21 results about "Reviewing Radiologist" patented technology

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.

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.

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.

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.

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.

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.

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.

The invention relates to X-ray imaging. An X-ray apparatus (100) is provided. The X-ray apparatus (100) comprises an X-ray generator (10) and an X-ray detector (12). The X-ray generator (10) is configured to generate a polychromatic X-ray beam within a frequency range of an object to be imaged. The multicolor X-ray beam is configured to have an energy dependent angle with respect to the X-ray detector (20). The X-ray detector (20) is configured to acquire a plurality of energy-resolved absorption images of the object, the plurality of energy-resolved absorption images being usable to generate a stereoscopic absorption image of the object. This new imaging modality can be considered between pure projection imaging and full 3D modality. Thus, it can be used to clarify the indication at the early stage of the diagnostic path without the need to acquire a complete 3D image. The X-ray apparatus may relax the requirement for precise patient positioning for fine examinations (e.g., outboard of the ankle), since the radiologist may compensate for misalignment after image acquisition.

A kind of COVID-19 infection medical image classification method based on self-supervised learning belongs to image classification technical field, based on MAE self-supervised method, and is supplemented with self-distillation learning, constructs SSL-Covid model, SSL-Covid model learns domain knowledge from data by self-supervised learning, and is added with self-distillation learning to improve model feature extraction capability, provides auxiliary information for COVID-19 infection classification;MAE is introduced into this field, and corresponding improvement is carried out, a self-distillation module is added, and a self-supervised learning COVID-19 medical image classification model SSL-Covid is constructed, which aims to shorten the diagnosis time of COVID-19 patients by using computed tomography CT, reduce the duration and number of disease spread and provide decision support system for radiologists to diagnose COVID-19.The SSL-Covid model of the application has more advantages than MAE in the recognition of COVID-19 infection in CT image, and well relieves the problem of large-scale data demand, improves the performance of ViT in medical image classification.

The invention relates to the technical field of disease diagnosis models, in particular to a construction method and application of a liver cirrhosis diagnosis model based on liver MRI and serum biomarkers, and is technically characterized in that the construction method comprises the following steps: S1, constructing a convolutional neural network model; s2, constructing a fusion model based on a convolutional neural network model and clinical and laboratory indexes; and S3, evaluating the model diagnosis efficiency. The hepatocirrhosis non-invasive diagnosis model is established by fusing multi-dimensional indexes of'multi-parameter MRI + liver function indexes + deep learning technology ', the hepatocirrhosis diagnosis probability is output by inputting the MRI image and the liver function indexes, and compared with traditional radiologists, the diagnosis result is more visual, objective and convenient. On one hand, radiologists can be assisted to diagnose liver cirrhosis, the diagnosis efficiency and confidence of the radiologists can be improved, on the other hand, clinicians can be assisted to make individualized follow-up monitoring and treatment decisions for patients, and high-quality accurate diagnosis and treatment are achieved.

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.