KR-20260066614-A - System for amyotrophic lateral sclerosis diagnostic aid and method thereof

KR20260066614AKR 20260066614 AKR20260066614 AKR 20260066614AKR-20260066614-A

Abstract

The present invention relates to an amyotrophic lateral sclerosis (ALS) diagnostic assistance system and a method thereof. According to the present invention, an ALS diagnostic assistance system may include: an input unit that receives the value of a preset parameter from the results of an autonomic nervous system function test of a subject; an analysis unit that compares and analyzes the value of the parameter with a reference value of each parameter to classify the certainty of the diagnosis into a preset plurality of stages; and a providing unit that provides an auxiliary indicator for the diagnosis of ALS in a subject based on the analysis results. As such, according to the present invention, parameter values related to the autonomic nervous system are analyzed based on the Valsalva maneuver results and autonomic nervous system function test results to classify the certainty of the diagnosis and provide auxiliary indicators for the diagnosis of amyotrophic lateral sclerosis in subjects.

Inventors

김병조
박진우

Assignees

고려대학교 산학협력단

Dates

Publication Date: 20260512
Application Date: 20250806
Priority Date: 20241104

Claims (8)

Input unit that receives the value of a pre-set parameter from the subject's autonomic nervous system function test result; An analysis unit that compares and analyzes the values of the above parameters with reference values for each parameter to classify the certainty of the diagnosis into multiple pre-set stages; and An amyotrophic lateral sclerosis diagnosis assistance system comprising a providing unit that provides auxiliary indicators for the diagnosis of amyotrophic lateral sclerosis in a subject based on analysis results.
In paragraph 1, The above parameters are, A diagnostic aid system for amyotrophic lateral sclerosis comprising at least one of adrenergic baroreflex sensitivity, Valsalva ratio, heart rate variability, and reduction in diastolic blood pressure during a tilt table test.
In paragraph 2, The above analysis unit is, A diagnostic assistance system for amyotrophic lateral sclerosis that classifies the certainty of the diagnosis of amyotrophic lateral sclerosis for a subject into any one of stages 1 to 4 according to the value of the above parameters.
In paragraph 3, The above analysis unit is, If the value of the above adrenergic pressure reflex sensitivity is below the first threshold, the certainty of the above diagnosis is classified as the first stage, and If the value of the above adrenergic baroreflective sensitivity is greater than the first threshold and less than or equal to the second threshold, the certainty of the above diagnosis is classified into the second stage, and If the value of the above adrenergic baroreflex sensitivity is greater than the second threshold and less than or equal to the third threshold, the certainty of the above diagnosis is classified into the third stage, and A diagnostic assistance system for amyotrophic lateral sclerosis that classifies the certainty of the diagnosis into a fourth stage if the value of the above adrenergic baroreflex sensitivity is greater than a third threshold.
A step in which an input unit receives the value of a pre-set parameter from the subject's autonomic nervous system function test result; A step in which an analysis unit compares and analyzes the value of the above parameter with the reference value of each parameter to classify the certainty of the diagnosis into a plurality of pre-set stages; and A method for assisting in the diagnosis of amyotrophic lateral sclerosis, comprising the step of providing an auxiliary indicator for the diagnosis of amyotrophic lateral sclerosis in a subject based on the analysis results of the providing part.
In paragraph 5, The above parameters are, A diagnostic aid for amyotrophic lateral sclerosis comprising at least one of adrenergic pressure reflex sensitivity, Valsalva ratio, heart rate variability, and reduction in diastolic blood pressure during a tilt table test.
In paragraph 6, The above classification step is, A method for assisting in the diagnosis of amyotrophic lateral sclerosis, which classifies the certainty of the diagnosis of amyotrophic lateral sclerosis in a subject into one of the first to fourth stages according to the value of the above parameters.
In Paragraph 7, The above classification step is, If the value of the above adrenergic pressure reflex sensitivity is below the first threshold, the certainty of the above diagnosis is classified as the first stage, and If the value of the above adrenergic baroreflective sensitivity is greater than the first threshold and less than or equal to the second threshold, the certainty of the above diagnosis is classified into the second stage, and If the value of the above adrenergic baroreflex sensitivity is greater than the second threshold and less than or equal to the third threshold, the certainty of the above diagnosis is classified into the third stage, and A diagnostic aid for amyotrophic lateral sclerosis that classifies the certainty of the diagnosis into a fourth stage if the above difference is greater than a third threshold.

Description

System for amyotrophic lateral sclerosis diagnostic aid and method thereof The invention relates to an amyotrophic lateral sclerosis (ALS) diagnostic assistance system and method, and more specifically, to an ALS diagnostic assistance system and method that classifies the certainty of a diagnosis based on the results of autonomic function tests (AFT) for ALS patients and provides an auxiliary indicator for the diagnosis of ALS. Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease that affects motor neurons in the upper and lower extremities, causing muscle weakness, paralysis, and respiratory failure. Recently, it has been revealed that ALS can also affect the autonomic nervous system (ANS). However, since amyotrophic lateral sclerosis (ALS) causes systemic disorders affecting the cardiovascular system, sweat secretion, salivary glands, tear glands, gastrointestinal tract, pupils, etc., there is a limitation in that it is difficult to clinically apply methods to compare autonomic nervous system function test results between ALS patients and healthy control groups. Among clinically applicable autonomic nervous system tests, the Valsalva maneuver (VM) is a test method that evaluates the autonomic nervous system by measuring changes in blood pressure and heart rate using the act of forcibly exhaling through a closed airway, and can assess sympathetic and parasympathetic nervous system functions. However, there is currently no known method to assist in the diagnosis of amyotrophic lateral sclerosis by utilizing the results of the Valsalva maneuver test. The technology forming the background of the present invention is disclosed in Korean Patent Publication No. 10-2025-0016381 (published on February 3, 2025). Figure 1 is a configuration diagram of an amyotrophic lateral sclerosis diagnostic assistance system according to one embodiment of the present invention. FIG. 2 is a flowchart of a diagnostic assistance method for amyotrophic lateral sclerosis according to another embodiment of the present invention. FIG. 3 is a Consort flowchart of a diagnostic assistance method for amyotrophic lateral sclerosis according to another embodiment of the present invention. FIGS. 4 and FIGS. 5 are drawings illustrating the difference in autonomic nervous system parameters between a patient group and a control group according to another embodiment of the present invention. Figure 6 is a diagram showing the correlation between the average diagnostic score for amyotrophic lateral sclerosis and autonomic nervous system function test parameters according to another embodiment of the present invention. Figure 7 is a diagram showing the association between the average diagnostic score of amyotrophic lateral sclerosis and the duration of the disease according to another embodiment of the present invention. FIG. 8 is a diagram showing the performance of diagnosing amyotrophic lateral sclerosis using adrenergic pressure reflex sensitivity and heart rate variability according to another embodiment of the present invention. Preferred embodiments according to the present invention will be described in detail below with reference to the attached drawings. In this process, the thickness of lines or the size of components shown in the drawings may be exaggerated for clarity and convenience of explanation. Furthermore, the terms described below are defined in consideration of their functions within the present invention, and these may vary depending on the intent or practice of the user or operator. Therefore, the definitions of these terms should be based on the content throughout this specification. In the embodiments described below, the amyotrophic lateral sclerosis diagnostic assistance system (100) is described by specific examples as being performed by a computing device comprising one or more memories or one or more processors capable of performing the following processes. Figure 1 is a configuration diagram of an amyotrophic lateral sclerosis diagnostic assistance system according to one embodiment of the present invention. As illustrated in FIG. 1, the amyotrophic lateral sclerosis diagnostic assistance system (100) may include an input unit (110), an analysis unit (120), and a providing unit (130). First, the input unit (110) can receive the value of a pre-set parameter from the subject's Valsalva maneuver (VM) result and tilt table test result. At this time, the parameter includes at least one of adrenergic baroreflex sensitivity (BRSa), Valsalva ratio (VR), heart rate variability (HRV), and the amount of reduction in diastolic blood pressure during the tilt table test. Next, the analysis unit (120) can analyze the input parameter values by comparing them with reference values for each parameter and classify the certainty of the diagnosis into multiple pre-set stages. At this time, the reference value for each parameter is a value derived through regression analysis. Specifically, the analysis unit (120) c