US20250000374
2025-01-02
Human necessities
A61B5/02055
The handheld medical diagnostic device integrates a wide array of sensor modules, including a high-magnification camera, otoscope camera, stethoscope, infrared thermometer, EKG, pulse oximeter, body composition monitor, glucometer, and hematology analyzer. It utilizes a system-on-chip (SoC) processor for efficient data management and supports both edge computing and cloud-based AI processing while ensuring data privacy. The device is designed with ergonomic considerations and features a display with a force-sensitive layer for user interaction. It also includes a wireless transceiver for seamless data communication between sensors and cloud platforms.
The high-magnification camera module provides up to 300× magnification for detailed visualization of blood cells and skin, equipped with an annular LED array for illumination. The otoscope camera is designed with a narrow profile suitable for ear, nose, and throat imaging. Advanced security features include an encryption engine and physically unclonable function (PUF) circuit to protect sensitive medical data. The device addresses the limitations of existing portable medical devices by integrating multiple diagnostic functions without compromising accuracy or portability.
Current medical diagnostic devices are typically large and lack portability, requiring significant expertise to operate. Portable devices often focus on single functions due to challenges in maintaining accuracy and reliability across multiple sensors. Additionally, these devices face power management issues, limiting battery life and usability. Processing capabilities are often insufficient for synchronizing data from multiple sensors or running complex analysis algorithms, leading to latency and errors in diagnostic results.
Traditional approaches to integrating AI in medical devices rely on either on-device processing or cloud computing, each with significant trade-offs. On-device processing limits AI complexity due to restricted computational resources, while cloud computing introduces latency and privacy concerns. The new device combines both methods to optimize AI performance while preserving user privacy. This approach addresses the need for real-time personalized insights without compromising data security.
The device aims to provide comprehensive health assessments by integrating various sensor readings into a cohesive analysis framework. Unlike existing health tracking devices that operate in isolation, this device can analyze multi-factorial sensor data in real-time and generate personalized health insights. It overcomes the limitations of current devices by offering interoperability with other data sources such as medical records and past lab results, enhancing the accuracy of health assessments.