Guided Stethoscope Audio Measurement for Remote Diagnosis Accuracy
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Solution Overview
Problem
Existing telemedicine solutions do not provide sufficient patient-physician interaction for accurate medical diagnosis, leading to inefficiencies in patient intake, triage, diagnosis, treatment, electronic health record data entry, billing, and patient follow-up, which increases healthcare costs.
Innovation Solution
A computer-assisted medical diagnostic system utilizing a medical kiosk or camera system with deep learning and sensors to enable patients to perform medical measurements, guided by augmented or virtual reality, ensuring accurate data collection and analysis for diagnosis and treatment recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If telemedicine solutions use video or audio conferencing with a doctor, then patient-physician interaction is provided, but the interaction is insufficient for accurate medical diagnosis
Solution Approach 1:
The patent introduces an automated diagnostic system with computer vision and deep learning algorithms as an intermediary between the patient and physician. The system captures images of the patient's condition, automatically analyzes them using neural networks, and provides diagnostic assistance to the physician, thereby enhancing the quality of patient-physician interaction and diagnosis accuracy without requiring direct physical examination
Solution Approach 2:
The patent replaces the mechanical system of direct physical examination with an automated computer vision system. Instead of the physician physically examining the patient, the system uses cameras, image processing, and deep learning algorithms to automatically detect and analyze medical conditions, thereby improving diagnostic reliability while maintaining remote consultation capabilities
2Productivity
If traditional medical examination processes are used, then accurate diagnosis is achieved, but patient intake, triage, diagnosis, treatment, electronic health record data entry, billing, and patient follow-up activity are inefficient
Solution Approach 1:
The patent implements self-service functionality where the automated diagnostic system performs image capture, analysis, and diagnostic recommendations without requiring physician intervention for each step. The system automatically generates diagnostic reports and can update electronic health records, allowing physicians to focus on treatment decisions and patient communication, thereby increasing throughput and reducing time spent on administrative tasks
Solution Approach 2:
The patent performs preliminary diagnostic analysis automatically before the physician reviews the case. The computer vision system pre-processes patient images, detects conditions, and generates initial diagnostic recommendations, so that when the physician reviews the case, the preliminary work is already completed, significantly reducing the time required for each patient encounter
3Reliability
If more medical staff and resources are allocated to improve patient care efficiency, then diagnosis and treatment quality improves, but healthcare costs increase
Solution Approach 1:
The patent replaces expensive human labor and resources with an automated computer vision system. Instead of requiring additional medical staff to perform examinations and data entry, the system uses cameras, processors, and algorithms to automatically capture and analyze patient information, thereby maintaining high diagnostic quality while significantly reducing operational costs
Solution Approach 2:
The patent creates digital copies of patient examinations through automated image capture and analysis. The computer vision system generates digital diagnostic reports and electronic health record entries that replicate the information gathering process without requiring physical presence or additional manual documentation, reducing the need for physical resources and staff time
Data Source
AI summary
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.