KR-20260065253-A - Swallowing disorder prediction method with improved accuracy and system thereof

KR20260065253AKR 20260065253 AKR20260065253 AKR 20260065253AKR-20260065253-A

Abstract

The present disclosure relates to a method for determining a swallowing disorder based on a cough sound, comprising acquiring a target spectrogram corresponding to a user's cough sound, inputting the target spectrogram into a swallowing disorder prediction model to acquire a predicted value regarding the presence or absence of a swallowing disorder, and determining whether the user has a swallowing disorder based on the predicted value, wherein the swallowing disorder prediction model is trained by fine-tuning a cough judgment model to receive data in the form of a spectrogram and output a value regarding the presence or absence of a swallowing disorder, and the cough judgment model is trained to receive data in the form of a spectrogram and output a value regarding the presence or absence of a cough.

Inventors

이기욱
정지영
오병모
서한길

Assignees

사운더블헬스코리아 주식회사
서울대학교병원

Dates

Publication Date: 20260508
Application Date: 20241101

Claims (9)

Acquire a target spectrogram corresponding to the user's cough sound; Input the above target spectrogram into a dysphagia prediction model to obtain a predicted value regarding the presence of dysphagia; and Determining whether the user has a swallowing disorder based on the above predicted value; including, The above-mentioned dysphagia prediction model is a model created by fine-tuning a cough judgment model to receive spectrogram-shaped data as input and output a value regarding the presence or absence of dysphagia. The above cough judgment model is a model trained to receive spectrogram-shaped data as input and output a value regarding whether or not a cough has occurred, Method for diagnosing dysphagia based on cough sounds.
In paragraph 1, The above swallowing disorder prediction model is, It takes data in the form of a spectrogram converted from the cough sound of a person with a swallowing disorder as input, and outputs a value indicating the presence of a swallowing disorder, and A model generated by fine-tuning the above cough judgment model to receive data in the form of a spectrogram converted from the cough sound of a person without a swallowing disorder and output a value indicating the absence of a swallowing disorder, Method for diagnosing dysphagia based on cough sounds.
In paragraph 1, The above cough judgment model is, It receives data in the form of a spectrogram converted from a cough sound as input, and outputs a value for the cough, A model trained to take input in the form of spectrograms converted from non-cough sounds and output values for nasal coughs, Method for diagnosing dysphagia based on cough sounds.
In paragraph 1, Acquiring a target spectrogram corresponding to the cough sound of the above user is, Extracting a target cough sound of a predetermined time length from the above cough sound; and Acquiring the target spectrogram corresponding to the extracted target cough sound; comprising Method for diagnosing dysphagia based on cough sounds.
In paragraph 1, The time length of the spectrogram-shaped data used to generate the above-mentioned dysphagia prediction model is identical to the time length of the spectrogram-shaped data used to train the above-mentioned cough judgment model, Method for diagnosing dysphagia based on cough sounds.
In paragraph 1, The above cough judgment model includes a classifier head and an encoder layer, and The above-mentioned dysphagia prediction model is a model generated by fine-tuning the classifier head and encoder layer of the above-mentioned cough judgment model to receive spectrogram-shaped data as input and output a value regarding the presence or absence of dysphagia. Method for diagnosing dysphagia based on cough sounds.
Acquire a target spectrogram corresponding to the user's cough sound; Input the above target spectrogram into a dysphagia prediction model to obtain a predicted value regarding the presence of dysphagia; and Determining whether the user has a swallowing disorder based on the above predicted value; including, The above-mentioned dysphagia prediction model is a model generated by fine-tuning a cough judgment model using spectrogram-shaped data labeled with values regarding the presence or absence of dysphagia as training data, and The above cough judgment model is a model trained using spectrogram-shaped data labeled with values for cough or non-cough as training data, Method for diagnosing dysphagia based on cough sounds.
In Paragraph 7, Among the training data used to generate the above-mentioned dysphagia prediction model, the spectrogram-shaped data converted from the cough sound of a person with dysphagia is labeled as having dysphagia, and the spectrogram-shaped data converted from the cough sound of a person without dysphagia is labeled as not having dysphagia. Method for diagnosing dysphagia based on cough sounds.
In Paragraph 7, Among the training data used for training the above cough judgment model, data in the form of spectrograms converted from cough sounds is labeled as cough, and data in the form of spectrograms converted from non-cough sounds is labeled as non-cough. Method for diagnosing dysphagia based on cough sounds.

Description

Swallowing disorder prediction method with improved accuracy and system thereof The present specification relates to a method and system for predicting dysphagia with improved accuracy, and more specifically, to a method and system for determining whether a cough sound is the cough of a person with dysphagia using a dysphagia prediction model generated by fine-tuning a cough judgment model. Previously, video fluoroscopic swallowing study (VFSS) and/or fiberoptic endoscopic examination of swallowing (FEES) methods were used to diagnose swallowing disorders. Since video fluoroscopic swallowing study requires X-ray imaging and fiberoptic endoscopic examination requires an endoscopy, specialized medical equipment and medical personnel were required to determine whether a swallowing disorder was present. The inventor of the present application attempted to predict whether a swallowing disorder exists based on cough sounds in order to determine this in daily life and/or conveniently. Specifically, the inventor intended to derive feature data from cough sounds and train a swallowing disorder prediction model that predicts the presence or absence of a swallowing disorder by receiving this data as input. However, the swallowing disorder prediction model trained solely on feature data derived from cough sounds and labeled with the presence or absence of a swallowing disorder had limitations in that it did not possess sufficient predictive performance. FIG. 1 is a drawing of a swallowing disorder prediction system according to one embodiment. FIG. 2 is a drawing for explaining feature data according to one embodiment. FIG. 3 is a block diagram showing the configuration of a user terminal according to one embodiment. FIG. 4 is a block diagram showing the configuration of a server according to one embodiment. FIG. 5 is a diagram illustrating a swallowing disorder prediction model according to one embodiment. FIG. 6 is a diagram illustrating a cough judgment model according to one embodiment. Figure 7 is a diagram showing the results of a Mann-Whitney U test analysis of a swallowing disorder prediction model generated by fine-tuning a cough judgment model according to one embodiment and a swallowing disorder prediction model generated without fine-tuning. FIG. 8 is a flowchart illustrating a method for predicting dysphagia according to one embodiment. The embodiments described in this specification are intended to clearly explain the concept of the invention to those skilled in the art to which the invention pertains; therefore, the invention is not limited by the embodiments described in this specification, and the scope of the invention should be interpreted to include modifications or variations that do not depart from the concept of the invention. The terms used in this specification have been selected to be as widely used as possible, taking into account their functions in the present invention; however, they may vary depending on the intent, custom, or emergence of new technologies of those skilled in the art to which the present invention pertains. However, if a specific term is defined and used with an arbitrary meaning, the meaning of that term will be described separately. Accordingly, the terms used in this specification should be interpreted based on their actual meaning and the content throughout this specification, rather than merely their names. Numbers used in the description of this specification (e.g., 1st, 2nd, etc.) are merely identifiers to distinguish one component from another. Furthermore, the suffixes "module" and "part" for components used in the following embodiments are assigned or used interchangeably solely for the ease of drafting the specification, and do not inherently possess distinct meanings or roles. In the following examples, singular expressions include plural expressions unless the context clearly indicates otherwise. In the following embodiments, terms such as "comprising" or "having" mean that the features or components described in the specification are present, and do not preclude the possibility that one or more other features or components may be added. The drawings attached to this specification are intended to facilitate the explanation of the present disclosure, and the shapes depicted in the drawings may be exaggerated as necessary to aid in understanding the present disclosure; therefore, the present disclosure is not limited by the drawings. Where an embodiment can be implemented differently, a specific process sequence may be performed differently from the order described. For example, two processes described consecutively may be performed substantially simultaneously or proceed in the reverse order of the description. In this specification, if it is determined that a specific description of known configurations or functions related to the present invention may obscure the essence of the present invention, such detailed description may be omitted as necessary. Accordin