41 results about "Reviewing Radiologist" patented technology

An artificial intelligence engine for directed hypothesis generation and ranking uses multiple heterogeneous knowledge graphs integrating disease-specific multi-omic data specific to a patient or cohort of patients. The engine also uses a knowledge graph representation of ‘what the world knows’ in the relevant bio-medical subspace. The engine applies a hypothesis generation module, a semantic search analysis component to allow fast acquiring and construction of cohorts, as well as aggregating, summarizing, visualizing and returning ranked multi-omic alterations in terms of clinical actionability and degree of surprise for individual samples and cohorts. The engine also applies a moderator module that ranks and filters hypotheses, where the most promising hypothesis can be presented to domain experts (e.g., physicians, oncologists, pathologists, radiologists and researchers) for feedback. The engine also uses a continuous integration module that iteratively refines and updates entities and relationships and their representations to yield higher quality of hypothesis generation over time.

The invention provides an osteoporosis automatic diagnosis method and system based on position specific attention, and the method comprises the steps: carrying out the dual-channel enhancement preprocessing of a lumbar CT image, and generating an enhanced image taking the bone mineral density and the bone trabecula structure characteristics into consideration; inputting the enhanced image into a deep convolutional neural network for multi-scale feature extraction to obtain a feature map containing spatial semantic information; on the basis of the anatomical position index information, generating attention weighted features which highlight the key diagnosis area of the vertebral body; global pooling is carried out on the attention weighted features, and then comprehensive diagnosis features containing anatomical priori knowledge are constructed; and inputting the comprehensive diagnosis features into a classifier to realize centrum-level accurate diagnosis. According to the invention, the workload of radiologists can be effectively relieved, and the efficiency and coverage of osteoporosis screening can be improved. Especially under the condition that primary medical institutions are lack of experienced radiologists, the primary medical service level can be improved.

Medical report data (e.g., a paragraph of text that is dictated by a radiologist as they review a medical image) is automatically parsed and associated with an appropriate section (e.g., Lungs, Mediastinum, Heart, Bones, etc.) of a medical report. Each report section of a template report may be associated with character strings that are used to associate portions of the medical report data with a corresponding report section. Natural Language Processing may then be applied to each report section (and any associated text from the medical report data) to determine whether the section includes a “finding” or “no finding.” The medical report may then be used to train an AI model to emulate the analyzed medical report data.

A system, an image analyzer and a method for diagnosing a presence of a disease or a condition in an image of a subject, for example, a veterinary patient, are provided including: classifying the image to a body region, and obtaining a classified, labeled, cropped, and oriented sub-image; directing the sub-image to artificial intelligence processor for obtaining an evaluation result, and comparing the evaluation result to a database library of evaluation results and matched written templates or a dataset cluster to obtain at least one cluster result; measuring the distance between the cluster result and the evaluation result to obtain at least one cluster diagnosis; and assembling the cluster diagnosis and the matched written templates to obtain a report to display the report to a radiologist. These system, analyzer and method are achieved in greatly reduced lengths of time and are useful for cost and time savings.

A non-transitory computer readable medium (16) stores instructions readable and executed by an electronic processor (14) to execute a method (100) requesting radiation assistance. The method includes providing a requester graphical user interface (GUI) (28) on a display device (24) of a requester electronic processing device (18) operable by a requester; receiving one or more inputs from the requester to fill in fields in the requester GUI to generate an assistance request for radiation assistance (40); sending the assistance request for radiation assistance to a recipient electronic processing device (30) operable by a radiologist; providing, on a display device (36) of the recipient electronic processing device, a recipient GUI (38) showing a list (44) of assistance requests for radiation assistance; and tracking and marking the status of each assistance request in the list for radiation assistance.

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.

In some embodiments provided herein is a generative foundation model trained over millions of health system-scale electronic health records along with web-scale medical text corpora to acquire knowledge of both medical practices and theories, and use of the generative model for rare disease diagnosis (including rare ophthalmic, diseases and rare systemic diseases), emergency condition identification (including ophthalmic emergencies and systemic emergencies), complex disease solving ("diagnostic puzzles"), or generating multimodal medical imaging reports (including ophthalmic images and radiology images such as X-rays and CT scans). In some embodiments, the generative model involves the use of language data, for pre-training, language data for supervised finetuning using a instruction tuning approach (e.g., QA pairs), and a human-machine hybrid evaluation strategy. In some embodiments, both the pre-training and supervised finetuning phases involve the use of a particular method of scaling to extend the context window. In some embodiments, the human-machine hybrid evaluation strategy involves language data for automated evaluations, as well as evaluations by generalists and by different specialists (e.g., ophthalmologists and radiologists) of varying levels of experience. In some embodiments, the generative foundation model, MetaGP, is used for unmet clinical needs through integration of medical and multimodal imaging data.

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.

The present invention relates to the technical field of radiology, and in particular to assisting radiologists in radiological examinations using artificial intelligence methods. The present invention relates to training a machine learning model and using the trained model to predict representations of an examination area after applications of different amounts of a contrast agent.

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.

Systems and methods for identifying and presenting prior findings data in a radiology review workflow, based on natural language processing (NLP) of unstructured radiology report text, are disclosed. In an example, prior findings are generated with operations including: receiving text from a prior radiology report prepared for a patient in a prior radiology study; processing the text with a trained NLP engine to classify discrete prior findings in the prior radiology study that identify observed condition(s) and characteristics of the observed condition(s); and outputting or presenting the prior findings, such as in a radiology review user interface. In an example, the radiology review user interface displays the observed condition(s) and the characteristic(s) of the observed condition(s) to a user (e.g., a radiologist), and can apply sorting, filtering, or grouping based on pathology phrases or attribute words associated with the observed conditions or the characteristics of such observed conditions.

An artificial intelligence engine for directed hypothesis generation and ranking uses multiple heterogeneous knowledge graphs integrating disease-specific multi-omic data specific to a patient or cohort of patients. The engine also uses a knowledge graph representation of ‘what the world knows’ in the relevant bio-medical subspace. The engine applies a hypothesis generation module, a semantic search analysis component to allow fast acquiring and construction of cohorts, as well as aggregating, summarizing, visualizing and returning ranked multi-omic alterations in terms of clinical actionability and degree of surprise for individual samples and cohorts. The engine also applies a moderator module that ranks and filters hypotheses, where the most promising hypothesis can be presented to domain experts (e.g., physicians, oncologists, pathologists, radiologists and researchers) for feedback. The engine also uses a continuous integration module that iteratively refines and updates entities and relationships and their representations to yield higher quality of hypothesis generation over time.

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.

Presented herein is a pipeline, integrating deep learning and language models to improve efficiency and accuracy in radiologist's workflow, as well as generate robust structured data abstracting patient's MRIs. A universally compatible pipeline can be integrated across major clinical platforms and shared with various institutions, marking a pivotal step towards reducing radiologist human error.

Existing approaches for identifying a prosthesis model involve rigorous examinations and visual inspection comparison of X-ray images which is difficult for both radiologists and orthopedic surgeons. This can be a meticulous task that is tedious, dependent on the surgeon's experience, time-consuming and an erroneous recognition can have certain consequences. Method and system disclosed herein provide an approach which involves use of a 3-block classifier for extracting finer features of implant from an X-ray image being processed, and then comparison of the extracted features with manufacturer specifications for identifying manufacturer and type.

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.

The invention provides a focus identification method based on a standard template and an image report generation method. The method specifically comprises the following steps: receiving a medical image of a patient; determining a part to which the medical image of the patient belongs, and selecting a standard template corresponding to the part, the standard template comprising an anatomical marker structure; extracting a key internal structure contour in the medical image of the patient, and performing feature matching on the key internal structure contour and a corresponding anatomical marker structure in the standard template; under the condition of feature matching registration, voxel-by-voxel difference calculation is carried out on the medical image of the patient and a standard template in various transform domains, and a multi-channel residual image is generated; generating an exception enhancement graph according to the multi-channel residual image to screen an exception region; and automatically generating a focus description statement based on the abnormal region. According to the invention, the workload of initial writing of an image report by a radiologist can be obviously reduced, and the clinical working efficiency and the image interpretation consistency can be improved.

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 medical image intelligent auxiliary diagnosis health management system based on deep learning, and relates to the field of medical information.The method comprises the steps that firstly, image data are preprocessed to eliminate equipment noise and physiological artifact interference, and multi-scale space-semantic features of cross-time-phase images are extracted; the method comprises the following steps: capturing a lesion evolution trajectory by using feature map differential operation, performing pathological feature enhancement and redundancy suppression on difference features through spatial-semantic joint enhancement, and finally converting time sequence difference features into a color gradient map according with cognitive habits of radiologists by means of a generation mechanism. Therefore, misjudgment caused by motion artifacts and physiologic changes can be overcome, the spatial topological structure of a focus area is kept, and meanwhile, the distinguishability of tiny pathological changes is enhanced, so that tiny but key pathological feature expression is enhanced, and the original image anatomical sites are visually mapped.

To provide methods and computer systems directed to performing real-time and / or AI-assisted radiology.SOLUTION: A method for processing an image of a location of the body of a subject includes: a step 102 of obtaining the image of the location of the body of the subject; a step 104 of using a trained algorithm to classify the image or a derivative thereof to one category among a plurality of categories; a step 108 of sending the image to a first radiologist for radiological assessment if the image is classified to a first category among the plurality of categories, or a step 110 of sending the image to a second radiologist for radiological assessment if the image is classified to a second category among the plurality of categories; and a step 112 of receiving a recommendation from the first or second radiologist to examine the subject on the basis of at least part of the radiological assessment.SELECTED DRAWING: Figure 1

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.

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.

A venous access port assembly having a housing base with a discharge stem, a cap, and an interior reservoir. The port assembly is provided with X-ray discernable indicia to identify an attribute of the assembly after its implantation and clearly appear on an X-ray of the patient in a manner informing the radiologist or technologist and the medical practitioner of that particular attribute. The indicia are cuts in a reservoir lining of radiopaque material such as metal where the cuts having narrow slot width are in the form of one or more sets of alphabetical letters such as “CT” in the lining's side wall or bottom wall.

Improved systems and devices for medical imaging distribution are provided. A medical imaging order may be received from a medical facility that includes medical imaging. A configuration may be selected and applied based on a body site and an urgency field associated with the order that defines queueing rules for the medical imaging order. Utilization factors for queues associated with radiologists may also be determined. The configuration and the utilization factors may be used to determine a subset of queues associated with a subset of radiologists. The subset of queues may be prioritized based on certain requirements, such as how many medical imaging reports a particular radiologist is required to review, how many medical imaging reports are required to be allocated to a particular radiologist, and the like. The highest prioritized queue may be selected and the medical imaging order may be transmitted to the radiologist associated with that queue.

A non-transitory computer readable medium (26s) stores instructions executable by at least one electronic processor (14s) to perform a method (100) to coordinate radiologist review of a medical imaging procedure performed using a medical imaging device (2). The method includes acquiring a video (17) of the medical imaging device, the acquired video including at least one of video acquired by a camera (16) arranged to image the medical imaging device and / or video comprising screen-scraped image frames of an imaging device controller (10) of the medical imaging device: determining a review time for the medical imaging procedure based on the video: providing a notification to a remote electronic processing device (12) operable by a radiologist of the determined review time; and at the review time, extracting at least one review image (38) from the video and making the at least one review image available at the remote electronic processing device.

Provided are systems and methods for analyzing sonograms of abdomen to identify presence or absence of free fluid in the abdomen. The systems and methods are useful for performing or assisting with point of care diagnosis of presence of internal bleeding in trauma patients without requiring input from a radiologist or another clinician trained to analyze sonograms for presence or absence of free fluid. Also provided are methods and system for training a medical imaging system for analyzing sonograms of abdomen to identify presence or absence of free fluid in the abdomen.

Systems and methods are provided for improved intra-operative micro-CT imaging of explanted tissue samples and for improved visualization of such samples. These embodiments provide for reduced scan times and the ability for radiologists to quickly receive useful scan imagery and to provide accurately-communicated recommendations to the operating surgeon. Improved scan visualization methods facilitate surgeon and radiologist interaction with the scan data, including of annotation, viewing, and re-orientation to accurately reflect the orientation of imaged tissue samples relative to the body prior to explantation. Improved visualization methods include color-coded sample texturing to indicate sample orientation, color-coded tumor visualization to indicate proximity to sample margins, and intuitive methods for adjusting the location and orientation of two-dimensional visualizations relative to the sample.