Interchangeable imaging modules for a medical diagnostics device with integrated artificial intelligence capabilities
Inventors
Moretti, Luke Michael • Digiore, Andrew
Assignees
Publication Number
US-12178392-B2
Publication Date
2024-12-31
Expiration Date
2042-02-23
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Abstract
A medical diagnostic instrument can include a housing with a mounting interface configured to support a plurality of imaging devices, each of the plurality of imaging devices configured to capture image data of a different anatomical region of a patient. The instrument can include an electronic processing circuitry, which can include a memory and a processor. The processor can be configured to, responsive to an attachment of an imaging device of the plurality of imaging devices to the mounting interface, retrieve from the memory and execute at least one machine learning model from a plurality of machine learning models configured to identify, based on image data, one or more diseases of the patient. The at least one machine learning model can be configured to identify one or more diseases of an anatomical region of the patient an image data of which the imaging device is configured to capture.
Core Innovation
The invention relates to a medical diagnostic instrument comprising a housing with a mounting interface configured to support multiple interchangeable imaging devices, where each imaging device is designed to capture image data from a distinct anatomical region of a patient. The instrument incorporates electronic processing circuitry, including a memory and a processor. Upon the attachment of one of the imaging devices, the processor retrieves and executes at least one relevant machine learning model from a plurality of stored models. This enables the system to identify one or more diseases specific to the anatomical region imaged.
The problem addressed is the cumbersome nature and expense of maintaining multiple single-purpose diagnostic devices for examining different parts of the body, such as the retina, ear, or skin. Existing devices are often fixed in functionality and require separate equipment for each anatomical region, complicating clinical workflows and increasing costs for procurement and maintenance.
The core innovation allows a standardized base diagnostic system to seamlessly switch between imaging applications (such as ophthalmology, otology, and dermatology) via attachment of different modules. The base unit detects the type of module attached, automatically loads and executes the corresponding machine learning model for disease analysis, and facilitates anatomy-specific imaging and diagnostics. This modular approach supports efficient, context-aware AI-assisted disease detection with minimal hardware duplication.
Claims Coverage
The independent claims define two inventive features relating to a medical diagnostic instrument with modular imaging capability and a method for its use.
Medical diagnostic instrument with interchangeable imaging devices and dedicated machine learning models
A medical diagnostic instrument includes: - A housing with a mounting interface designed to support multiple imaging devices, each capable of capturing image data from a different anatomical region of a patient. - Electronic processing circuitry comprising memory and a processor, where the processor, upon attachment of an imaging device, retrieves and executes at least one machine learning model from multiple available models. - The selected machine learning model is configured to identify one or more diseases specifically related to the anatomical region imaged by the attached device.
Method of operating a modular medical diagnostic instrument
A method comprising: 1. Detecting the attachment of an imaging device—chosen from a plurality of devices that each image a different anatomical region—to the mounting interface of the instrument housing. 2. Upon detection, retrieving from memory and executing at least one machine learning model (from a plurality of models) that is configured to identify, based on the image data, diseases specific to the anatomical region corresponding to the attached imaging device.
The inventive features are directed to a modular medical diagnostic instrument with context-aware machine learning disease detection based on interchangeable imaging devices and the corresponding method of instrument operation.
Stated Advantages
Makes the process of diagnosis across multiple anatomical regions less complex, easier to perform, and potentially reduces overall cost by using interchangeable imaging modules with a standardized base system.
Enables on-board artificial intelligence analysis, providing real-time analysis and diagnosis of disease without reliance on network connectivity or external computing devices.
Facilitates faster image processing and diagnostics by eliminating intermediary data transfer steps, enhancing workflow efficiency.
Improves diagnostic accuracy, sensitivity, and specificity for diseases by applying anatomical region-specific machine learning models based on the detected imaging module.
Enhances usability, functionality, and security by integrating image capture, AI analysis, user interface, and result storage on the device, supporting use in diverse settings including locations lacking reliable technological infrastructure.
Reduces unnecessary specialist referrals and supports more accurate decision-making for clinical visits.
Documented Applications
Diagnosis and disease detection in the eye, including identification of diseases such as diabetic retinopathy, glaucoma, and cataract via images captured by a retinal imaging module.
Assessment and diagnosis of ear diseases, including wax obstruction, chronic perforations, and acute otitis media using an ear imaging module with AI analysis.
Assessment and diagnosis of skin diseases, including detection of lesions, moles, and melanomas with a skin imaging module and corresponding machine learning model.
Use in various healthcare environments such as physician offices, clinics, emergency departments, hospitals, telemedicine settings, and at home.
Application to other anatomical imaging, including modalities such as X-ray, ultrasound, or MM images for classification and pathology screening in different tissue regions.
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