15 results about "Image Study" patented technology

A blockchain-based system and method for secure and auditable sharing of medical image studies between providers, patients, and authorized recipients. The system has modules for verifying identities of trusted healthcare providers as issuers of medical data, managing secure storage of medical images and metadata, minting non-fungible tokens (NFTs) representing patient ownership of studies, authenticating patient consent for data sharing, and controlling access to shared data by authorized parties. Trusted issuer registration authenticates healthcare providers permitted to submit studies by verifying credentials against authoritative sources. Secure data management employs hierarchical deterministic cryptographic wallets and smart contracts to ensure only verified issuers can create new medical data NFTs on the blockchain. Automated processing extracts image files and metadata upon upload for separate secure storage, with metadata references encrypted in the associated NFT. The system generates patient-controlled wallets for managing ownership of their medical NFTs without separate identity verification. Patient consent for sharing data is authenticated through multi-signature wallets requiring patient approval before minting study NFTs. Controlled access for other parties is gated by cryptographic proofs of authorization without exposing data. The immutable blockchain ledger records all sharing transactions initiated through patient and provider wallets, providing transparency and an auditable trail of how and when medical data is shared or accessed and by whom. The decentralized architecture promotes patient control, security, and accountability in managing sensitive healthcare information.

A blockchain-based system and method for secure and auditable sharing of medical image studies between providers, patients, and authorized recipients. The system has modules for verifying identities of trusted healthcare providers as issuers of medical data, managing secure storage of medical images and metadata, minting non-fungible tokens (NFTs) representing patient ownership of studies, authenticating patient consent for data sharing, and controlling access to shared data by authorized parties. Issuer registration authenticates healthcare providers permitted to submit studies by verifying credentials against authoritative sources. Secure data management employs cryptographic wallets and smart contracts to ensure only verified issuers can create new medical data NFTs on the blockchain. Automated processing extracts medical data such as image files and metadata upon upload for separate secure storage, with metadata references encrypted in the associated NFT. The system generates patient-controlled wallets for managing ownership of their medical NFTs without separate identity verification. Patient consent for sharing data is authenticated through multi-signature wallets requiring patient approval before minting study NFTs. Controlled access for other parties is gated by cryptographic proofs of authorization without exposing data. The immutable blockchain ledger records all sharing transactions initiated through patient and provider wallets, providing transparency and an auditable trail of how and when medical data is shared or accessed and by whom. The decentralized architecture promotes patient control, security, and accountability in managing sensitive healthcare information.

A computer-implemented method of compensating for image quality issue in an image study. The method includes acquiring a current image study, measuring, via a processor, a patient positioning parameter indicating a deviation of a patient position in the current image study to an optimal patient position for an image exam type of the current image study and analyzing, via an analysis module, the current image study and the determined patient positioning parameter to determine a relationship between positioning errors and a grading of a diagnostic finding for the current image study, wherein the grading of the diagnostic finding includes one of a severity of the diagnostic finding or a certainty classification of the diagnostic finding.

Disclosed herein are various examples of a workstation that integrates multiple distinct computer systems in a single chassis. A first blade computing device of a plurality of blade computing devices can receive a selection of a medical image study in a user interface. The first blade computing device can determine whether the medical image study is compatible with the first blade computing device. In response to determining that the medical image study is incompatible with the first blade computing device, the first blade computing device can identify a second blade computing device of the plurality of blade computing device with which the medical image study is compatible. The first blade computing device can generate a command to activate the second blade computing device and to access the medical image study and provide the command to the switching device.

The system brings together patient data both clinical records and imaging studies from disparate sources to the user workstation or mobile device in real-time and on-demand. In order to do so, the system needs to establish application layer connectivity utilizing HL7 or FHIR and DICOM for imaging. Once a secure connection is established, the system is able to search and retrieve records and present it to end user.

Systems and methods for selecting a prior comparison study. One system includes an electronic processor configured to, for a medical image study associated with a patient, select a prior comparison image study. The electronic processor is also configured to automatically determine, based on monitored user interaction with the selected prior comparison image study, a usefulness of the selected prior comparison image study. The electronic processor is also configured to automatically update a selection model based on the usefulness of the prior comparison image study to a user.

In an example embodiment, a computing system may receive a selection of a medical image study in a user interface and determine whether the medical image study is accessible by a first computing device based at least in part on a token associated with the medical image study, the first computing device being an active computing device, and the token comprising an indication of a source picture archiving and communications systems (PACS) server from which the medical image study originated. In response to determining that the medical image study is inaccessible by the first computing device, the computing system may identify a second computing device among a plurality of computing devices configurable to access the medical image study and generate an output signal to cause the computing system to activate the second computing device for accessing the medical image study.

In a method for providing a medical video report, context information pertaining to a medical image study is extracted, and a template video script is selected from a plurality of candidate template video scripts using the context information. The candidate template video scripts respectively encode instructions for image processing of a medical image study to generate a video visualizing a medical diagnosis to a human recipient. The selected template video script may then be executed to generate the medical video report.

The invention relates to a method and system for providing medical video reports. In particular, it is proposed to extract contextual information (CI) relating to a medical image research (MIS) and to select (S30) a template video script (TVS) from a plurality of candidate template video scripts (CVS) using the contextual information (CI). Candidate template video scripts (CVS) encode instructions for image processing steps of medical image studies (MIS), respectively, to generate videos for human recipients (HR) that visualize medical diagnostics. The selected template video script (TVS) may then be executed to generate a medical video report (MVR).

Disclosed are various embodiments for equitably assigning medical images for examination. Data describing medical image studies pending examination are obtained from a medical data server. A relative complexity value is determined for each of the medical image studies. The medical image studies are assigned for examination by a respective user based on preferences associated with the respective user and the relative complexity value determined for each medical image study. A user is prevented from viewing a particular medical image study based at least in part on a lock status associated with the particular medical image study.

A blockchain-based system and method for secure and auditable sharing of medical image studies between providers, patients, and authorized recipients. The system has modules for verifying identities of trusted healthcare providers as issuers of medical data, managing secure storage of medical images and metadata, minting non- fungible tokens (NFTs) representing patient ownership of studies, authenticating patient consent for data sharing, and controlling access to shared data by authorized parties. Issuer registration authenticates healthcare providers permitted to submit studies by verifying credentials against authoritative sources. Secure data management employs cryptographic wallets and smart contracts to ensure only verified issuers can create new medical data NFTs on the blockchain. Automated processing extracts medical data such as image files and metadata upon upload for separate secure storage, with metadata references encrypted in the associated NFT.

Disclosed are various embodiments for equitably assigning medical images for examination. Data describing medical image studies pending examination are obtained from a medical data server. A relative complexity value is determined for each of the medical image studies based on an average amount of time to perform a particular type of image study. The medical image studies are assigned for examination by a respective user based on preferences associated with the respective user and the relative complexity value determined for each medical image study. A user interface is rendered by a client device, where the user interface includes a respective user worklist for each user.

The system brings together patient data both clinical records and imaging studies from disparate sources to the user workstation or mobile device in real-time and on-demand. In order to do so, the system needs to establish application layer connectivity utilizing HL7 or FHIR and DICOM for imaging. Once a secure connection is established, the system is able to search and retrieve records and present it to end user.

Methods and systems for rending medical images within a web browser application. The web browser application retrieves a worklist and automatically determines an image study from the worklist to be cached. The web browser application retrieves at least one medical image included in the image study. The web browser application creates a web database for storing the at least one medical image within the browser application. When a user selects a medical image for display within the web browser, the web browser application determines whether the medical image is stored in the web database. When the medical image is stored in the web database, the web browser application retrieves the medical image from the web database. When the medical image is not stored in the web database, the web browser application retrieves the medical image from a remote image repository.

Method and systems for providing a medical video report (MVR) are disclosed. Specifically, it is proposed to extract a context information (CI) pertaining to a medical image study (MIS) and to select (S30) a template video script (TVS) from a plurality of candidate template video scripts (CVS) using the context information (CI). The candidate template video scripts (CVS) respectively encode instructions for image processing steps of a medical image study (MIS) in order to generate a video visualizing a medical diagnosis to a human recipient (HR). Then, the selected template video script (TVS) may be executed so as to generate the medical video report (MVR) .