Portable medical diagnostics device with integrated artificial intelligence capabilities
Inventors
Moretti, Luke Michael • Digiore, Andrew
Assignees
Publication Number
US-11950847-B1
Publication Date
2024-04-09
Expiration Date
2041-03-24
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Abstract
A handheld, portable devices with integrated artificial intelligence (AI) configured to assess a patient's body part to detect a disease and methods of operating such devices are disclosed. In some cases, a device can be a retina camera configured to assess a patient's retina and, by using an on-board AI retinal disease detection system, provide real-time analysis and diagnosis of the patient's retina. Easy and comfortable visualization of the patient's retina can be facilitated using such retina camera, which can be placed over the patient's eye, display the retina image on a high-resolution display, analyze a captured image by the on-board AI system, and provide determination of presence of a disease.
Core Innovation
The invention describes a handheld, portable medical diagnostics device with integrated artificial intelligence (AI) capabilities designed to assess a patient's body part—such as an eye, ear, or skin—to detect disease. The device, exemplified as a retina camera, includes on-board AI for real-time analysis and diagnosis, facilitating easy and comfortable visualization by displaying the captured image on a high-resolution display and performing autonomous disease detection without reliance on external connectivity.
The problem addressed is the need for accurate and efficient medical diagnostics, particularly for retinal diseases, using portable devices that do not require external computer analysis or cloud-based connectivity. Existing fundus cameras may lack portability, displays, or on-board AI, necessitate another device for viewing data, or depend on network connectivity and human expertise for analysis, resulting in workflow interruptions, increased training requirements, and potential privacy concerns.
The core innovation lies in a retina camera or analogous portable diagnostic device that performs image acquisition, AI-powered real-time image analysis, and on-device disease diagnosis using optimized machine learning models. The system incorporates miniaturized, power- and memory-efficient AI implementations for on-board disease detection across a range of diseases, outputs immediate diagnostic results on the device display, and is operable in locations with limited or no network connectivity.
Claims Coverage
The patent includes three independent claims, addressing critical inventive features for a portable diagnostic device with on-board AI capabilities.
Retina camera with on-board AI disease detection and diagnosis
A retina camera includes: - A housing with a body and handle configured for handheld use. - An integrated display at least partially supported by the housing. - A light source to irradiate a patient's eye. - Imaging optics to receive light reflected by the eye. - An image detector array to sense the reflected light and capture images. - Electronic processing circuitry that: - Generates image(s) from the detector array signals. - Displays the image(s) on the display. - Processes the image(s) using a machine learning model, trained to detect a plurality of diseases. - Provides a determination of the presence of any detected disease on the display. - The entire analysis and determination occur on the device, without requiring network connectivity.
Portable medical diagnostics instrument for body part disease detection using machine learning
A portable instrument comprises: - A housing with a body and handle for handheld operation. - An integrated display. - A light source to irradiate a body part (such as eye, ear, or skin). - Imaging optics to receive reflected light from the body part. - An image detector array to sense and capture images. - Electronic processing circuitry that: - Generates at least one image from signals by the image detector array. - Displays the image on the display. - Processes the image with a machine learning model to determine the presence of one or more diseases. - Displays a determination regarding disease presence on the device.
Method of diagnosing a patient using a portable device with onboard machine learning
A method comprises: 1. By electronic processing circuitry of the portable medical diagnostics instrument: - Generating at least one image of a body part using signals from an image detector array that senses light reflected after irradiation. - Displaying the image(s) on a display. - Processing the image(s) with a machine learning model to detect at least one disease from a plurality of diseases. - Providing a determination of disease presence on the display.
The claims establish coverage for a class of portable diagnostic devices and methods enabling autonomous, on-device disease detection and reporting through AI-powered image analysis of patient body parts, with specific implementation for a retina camera and generalization to other anatomical targets.
Stated Advantages
Enables real-time, on-device analysis and diagnosis without network connectivity, avoiding workflow interruptions.
Provides improved accuracy compared to devices relying solely on human analysis.
Enhances usability through intuitive operation and elimination of data transfer to external devices for analysis.
Supports use in locations with unreliable or lacking network infrastructure.
Improves data security and patient privacy, as data remains on the device.
Facilitates easy and comfortable retinal assessment with high-quality imaging.
Potentially improves diagnostic sensitivity and specificity.
Can be used in varied settings including hospitals, clinics, emergency departments, and telemedicine.
May reduce unnecessary specialist visits by enabling effective triage and earlier decisions.
Documented Applications
Retina camera for real-time assessment and diagnosis of retinal diseases.
Otoscope for analysis and potential immediate diagnosis of ear diseases.
Dermatology scope for assessing and diagnosing skin diseases.
Portable diagnostics in physician offices, clinics, emergency departments, hospitals, and telemedicine settings.
Use in environments with limited or no network connectivity for medical assessment and diagnosis.
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