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案件記録

SYSTEMS AND METHODS FOR AUDIO MEDICAL INSTRUMENT PATIENT MEASUREMENTS

発明審査中
1閲覧数
7請求項 · 1 独立
§ Ⅰ

案件概要

発明者

James Stewart Bates; Kristopher Perry; Chaitanya Prakash Potaraju; Pranav Bhatkal; Aditya Shirvalkar; Tristan Royal

IPC分類

G16H 50/20A61B 5/A61B 5/205A61B 5/24A61B 5/316A61B 7/2G16H 50/30

CPC分類

G16H50/20A61B5/205A61B5/316A61B7/2A61B5/33A61B5/24A61B5/6897G16H50/30

Presented are systems and methods for the accurate acquisition of medical measurement data of a body part of patient. To assist in acquiring accurate medical measurement data, an automated diagnostic and treatment system provides instructions to the patient to allow the patient to precisely position a medical instrument in proximity to a target spot of a body part of patient. For a stethoscope examination, the steps may include utilizing object tracking to determine if the patient has moved the stethoscope to a recording site; utilizing DSP processing to confirm that the stethoscope is in operation, utilizing DSP processing to generate a pre-processed audio sample from a recorded audio signal; using machine learning (ML) to determine if a signal of interest (SOI) is present in the pre-processed sample. If SoI is present, using ML to evaluate characteristics in the signal which indicate the presence of abnormalities in the organ being measured.

原文(中国語)

Presented are systems and methods for the accurate acquisition of medical measurement data of a body part of patient. To assist in acquiring accurate medical measurement data, an automated diagnostic and treatment system provides instructions to the patient to allow the patient to precisely position a medical instrument in proximity to a target spot of a body part of patient. For a stethoscope examination, the steps may include utilizing object tracking to determine if the patient has moved the stethoscope to a recording site; utilizing DSP processing to confirm that the stethoscope is in operation, utilizing DSP processing to generate a pre-processed audio sample from a recorded audio signal; using machine learning (ML) to determine if a signal of interest (SOI) is present in the pre-processed sample. If SoI is present, using ML to evaluate characteristics in the signal which indicate the presence of abnormalities in the organ being measured.

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