ARTERIOVENOUS FISTULA STENOSIS DETECTION SYSTEM AND METHOD THEREOF AND SENSING DEVICE

• US 20190320912A1
• Filed: 04/19/2019
• Published: 10/24/2019
• Est. Priority Date: 04/21/2018
• Status: Active Grant

• Current Assignee: Above Care Inc.
• Original Assignee: Above Care Inc.
• Inventors: Chen, Wei-Ta, Chen, Yung-Hsin
• Granted Patent: US 11,937,901 B2
• Time in Patent Office: Days
• CPC Class Codes: A61B 5/02007 Evaluating blood vessel condition, e.g. elasticity, compliance
• CPC Class Codes: A61B 5/026 Measuring blood flow A61B3/1233, A61B3/1241 take precedence
• CPC Class Codes: A61B 7/00 Instruments for auscultation
• CPC Class Codes: A61B 7/02 Stethoscopes acoustic details thereof G10K11/00
• CPC Class Codes: G16H 10/60 for patient-specific data, e.g. for electronic patient records
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/30 for calculating health indices; for individual health risk assessment
• CPC Class Codes: G16H 50/70 for mining of medical data, e.g. analysing previous cases of other patients

Abstract

An arteriovenous fistula (AVF) stenosis detection system and method thereof and sensing device are provided. The AVF stenosis detection system includes: a sensing device including a microphone; and a server coupled to the sensing device. The sensing device contacts a first location of a patient body, wherein there is a first distance between the first location and a second location of an AVF of the patient body, and the first location is located on an extended path of an artery or a vein corresponding to the AVF. The sensing device receives a frequency spectrum signal through the microphone and transmits the frequency spectrum signal to the server. The server calculates a stenosis percentage of the AVF corresponding to the frequency spectrum signal through a machine learning module and transmits the stenosis percentage to the sensing device.

Claims

1. An arteriovenous fistula stenosis detection system, comprising:

a sensing device, comprising a microphone; and

a server, coupled to the sensing device, wherein

the sensing device contacts a first location of a patient body, wherein there is a first distance between the first location and a second location of an arteriovenous fistula of the patient body, and the first location is located on an extended path of an artery or a vein corresponding to the arteriovenous fistula,

the sensing device receives a frequency spectrum signal through the microphone and transmits the frequency spectrum signal to the server,

the server calculates a stenosis percentage of the arteriovenous fistula corresponding to the frequency spectrum signal through a machine learning module and transmits the stenosis percentage to the sensing device.

2. The arteriovenous fistula stenosis detection system as claimed in claim 1, wherein the machine learning module performs a training operation by using the frequency spectrum signal corresponding to a plurality of different first locations, and calculates the stenosis percentage according to a result of the training operation.

3. The arteriovenous fistula stenosis detection system as claimed in claim 1, wherein the server receives angiography information of the patient body and determines a real stenosis percentage according to the angiography information, and the machine learning module trains a plurality of parameters according to the real stenosis percentage to correct the stenosis percentage.

4. The arteriovenous fistula stenosis detection system as claimed in claim 1, wherein the microphone is coupled to a confined space of the sensing device.

5. The arteriovenous fistula stenosis detection system as claimed in claim 1, wherein the machine learning module calculates the stenosis percentage according to a plurality of parameters, and the parameters comprise at least one of age information, gender information, blood pressure information, left or right hand, patient historic data, and big data of the server.

6. A method for detecting arteriovenous fistula stenosis, comprising:

using a sensing device to contact a first location of a patient body, wherein there is a first distance between the first location and a second location of an arteriovenous fistula of the patient body, and the first location is located on an extended path of an artery or a vein corresponding to the arteriovenous fistula;

using the sensing device to receive a frequency spectrum signal through a microphone and transmitting the frequency spectrum signal to a server; and

using the server to calculate a stenosis percentage of the arteriovenous fistula corresponding to the frequency spectrum signal through a machine learning module and transmitting the stenosis percentage to the sensing device.

7. The method for detecting arteriovenous fistula stenosis as claimed in claim 6, wherein the machine learning module performs a training operation by using the frequency spectrum signal corresponding to a plurality of different first locations, and calculates the stenosis percentage according to a result of the training operation.

8. The method for detecting arteriovenous fistula stenosis as claimed in claim 6, wherein the server receives angiography information of the patient body and determines a real stenosis percentage according to the angiography information, and the machine learning module trains a plurality of parameters according to the real stenosis percentage to correct the stenosis percentage.

9. The method for detecting arteriovenous fistula stenosis as claimed in claim 6, wherein the microphone is coupled to a confined space of the sensing device.

10. The method for detecting arteriovenous fistula stenosis as claimed in claim 6, wherein the machine learning module calculates the stenosis percentage according to a plurality of parameters, and the parameters comprise at least one of age information, gender information, blood pressure information, left or right hand, patient historic data, and big data of the server.

11. A sensing device, coupled to a server, and comprising:

a microphone, wherein

the sensing device contacts a first location of a patient body, wherein there is a first distance between the first location and a second location of an arteriovenous fistula of the patient body, and the first location is located on an extended path of an artery or a vein corresponding to the arteriovenous fistula,

the sensing device receives a frequency spectrum signal through the microphone and transmits the frequency spectrum signal to the server,

the server calculates a stenosis percentage of the arteriovenous fistula corresponding to the frequency spectrum signal through a machine learning module, and transmits the stenosis percentage to the sensing device.