13 results about "Reviewing Radiologist" patented technology

The present invention relates to an AI-based system and method for generating enhanced radiology reports. The system comprises a database for storing multimodal patient data, a natural language processing (NLP) module for extracting clinical information, and a machine learning module for correlating the clinical information with radiology images to identify diagnostic insights. An AI-based report generation module analyzes the images and clinical information to generate a preliminary report, which is refined based on radiologist input. The generated report is then integrated into the patient's electronic health record. The system employs techniques such as multimodal deep learning, active learning, explainable AI, and federated learning to enhance diagnostic accuracy, capture expert feedback, provide transparency, and enable multi-institutional collaboration. The invention aims to improve the accuracy, efficiency, and value of radiology reporting in patient care.

The present disclosure provides methods and systems directed to performing real-time and / or AI-assisted radiology. A method for processing an image of a location of a body of a subject may comprise (a) obtaining the image of the location of a body of the subject; (b) using a trained algorithm to classify the image or a derivative thereof to a category among a plurality of categories, wherein the classifying comprises applying an image processing algorithm; (c) directing the image to a first radiologist for radiological assessment if the image is classified to a first category among the plurality of categories, or (ii) directing the image to a second radiologist for radiological assessment, if the image is classified to a second category among the plurality of categories; and (d) receiving a recommendation from the first or second radiologist to examine the subject based at least in part on the radiological assessment.

Systems and methods are described that implement displaying grouped medical images for review. Current medical image management systems generally use thumbnail image grouping of images that have the same position and side. These groups of thumbnail images can be large, making it difficult for a clinician (e.g., a radiologist) to quickly find a particular view they are interested in reviewing. The disclosed systems and methods provide for user-selectable grouping and ungrouping of thumbnail images in a graphical user interface, which makes it easier for a clinician to quickly find a particular view that they are interested in and enables a more-efficient review of an imaging study.

For autonomous MR scanning of a given medical test, a simplified MR scanner can be used without or with little input or control by a technical expert, e.g., by a physician, radiologist, or person trained in MR scanner operation. The MR scanner autonomously positions, scans, checks quality, analyzes, and / or outputs answers to diagnostic questions with or without MR images. Artificial intelligence-based scan analysis allows continuous or instant changes to the scan configuration to acquire the data desired to answer the diagnostic question. By using a simplified MR scanner, both the positioning of the patient relative to the MR scanner and the localization of the scan by the MR scanner are jointly solved. Sensors can sense the patient at the scan position, with reduced radio frequency requirements allowing a more open bore.

Various methods and systems are provided for reducing an amount of time and resources spent on reviewing digital breast tomosynthesis (DBT) images and corresponding synthetic 2D images. In one example, a method includes receiving a plurality of tomosynthesis data studies, each study comprising a first image set of synthetic two-dimensional (S2D) images, a second image set of slab images, and a third image set of plane images. For each study of the plurality of tomosynthesis data studies, a minimum dataset to be reviewed by a radiologist is determined based on an analysis of the tomosynthesis data by an AI-based system, and the minimum dataset is displayed on a display device rather than the plurality of tomosynthesis data studies.

The invention provides a system and method for quality control in radiology reports. It collects data from reports, medical scan images, and questionnaires within a Safety, Quality, Efficiency, and Productivity (SQEP) framework. Scores for safety, quality, productivity, and efficiency are computed for radiologists and departments using weighted parameters, peer reviews, and audit outcomes. A consolidated SQEP score evaluates overall performance. The system identifies deviations in report quality through trend analysis and correlations, providing corrective feedback and training recommendations to radiologists. By addressing errors proactively, the system enhances diagnostic accuracy, operational efficiency, and compliance with quality standards. This comprehensive approach ensures improved performance and reliability in radiology departments while maintaining high-quality patient care.

Embodiments of systems and methods for dynamic and / or live retrieval and display of relevant clinical outputs to a clinical entity are provided, for a clinical entity processing a case. The systems and methods leverage advanced models (e.g., multimodal models) for retrieval, processing, and rendering of relevant information to the clinical entity, in a manner that improves worklist processing performance. Application areas include systems and tools for radiologists.

Various methods and systems are provided for reducing the amount of time and resources used in reviewing digital breast tomosynthesis (DBT) images and corresponding synthetic 2D images. In one example, a method includes receiving a plurality of tomosynthesis data studies, each studies including synthesizing a first image set of two-dimensional (S2D) images, a second image set of slab images, and a third image set of plane images. For each of the plurality of tomosynthesis data studies, a minimum data set to be reviewed by the radiologist is determined from an analysis of the tomosynthesis data by the AI-based system, and the minimum data set is displayed on the display device instead of the plurality of tomosynthesis data studies.

It would be advantageous with the close quarters in breast procedure rooms to create a Gamma Probe system incorporating multi modalities, sensitivities, and multiple ranges for utilization in breast intervention procedures for the diagnosis and verification for breast cancer. Systems for specimen radiography assist radiologists and surgeons in the detection and classification of abnormal lesions in medical images gleaned from breast biopsies / lumpectomies and a Gamma Probe system with a sensor to detect radioactive, metallic / magnet, or RFID waves to help locate a lump or abnormality which were placed by an Interventional Radiologist. Radio guided surgery (RGS) uses gamma probes to localize radioactive targets. The three common RGS radioisotopes: technetium-99m (99mTc), indium-111 (111In), and iodine-125 (125I). The aspects of the disclosed embodiments pertain to a gamma probe that is able to simultaneously detect 2-3 isotopes automatically without actuating a physical switch.

This invention relates to X-ray imaging. An X-ray apparatus (100) is provided. The X-ray apparatus (100) includes an X-ray generator (10) and an X-ray detector (12). The X-ray generator (10) is configured to generate a multicolor X-ray beam within the frequency range of the object to be imaged. The multicolor X-ray beam is configured to have an energy-dependent angle relative to the X-ray detector (20). The X-ray detector (20) is configured to acquire multiple energy-resolved absorption images of the object, which can be used to generate a stereoscopic absorption image of the object. This novel imaging modality can be considered as an intermediate between pure projection imaging and full 3D modality. Therefore, it can be used in the early stages of the diagnostic process to clarify indications without the need to acquire a full 3D image. The X-ray apparatus can relax the requirement for precise patient positioning for detailed examinations (e.g., lateral ankle) because radiologists can compensate for misalignment after image acquisition.

A system may receive a radiology report including a finding of a radiologist with respect to a region of interest of a subject, and generate, using a first AI model, structured data including a set of predetermined fields and a set of corresponding values extracted from the radiology report. The system may determine, using the first AI model, a medical image dataset of the subject corresponding to the radiology report stored in a medical image database using the structured data. The system may segment and label, using a second AI model, a set of regions of each medical image of the medical image dataset. The system may determine, using the first AI model, a medical image of the medical image dataset that depicts the region of interest of the subject using the structured data, and perform an action based on determining the medical image that depicts the region of interest.

Embodiments of systems and methods for dynamic and / or live retrieval and display of relevant clinical outputs to a clinical entity are provided, for a clinical entity processing a case. The systems and methods leverage advanced models (e.g., multimodal models) for retrieval, processing, and rendering of relevant information to the clinical entity, in a manner that improves worklist processing performance. Application areas include systems and tools for radiologists.

Various systems and methods are provided for automatically generating a radiology report. A medical image of a region of interest of a subject may be received. A structure of the region of interest of the subject may be segmented using a segmentation model and classified using a classification model. The medical imaging including the classified structure may be displayed via a user device of a radiologist. Speech data of the radiologist related to the classified structure may be received from the user device. Text data corresponding to the speech data may be generated using a natural language processing model. A radiology report corresponding to the medical image may be generated in a predetermined format using an artificial intelligence model. The radiology report including the medical image and an annotation including the generated text data in relation to the classified structure may be displayed via the user device of the radiologist.