycardia, length of the QT interval, direction of deflection of P, R and T waves, or QRS duration.

[0021] Alternatively, a method can be provided in which the probability of developing thyroid dysfunction increases as the frequency of tachycardia increases.

[0022] Alternatively, a method can be provided in which the probability of developing thyroid dysfunction increases as the length of the QT interval increases.

[0023] Alternatively, a method can be provided in which the probability of developing the thyroid dysfunction increases as the deflection directions of the P wave, R wave, and T wave move toward the right.

[0024] Alternatively, a method can be provided in which the probability of developing thyroid dysfunction increases as the QRS duration becomes shorter.

[0025] Alternatively, the step of estimating the probability of thyroid dysfunction for the subject of electrocardiogram data based on the electrocardiogram data using a pre-trained neural network model can include a step of inputting biological data including at least one of age and gender together with the electrocardiogram data into the neural network model, and estimating the probability of thyroid dysfunction for the subject of the electrocardiogram data.

[0026] According to another embodiment of the present disclosure, there may be provided a computer program stored on a computer-readable storage medium, the computer program, when executed by one or more processors, causing the computer program to perform operations for diagnosing thyroid dysfunction based on an electrocardiogram, the operations including: acquiring electrocardiogram data; and estimating, based on the electrocardiogram data, a probability of thyroid dysfunction for a subject measured with the electrocardiogram data using a pre-trained neural network model, the neural network model being trained based on a correlation between thyroid function and changes in electrocardiogram characteristics.

[0027] According to yet another embodiment of the present disclosure, there is provided a computing device for diagnosing thyroid dysfunction based on an electrocardiogram, the device including: a processor including at least one core; and a memory including program code executable by the processor, wherein the processor acquires electrocardiogram data by executing the program code, and estimates a probability of thyroid dysfunction for a subject measured in the electrocardiogram data based on the electrocardiogram data using a pre-trained neural network model, the neural network model being trained based on a correlation between thyroid function and changes in electrocardiogram characteristics. [Effects of the Invention]

[0028] A method for diagnosing thyroid dysfunction according to one embodiment of the present disclosure can provide a method for estimating the probability of thyroid dysfunction for a subject based on electrocardiogram data using a neural network model. [Brief explanation of the drawings]

[0029] [Figure 1] FIG. 1 illustrates an electrocardiogram signal according to the present disclosure.

[0030] [Figure 2] FIG. 1 is a block diagram of a computing device according to one embodiment of the present disclosure.

[0031] [Figure 3] 1 is a flowchart illustrating a method for diagnosing thyroid dysfunction based on an electrocardiogram according to one embodiment of the present disclosure.

[0032] [Figure 4] FIG. 2 illustrates the structure of a neural network model according to an embodiment of the present disclosure.

[0033] [Figure 5] FIG. 1 illustrates a verification research process for a neural network model according to an embodiment of the present disclosure.

[0034] [Figure 6] FIG. 10 illustrates performance test results of a neural network model according to one embodiment of the present disclosure.

[0035] [Figure 7] FIG. 10 illustrates subgroup electrocardiogram analysis results categorized by gender and age according to one embodiment of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0036] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the accompanying drawings so that those skilled in the art can easily implement the present disclosure. The embodiments presented in this disclosure are provided to enable those skilled in the art to use or practice the contents of the present disclosure. Therefore, various modifications to the embodiments of the present disclosure will be apparent to those skilled in the art. That is, the present disclosure may be embodied in various different forms and is not limited to the following embodiments.

[0037] Throughout the specification of the present disclosure, the same or similar reference numerals refer to the same or similar components. In addition, in order to clearly explain the present disclosure, reference numerals of parts that are not relevant to the explanation of the present disclosure may be omitted from the drawings.

[0038] The term "or" as used in this disclosure is intended to mean an inclusive "or" rather than an exclusive "or." That is, unless otherwise specified or otherwise clear from the context in this disclosure, "x uses a or b" should be understood to mean one of the natural inclusive permutations. For example, unless otherwise specified or otherwise clear from the context in this disclosure, "x uses a or b" can be interpreted as either x uses a, x uses b, or x uses both a and b.

[0039] The term "and / or" as used in this disclosure must be understood to indicate and include all possible combinations of one or more of the associated listed concepts.

[0040] The terms "comprises" and / or "comprising" as used in this disclosure should be understood to mean that the specified features and / or components are present. However, the terms "comprises" and / or "comprising" should not be understood to exclude the presence or addition of one or more other features, other components and / or combinations thereof.

[0041] In this disclosure, unless otherwise specified or clear from the context as referring to the singular form, the singular should generally be construed as including "one or more."

[0042] The term "nth (n is a natural number)" used in this disclosure can be understood as an expression used to distinguish components of the present disclosure from one another based on a predetermined criterion, such as functional, structural, or convenience of description. For example, in this disclosure, components that perform different functional roles can be classified as a first component or a second component. However, components that are substantially identical within the technical concept of the present disclosure but must be distinguished for convenience of description can also be classified as a first component or a second component.

[0043] The term "acquire" as used in this disclosure may be understood to mean not only receiving data from an external device or system via a wired or wireless communication network, but also generating data in an on-device form.

[0044] Meanwhile, the terms "module" or "unit" used in this disclosure may be understood to refer to an independent functional unit that processes computing resources, such as a computer-related entity, firmware, software or a portion thereof, hardware or a portion thereof, or a combination of software and hardware. Here, a "module" or "unit" may refer to a unit composed of a single element or a unit expressed as a combination or collection of multiple elements. For example, as a concept of connotation, a "module" or "unit" may refer to a hardware element or a collection of hardware elements of a computing device, an application program that performs a specific software function, a processing procedure implemented by executing software, or a collection of instructions for executing a program. Furthermore, as a broad concept, a "module" or "unit" may refer to a computing device itself that constitutes a system, or an application executed on a computing device. However, the above concepts are merely examples, and the concepts of a "module" or "unit" may be defined in various ways within the scope of what one skilled in the art can understand based on the contents of this disclosure.

[0045] The term "model" as used in this disclosure may be understood as a system implemented using mathematical concepts and language to solve a specific problem, a set of software units for solving a specific problem, or an abstract model of a processing process for solving a specific problem. For example, a neural network "model" may refer to a system implemented as a neural network that has problem-solving capabilities through learning. Here, a neural network may have problem-solving capabilities by optimizing parameters connecting nodes or neurons through learning. A neural network "model" may include a single neural network or a neural network ensemble that combines multiple neural networks.

[0046] As used in this disclosure, "data" may include "image," signals, etc. As used in this disclosure, the term "image" may refer to multidimensional data made up of discrete image elements. In other words, "image" may be understood as a term that refers to a digital representation of an object that can be viewed by the human eye. For example, "image" may refer to multidimensional data made up of elements that correspond to pixels in a two-dimensional image. "Image" may refer to multidimensional data made up of elements that correspond to voxels in a three-dimensional image.

[0047] The term "block" used in this disclosure can be understood as a collection of components classified by various criteria, such as type, function, etc. Therefore, the components classified into one "block" can be variously changed depending on the criteria. For example, a neural network "block" can be understood as a collection of neural networks including at least one neural network. Here, it can be assumed that the neural networks included in a neural network "block" perform a specific operation equally.

[0048] The explanations of the above terms are intended to aid in understanding the present disclosure. Therefore, unless the above terms are explicitly stated as matters that limit the contents of the present disclosure, care should be taken not to use them in a way that limits the technical ideas of the contents of the present disclosure.

[0049] FIG. 2 is a block diagram of a computing device according to one embodiment of the present disclosure.

[0050] The computing device 100 according to an embodiment of the present disclosure may be a hardware device or part of a hardware device that performs comprehensive data processing and calculations, or may be a software-based computing environment connected via a communication network. For example, the computing device 100 may be a server that performs intensive data processing functions and shares resources, or a client that shares resources by interacting with the server. The computing device 100 may also be a cloud system in which multiple servers and clients interact with each other to comprehensively process data. The above description is merely an example of a type of computing device 100, and various types of computing devices 100 may be configured within the scope of what one skilled in the art would understand based on the contents of this disclosure.

[0051] 2, a computing device 100 according to an embodiment of the present disclosure may include a processor 110, a memory 120, and a network unit 130. However, since FIG. 2 is merely an example, the computing device 100 may include other components for implementing a computer environment. Also, the computing device 100 may include only some of the disclosed components.

[0052] The processor 110 according to an embodiment of the present disclosure may be understood as a component including hardware and / or software for performing computing operations. For example, the processor 110 may read a computer program to perform data processing for machine learning. The processor 110 may process operations such as input data processing for machine learning, feature extraction for machine learning, and error calculation based on backpropagation. The processor 110 for performing such data processing may include a central processing unit (CPU), a general-purpose graphics processing unit (GPGPU), a tensor processing unit (TPU), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), etc. The types of processor 110 described above are merely examples, and various types of processor 110 may be configured within the scope of what one skilled in the art would understand based on the present disclosure.

[0053] The processor 110 can train a neural network model for diagnosing thyroid dysfunction based on medical data. For example, the processor 110 can train the neural network model to estimate the presence or absence and progression of hyperthyroidism based on biological data, including information such as gender and age, as well as electrocardiogram data. Specifically, the processor 110 inputs electrocardiogram data and various biological data into the neural network model and trains the neural network model to detect electrocardiogram changes due to the onset of hyperthyroidism. Here, the neural network model can be trained based on the correlation between thyroid function and electrocardiogram changes. The correlation between thyroid function and electrocardiogram changes can be understood as information about the relationship between changes in thyroid function and morphological changes in the electrocardiogram signal. The processor 110 can perform calculations to represent at least one neural network block included in the neural network model during the training process of the neural network model.

[0054] The processor 110 can use the neural network model generated by the above-described learning process to predict the occurrence of thyroid dysfunction based on medical data. The processor 110 can generate inference data indicating the results of estimating the probability of a person developing thyroid dysfunction by inputting electrocardiogram data and biological data, including age and gender information, into the neural network model trained by the above-described process. For example, the processor 110 can input electrocardiogram data into the neural network model that has completed training to predict the occurrence of hyperthyroidism and its progression. The processor 110 can accurately predict the occurrence of thyroid dysfunction by effectively grasping subtle electrocardiogram changes that are difficult for humans to interpret through the neural network model for diagnosing thyroid dysfunction.

[0055] In addition to the above examples, the types of medical data and the output of the neural network model can be configured in a variety of ways within the scope of what would be understood by one skilled in the art based on the contents of this disclosure.

[0056] The memory 120 according to an embodiment of the present disclosure may be understood as a component including hardware and / or software for storing and managing data processed by the computing device 100. That is, the memory 120 may store any type of data generated or determined by the processor 110 and any type of data received by the network unit 130. For example, the memory 120 may include at least one type of storage medium selected from the group consisting of flash memory, hard disk, multimedia card micro, card-type memory, random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, and optical disk. The memory 120 may also include a database system that manages data in a predetermined manner. The types of memory 120 described above are merely examples, and various configurations of the memory 120 are possible within the scope of what would be understood by one skilled in the art based on the present disclosure.

[0057] The memory 120 may structure and organize and manage data, data combinations, and program code executable by the processor 110 required for the processor 110 to perform calculations. For example, the memory 120 may store medical data received via the network unit 130 (described below). The memory 120 may store program code for operating a neural network model to receive medical data and perform learning, program code for operating the neural network model to receive medical data and perform inference according to the intended use of the computing device 100, and processed data generated by executing the program code.

[0058] The network unit 130 according to an embodiment of the present disclosure may be understood as a component that transmits and receives data via any type of known wired or wireless communication system. For example, the network unit 130 may transmit and receive data using a wired or wireless communication system such as a local area network (LAN), wideband code division multiple access (WCDMA), long term evolution (LTE), wireless broadband internet (WiBro), 5G, ultra wide-band, ZigBee, radio frequency (RF) communication, wireless LAN, wireless fidelity (WiFi), near field communication (NFC), or Bluetooth. The above-described communication systems are merely examples, and various wired and wireless communication systems for transmitting and receiving data by the network unit 130 may be applied in addition to the above examples.

[0059] The network unit 130 may receive data necessary for the processor 110 to perform calculations via wired or wireless communication with any system or any client. The network unit 130 may also transmit data generated by calculations by the processor 110 via wired or wireless communication with any system or any client. For example, the network unit 130 may receive medical data via communication with a database in a hospital environment, a cloud server that performs tasks such as standardizing medical data, or a computing device. The network unit 130 may transmit output data of the neural network model, and intermediate data and processed data derived during the calculation process of the processor 110 via communication with the database, server, computing device, or the like.

[0060] FIG. 3 is a flow chart illustrating a method for diagnosing thyroid dysfunction based on an electrocardiogram, according to one embodiment of the present disclosure.

[0061] Referring to FIG. 3, a method for diagnosing thyroid dysfunction based on an electrocardiogram can be performed by a computing device 100 including at least one processor 110, and can first perform a step (S100) of acquiring electrocardiogram data.

[0062] The electrocardiogram data can be directly acquired as measured by an electrocardiogram measuring device, or can be acquired from the electrocardiogram measuring device via network communication.

[0063] Thereafter, a step (S110) of estimating the probability of thyroid dysfunction occurring for the subject of measurement of the electrocardiogram data based on the electrocardiogram data using the pre-trained neural network model can be performed.

[0064] The estimation step (S110) may include a step of inputting biological data including at least one of age and sex together with the electrocardiogram data into the neural network model, and estimating the probability of thyroid dysfunction occurring for the subject of the electrocardiogram data measurement.

[0065] Here, the neural network model may be trained based on the correlation between thyroid function and changes in electrocardiogram characteristics. The neural network model may also be trained based on the correlation between thyroid function and changes in characteristics such as electrocardiogram, gender, and age. Specifically, the neural network model may be trained based on the correlation between the presence or absence of hyperthyroidism, its progression, and changes in electrocardiogram and other characteristics. The neural network model can be used to diagnose various thyroid dysfunctions, including not only hyperthyroidism but also hypothyroidism.

[0066] The neural network model may be trained based on a 12-lead electrocardiogram obtained from electrodes of an electrocardiogram measuring device connected to the human body. As an example, the electrocardiogram may be measured over 12 leads for 10 seconds and stored at 500 points per second. Additionally, the neural network model may be trained based on partial information extracted from only six limb lead electrocardiograms and a single lead (lead I) electrocardiogram out of the 12-lead electrocardiogram.

[0067] Referring to FIG. 4, a diagram illustrating the structure of a neural network model according to one embodiment of the present disclosure.

[0068] 4, a neural network model according to an embodiment of the present disclosure may include a neural network composed of a plurality of residual blocks. The neural network composed of the residual blocks may receive electrocardiogram data and output the probability of developing overt hyperthyroidism.

[0069] Here, overt hyperthyroidism can be diagnosed when the free thyroxine level is higher than a predetermined reference range or the thyroid stimulating hormone level is lower than a predetermined reference range.

[0070] Specifically, the neural network model can have a ResNet neural network structure using six residual blocks. Each residual block can consist of a convolutional neural network (CNN), batch normalization, a rectified linear unit (ReLU) function, and a dropout layer. The convolutional neural network can be one-dimensional, and the filter size can be set to 21. The input length can be halved after passing through three residual blocks out of the six total residual blocks. A different neural network can be applied to each electrocardiogram lead. Average pooling can be applied per channel at the end of the residual block. The outputs of the neural network can be concatenated to derive the probability of thyroid dysfunction.

[0071] The neural network model may include a neural network corresponding to each of a plurality of leads of electrocardiogram data, i.e., the neural network model may include separate neural networks to which the electrocardiograms measured in the separate leads are input.

[0072] For example, the neural network model may include a first sub-neural network model trained based on electrocardiogram data measured using 12 multiple leads. The neural network model may further include a second sub-neural network model trained based on at least one of six limb leads or six precordial leads. The neural network model may further include a third sub-neural network model trained based on electrocardiogram data measured using a single lead. The neural network model may selectively use at least one of the first sub-neural network model, the second sub-neural network model, and the third sub-neural network model depending on the number of leads. Therefore, the neural network model can effectively predict the onset of thyroid dysfunction regardless of the number of leads. When 12-lead electrocardiogram data is input, the neural network model may use all of the first sub-neural network model, the second sub-neural network model, and the third sub-neural network model, and combine the outputs of each sub-model to output the probability of thyroid dysfunction. By using such a combination, the neural network model can improve the accuracy of predicting the onset of thyroid dysfunction.

[0073] The following describes the statistical analysis methods used to validate the neural network model with the above structure. To confirm basic characteristics, continuous variables were presented as means and standard deviations. Verification results were compared using the unpaired student's t-test or the Mann-Whitney u-test. Categorical variables were expressed as percentages, and the chi-square test was used.

[0074] The performance of the neural network model was verified by comparing the model's calculated probability with the presence or absence of hyperthyroidism in internal and external validation datasets. Validation was performed by referring to the area under the receiver operating characteristic curve (AUC). Cutoff points were identified using the Youden J statistic in the training dataset. Sensitivity, specificity, positive predictive value, and negative predictive value were calculated using the cutoff points in the internal and external validation datasets. The 95% confidence intervals for AUC were calculated using Sun & Su's optimization of the DeLong method.

[0075] Below, a validation study of a neural network model according to one embodiment of the present disclosure is described.

[0076] To demonstrate the robustness of the neural network model, sensitivity analyses were performed by creating subgroups based on age and gender: gender was categorized as male and female, and age was categorized as under 40, 40 to under 50, 50 to under 60, 60 to under 70, and 70 or older.

[0077] We hypothesized that subtle changes in electrocardiograms may occur prior to the onset of hyperthyroidism, and that the neural network model could detect these small changes and predict the onset of hyperthyroidism. To confirm this, we performed subgroup analysis. The external validation dataset was extracted from patients who had a normal initial thyroid function test (TFT) and subsequently underwent subsequent thyroid function tests. The time interval between the initial and subsequent thyroid function tests was 4 weeks or longer. Based on the probability of developing hyperthyroidism estimated by the neural network model, study subjects were classified into high-risk and low-risk groups. The cutoff point was determined using the Juden J statistic on the training dataset. The Kaplan-Meier method was used to analyze the 36-month outcomes.

[0078] FIG. 5 is a diagram illustrating a verification research process for a neural network model according to an embodiment of the present disclosure.

[0079] Referring to FIG. 5, the subjects of the validation study of the neural network model according to one embodiment of the present disclosure were 113,215 patients from Hospital A and 33,485 patients from Hospital B. 21 patients from Hospital A and 7 patients from Hospital B, whose clinical information or electrocardiogram data were missing, were excluded. A total of 2,164 hyperthyroid patients were included. 139,521 electrocardiogram data measured on 90,554 patients from Hospital A were used for neural network training. 34,810 electrocardiogram data measured on 518 patients from Hospital A were used for internal validation. 48,684 electrocardiogram data measured on 33,478 patients from Hospital B were used for external validation. The basic characteristics of the neural network model training cohort (Hospital A, n=113,175) and the external validation cohort (Hospital B, n=33,478) are shown in Table 1 below.

[0080] [Table 1]

[0081] In Table 1, the alternative hypothesis for p values marked with a † is that there is a difference between hyperthyroidism and overt hyperthyroidism. The alternative hypothesis for p values marked with a ‡ is that there is a difference between Hospital A (model development and internal validation data group) and Hospital B (external validation group) for each variable. Gender, age, and incidence of hyperthyroidism showed statistically significant differences between hospitals. Hyperthyroid patients had a relatively higher incidence of tachycardia and a longer QT interval. Hyperthyroid patients showed right-sided deflections of the P, R, and T wave axes and a shorter QRS duration.

[0082] In summary, the correlation between thyroid function and changes in electrocardiogram characteristics described above may be based on electrocardiogram characteristics including at least one of the frequency of tachycardia, the length of the QT interval, the direction of deflection of P waves, R waves, and T waves, or QRS duration.

[0083] The probability of thyroid dysfunction estimated by the neural network model may be higher with a higher frequency of tachycardia. The probability of thyroid dysfunction may be higher with a longer QT interval.

[0084] The probability of developing thyroid dysfunction estimated by the neural network model may increase as the P, R, and T wave deviations move to the right.

[0085] The probability of developing thyroid dysfunction estimated by the neural network model may be higher when the QRS duration is shorter.

[0086] FIG. 6 illustrates performance test results of a neural network model according to one embodiment of the present disclosure.

[0087] Referring to Figure 6, in the internal validation and external validation, AUC means the area under the receiver operating characteristic curve of the neural network model, DLM means the neural network model, ECG means the electrocardiogram, NPV means the negative predictive power, PPV means the positive predictive power, SEN means the sensitivity, and SPE means the specificity.

[0088] In internal and external validation, the AUCs of the neural network model using 12-lead ECGs were 0.918 (0.909-0.927) and 0.897 (0.879-0.916), respectively. Sensitivity analysis confirmed the robustness of the neural network model with respect to gender and age. Patients identified by the neural network model as high-risk showed a significant difference in the incidence of hyperthyroidism compared with those identified as low-risk (p<0.01). The performance of the neural network model using 6-lead and single-lead ECGs was also confirmed. As shown in Table 2, in the sensitivity analysis for gender and age, all models performed with AUC values of 0.830 or higher.

[0089] [Table 2]

[0090] FIG. 7 is a diagram illustrating the results of subgroup electrocardiogram analysis categorized by gender and age according to one embodiment of the present disclosure. Referring to FIG. 7, the subgroup analysis was performed using the subsequent thyroid function test results of 6,762 patients who were determined to be normal in the thyroid function test. Of these, 24 patients developed hyperthyroidism. The subgroup analysis subjects were divided into a high-risk group (4,749 patients) and a low-risk group (2,013 patients) based on the probability of developing hyperthyroidism output by the neural network model. It was confirmed that the high-risk group had a significantly higher risk of developing hyperthyroidism than the low-risk group (0.48% vs. 0.05%, p<0.01).

[0091] Meanwhile, thyroid function is closely related to cardiovascular function, affecting not only cardiac function, vascular resistance, and cardiovascular autonomic control, but also the cardiovascular system. In particular, thyroid hormone-mediated changes can enhance cardiac function through enhanced relaxation, leading to increased ventricular contraction (inotropy) and heart rate (chronotropy). Signs and symptoms of thyroid dysfunction can be attributed to the effects of thyroid hormone on the heart and cardiovascular system. Thyroid dysfunction may be associated with increased cardiovascular disease morbidity and mortality. Furthermore, untreated hyperthyroidism was associated with a higher risk of cardiovascular disease than treated hyperthyroidism. In both treated and untreated hyperthyroidism, cardiovascular morbidity increased with periods of decreased thyroid-stimulating hormone levels.

[0092] Therefore, in order to detect and predict such risky thyroid dysfunction early, a thyroid dysfunction diagnosis method according to one embodiment of the present disclosure uses a neural network model to estimate the probability of thyroid dysfunction occurring for a subject whose electrocardiogram data is measured, based on the electrocardiogram data.

[0093] Furthermore, the method for diagnosing thyroid dysfunction according to an embodiment of the present disclosure has the effect of being able to diagnose hyperthyroidism using a neural network model based on information such as electrocardiogram, gender, and age.

[0094] The various embodiments of the present disclosure described above can be combined with additional embodiments and can be modified within the scope that can be understood by those skilled in the art from the above detailed description. The embodiments of the present disclosure are illustrative in all respects and should not be construed as limiting. For example, each component described as a single type can also be implemented in a distributed form, and similarly, components described as distributed can also be implemented in a combined form. Therefore, all modifications and variations derived from the meaning, scope, and equivalent concepts of the claims of the present disclosure should be construed as being within the scope of the present disclosure. [Explanation of symbols]

[0095] 100: Computing equipment 110: Processor 120: Memory 130: Network Department

Claims

1. 1. A method for diagnosing thyroid dysfunction based on an electrocardiogram, the method being performed by a computing device including at least one processor, the method comprising: acquiring electrocardiogram data; and estimating, based on the electrocardiogram data, a probability of onset of thyroid dysfunction for the subject measured in the electrocardiogram data using a pre-trained neural network model, The method, wherein the neural network model is trained based on the correlation between thyroid function and changes in electrocardiogram characteristics.

2. 2. The method of claim 1, wherein the neural network model includes a first sub-neural network model trained based on electrocardiogram data measured in 12 multiple leads.

3. 2. The method of claim 1, wherein the neural network model includes a second sub-neural network model trained based on at least one of six limb leads or six precordial leads.

4. The method of claim 1 , wherein the neural network model includes a third sub-neural network model trained based on electrocardiogram data measured in a single lead.

5. The neural network model includes a neural network composed of a plurality of residual blocks, The method according to claim 1 , wherein the neural network formed by the residual blocks receives the electrocardiogram data and outputs the probability of developing overt hyperthyroidism.

6. 6. The method of claim 5, wherein the overt hyperthyroidism is when the free thyroxine level is higher than a predetermined reference range or the thyroid stimulating hormone level is lower than a predetermined reference range.

7. the neural network model includes a neural network corresponding to each of a plurality of leads of electrocardiogram data; 10. The method of claim 1, wherein the outputs of the neural networks are concatenated together to derive the probability of developing thyroid dysfunction.

8. 2. The method of claim 1, wherein the correlation between thyroid function and changes in electrocardiographic characteristics is based on electrocardiographic characteristics including at least one of frequency of tachycardia, length of QT interval, direction of deflection of P, R and T waves, or QRS duration.

9. The method according to claim 8, wherein the probability of developing the thyroid dysfunction increases as the frequency of the tachycardia increases.

10. The method according to claim 8, wherein the probability of developing the thyroid dysfunction increases as the length of the QT interval increases.

11. The method according to claim 8, wherein the probability of developing the thyroid dysfunction increases as the deflection directions of the P wave, R wave, and T wave move toward the right.

12. The method of claim 8, wherein the probability of developing the thyroid dysfunction increases as the QRS duration decreases.

13. The step of estimating the probability of onset of thyroid dysfunction for the subject of measurement of the electrocardiogram data based on the electrocardiogram data using a pre-trained neural network model includes: a step of inputting biological data including at least one of age and sex together with the electrocardiogram data into the neural network model, and estimating the probability of thyroid dysfunction occurring for the subject of the electrocardiogram data; The method of claim 1 , comprising:

14. A computer program stored on a computer-readable storage medium, comprising: The computer program, when executed by one or more processors, performs operations for diagnosing thyroid dysfunction based on an electrocardiogram, The operation is acquiring electrocardiogram data; and an operation of estimating, based on the electrocardiogram data, a probability of onset of thyroid dysfunction for the subject measured in the electrocardiogram data, using a pre-trained neural network model, The neural network model is trained based on the correlation between thyroid function and changes in electrocardiogram characteristics.

15. 1. A computing device for electrocardiogram-based thyroid dysfunction diagnosis, comprising: a processor including at least one core; a memory containing program code executable by the processor; Including, The processor executes the program code to acquire electrocardiogram data, and estimates, based on the electrocardiogram data, a probability of thyroid dysfunction for the subject to be measured using a pre-trained neural network model; The neural network model is trained based on a correlation between thyroid function and changes in electrocardiogram characteristics.

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