Network for medical image analysis, decision support system, and related graphical user interface (GUI) applications
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Publication Number
US-10762993-B2
Publication Date
2020-09-01
Expiration Date
Abstract
Described herein is a platform and supported graphical user interface (GUI) decision-making tools for use by medical practitioners and/or their patients, e.g., to aide in the process of making decisions about a course of cancer treatment and/or to track treatment and/or the progress of a disease.
Core Innovation
The invention is a cloud-based network-based decision support system configured with a processor and memory. The system receives and stores a plurality of medical images in a database, where each medical image is associated with a corresponding patient, and upon user request accesses one or more medical images associated with a particular patient for transmission to a user computing device for display. The system automatically analyzes the one or more medical images using a machine learning algorithm and generates a radiologist report for the particular patient according to the one or more medical images for the patient.
In the disclosed whole-body scan use, the plurality of medical images comprise a whole-body scan of a first patient made with a gamma camera following administration to the first patient of an imaging agent comprising technetium 99m methylenediphosphonate (99mTc MDP). The analysis is performed in a cloud-based manner using the stored medical images and the machine learning algorithm to produce patient-specific outputs, including radiologist report generation.
For longitudinal evaluation, the invention provides methods and cloud-based systems that repeatedly receive and store medical images over time to obtain a series of medical images for one or more patients. Using a machine learning algorithm, the system automatically analyzes the series of medical images to determine values of one or more risk indices for each medical image of the series, thereby tracking the determined values over a course of prostate cancer progression and treatment. The determined risk-index values are stored for further processing or caused to be displayed as a graphical representation.
Claims Coverage
The independent claims cover a cloud-based networked decision support workflow that receives and stores medical images, applies machine learning for automated analysis, and generates radiologist reports or tracks prostate-cancer risk indices over time, including a gamma-camera whole-body scan after administration of technetium 99m methylenediphosphonate (99mTc MDP).
Cloud-based networked decision support for receiving, storing, and displaying patient medical images
A cloud-based decision support system receives and stores a plurality of medical images in a database, where each medical image is associated with a corresponding patient, and accesses one or more images associated with a particular patient upon user request for transmission to the user for display on a user computing device.
Machine learning-based automated image analysis and radiologist report generation
The system automatically analyzes the one or more medical images using a machine learning algorithm and generates a radiologist report for the particular patient according to the one or more medical images for the patient.
Whole-body gamma camera imaging with 99mTc MDP included in the image set
The plurality of medical images comprise a whole-body scan of a first patient made with a gamma camera following administration to the first patient of an imaging agent comprising technetium 99m methylenediphosphonate (99mTc MDP).
Longitudinal tracking of prostate cancer risk indices via time-series analysis with machine learning
A method for tracking prostate cancer progression and treatment efficacy over time repeatedly receives and stores, over time, a plurality of medical images for each patient to obtain a series taken over time, automatically analyzes the series using a machine learning algorithm to determine values of one or more risk indices for each medical image of the series, and tracks the determined values over the course of prostate cancer progression and treatment.
Cloud-based system for tracking prostate cancer progression and treatment efficacy using risk indices over time
A cloud-based system repeatedly receives and stores, over time, a plurality of medical images for each patient to obtain a series taken over time, automatically analyzes the series using a machine learning algorithm to determine values of one or more risk indices for each medical image of the series to track the determined values over a course of prostate cancer progression and treatment, and stores the determined values for further processing or causes display of a graphical representation of the determined values.
The claims cover a cloud-based, network-based decision support architecture that stores and retrieves patient medical images, performs machine-learning-based automated analysis, and generates radiologist reports, and further covers repeated time-series acquisition and machine-learning-based determination and tracking of one or more risk indices for prostate cancer progression and treatment efficacy, with whole-body gamma camera imaging after administration of 99mTc MDP.
Stated Advantages
Generates a radiologist report for the particular patient according to the one or more medical images for the patient.
Tracks determined values of one or more risk indices over a course of prostate cancer progression and treatment for the patient.
Provides storage of determined risk index values for further processing and/or causes display of a graphical representation of determined risk index values.
Documented Applications
Decision support for generating radiologist reports based on one or more medical images, including a whole-body scan made with a gamma camera following administration of an imaging agent comprising technetium 99m methylenediphosphonate (99mTc MDP).
Tracking prostate cancer progression and treatment efficacy over time for one or more patients by determining and tracking one or more risk indices across a series of medical images taken over time, with risk-index values stored for further processing or displayed as a graphical representation, including for whole-body gamma camera scans after 99mTc MDP administration.
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