162 results about "Radiology studies" patented technology

The invention relates to the technical field of medical image processing, in particular to a CT image intelligent analysis system for pneumonia auxiliary screening. The method comprises the following steps: firstly, preprocessing a chest CT image and detecting a candidate focus area; secondly, extracting a topological feature, a deep convolution feature and a texture statistical feature based on a persistent coherence theory from each candidate focus, and performing feature fusion through a multi-head self-attention mechanism to generate a unified focus representation vector; mapping the lesion characterization vectors to a pre-constructed radiology knowledge graph, adopting a graph neural network for reasoning, and outputting the pneumonia suspected probability and lesion classification of each lesion; and finally, performing fusion and uncertainty quantification on the analysis results of the plurality of focuses by adopting an evidence theory, and generating a comprehensive screening report. According to the method, complex-form lesions are effectively identified through topological features, accurate identification of lesion types is realized through knowledge graph reasoning, and diagnosis uncertainty quantification is provided through an evidence theory.

The invention provides an automatic focus identification and grading method for a medical image, and the method comprises the steps: carrying out the standardization of an obtained multi-modal original image based on anatomical constraint, and obtaining a standardized image; generating semantic enhancement features through a cross-modal feature compensation network based on the standardized image and associated radiological text description; performing dynamic feature adaptation processing on the semantic enhancement feature to generate a modal adaptive feature; performing context reasoning through a multi-scale feature interaction algorithm based on the modal adaptive features to generate context reasoning features; and lesion identification decoding processing is carried out on the context inference feature map, a lesion segmentation mask is generated, and the lesion segmentation mask is used for extracting lesion area feature parameters to carry out lesion classification. By adopting the method, the adaptability to the missing mode can be enhanced, and the focus identification and grading precision can be improved.

The invention discloses a radiology report generation method and system based on visual collaborative enhancement and a cross-modal fusion network, and belongs to the technical field of natural language processing. According to the invention, a visual collaborative enhancement module is designed for modeling visual features from global and local perspectives to enhance the recognition of abnormal lesions in a radiology image, so that the attention deviation of an abnormal region caused by unbalanced data distribution is relieved. Meanwhile, a cross-modal information fusion device is provided, the module utilizes a novel double cross-modal communication component to promote multi-level fusion of visual and text information, the problem of modal isomerism is solved, and semantic-level feature alignment and refinement are achieved. According to the method, the problem that a model cannot capture key focus features due to unbalanced data distribution in a radiology image is solved, and the problem that effective alignment and fusion are difficult due to the fact that feature spaces of different modal information of image and text information are different is solved.

The invention discloses a cross-modal gating fusion radiology report generation method combined with clinical semantic enhancement, and relates to the technical field of medical report generation. A semantic enhancement decoding unit is used for performing deep fusion on image sequence representation, historical text embedding and clinical semantic representation to obtain final enhancement decoding representation of multi-modal semantic enhancement; and performing report generation on the final enhanced decoding representation output by the semantic enhanced decoding unit to obtain a predicted radiology report. The predicted radiology report generated by the radiology report generation method is closer to a radiology report sample text in the aspects of sentence structure and coherence.

The present invention relates to a cognitive computing-assisted clinical decision support system designed to enable personalized neurological rehabilitation. The system acquires structured user data across clinical, anatomical, radiological, etiological, pathological, and rehabilitation domains to create individualized profiles. These profiles are mapped against a repository of historical cases using analog matching and similarity scoring to generate stratified, evidence-based rehabilitation recommendations. Real-time monitoring of rehabilitation progress is performed using global recovery and function outcome indicators, allowing for dynamic adjustment of treatment plans. Clinician intervention modules ensure safety, interpretability, and context-aware customization. The system incorporates a continuous feedback mechanism to refine future predictions and recommendations, making it increasingly adaptive over time. The invention improves rehabilitation outcome prediction accuracy, reduces recovery variability, and optimizes functional outcomes by transforming static rehabilitation models into intelligent, responsive, and personalized care pathways.

The invention relates to the technical field of deep learning, in particular to a radiotherapy plan dose distribution verification method based on deep learning, and the method comprises the following steps: collecting historical radiotherapy plan data, generating a physical reference dose field through a Monte Carlo algorithm, unifying the voxel resolution of an anatomical structure to 1 cubic millimeter, and normalizing the dose according to a prescription, data enhancement is carried out only by adopting translation and mirror transformation, trace Gaussian noise is added, and a physical information enhanced three-dimensional training data set is constructed. According to the method, a three-dimensional convolutional network is utilized to automatically learn a dose distribution rule of a historical high-quality plan, a physical constraint module is embedded to ensure that a prediction result accords with a radiology principle, a real-time clinical rule engine is combined to instantly identify and correct a violation hot spot cold region, and an uncertainty quantification technology is assisted to position a high-risk region, so that the accuracy of a prediction result is improved. Finally, minute-level full-automatic verification is achieved, executable optimization suggestions are output, and efficiency is improved by dozens of times while safety is improved.

A real-time artificial intelligence (AI) framework is provided for the automated analysis of radiological images and detection of disease, such as extracapsular extension (ECE) in prostate cancer. The system includes a dual deep learning architecture comprising a first convolutional neural network (CNN) for identifying diagnostically relevant image slices from three-dimensional MRI data, and a second CNN for classifying disease presence based on those slices. A preprocessing pipeline standardizes and harmonizes image input, and cropping algorithms isolate the region of interest for enhanced model performance. This framework enables scalable, high-accuracy diagnosis across various imaging modalities including but not limited to MRI, CT, PET, ultrasound, and diverse disease types, improving clinical decision-making and supporting integration into real-time radiology workflows.

The invention discloses a radiology report generation method and system based on global dependency learning, and belongs to the technical field of natural language processing. According to the method, a global dependency learning module is designed, and the module integrates rotation position coding to enhance original Mama, so that long-sequence visual feature dependency is effectively modeled, and the model calculation efficiency is improved. According to the method, the problems that the visual long-term dependence modeling and calculation efficiency in the radiology image are difficult to effectively balance, and the heterogeneity of the characteristics of different modal information of the image and the text is difficult to effectively align and fuse are solved; the method not only can effectively capture the long-term dependency relationship on the key visual features in the radiology image, but also can improve the model calculation efficiency, and can enhance the alignment and fusion capability among the multi-modal heterogeneous information.

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.

Apparatuses, systems, and methods are disclosed to manage and process surgical data, and enable collaboration between two or more surgeons or other clinicians. A repository may be created that includes surgical data (including video data and / or 2024 / 030683 or radiological image data) that may be selectively shared between medical professionals. One or more trained neural networks may process the annotated surgical reports in order to populate the repository. Additional trained neural networks may generate surgical recommendations in response to user requests, based on contents of the repository. Other trained neural networks may generate surgical templates to guide a surgeon during an operation.

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 invention discloses a chest image diagnosis method and system based on multi-modal sign collection, and relates to the technical field of medical image.The method comprises the steps that a chest image of a patient is obtained, electrocardiosignals and blood oxygen saturation data are synchronously collected, and an associated radiology report is obtained; the image lesion features and the frequency domain rhythm template are combined for processing, motion artifacts are eliminated through a frequency domain decoupling equation, and refined image features are output; inputting the refined image features and the text pathological semantic features into a bidirectional attention mechanism to generate fusion features, and splicing the oxyhemoglobin saturation data and the text pathological semantic features into a sign-text vector; and inputting the fusion feature and the sign-text joint vector into a multi-task loss function, and outputting a structured diagnosis report. According to the method, accurate elimination of motion artifacts is achieved through a frequency domain decoupling equation, and coupling calculation is conducted on an electrocardio rhythm template and image lesion features in a frequency domain space.

A computer-implemented method for assisting radiologists in efficiently preparing radiology reports from diagnostic images is disclosed. The method includes processing radiology images using artificial intelligence to automatically detect anatomical structures and pathologies, and generating positional and descriptive data for each detected feature. An initial radiology report, fully populated with the detected features, is automatically generated prior to user interaction and displayed through a user interface comprising synchronized image and text panels. The radiologist reviews this initial report by selectively adding, modifying, or deleting features through a user interface input that identifies each feature and an associated action. The report is updated immediately based on these inputs, ensuring continued synchronization between image annotations and their descriptive narratives. This approach reduces reporting turnaround times, decreases cognitive workload, and minimizes diagnostic errors.

The invention discloses a learnable radiology report generation method based on visual text alignment and fusion of retrieval enhancement. The method comprises the steps of collecting and respectively constructing a model training data set and an auxiliary data set, then constructing a visual text alignment and fusion model based on retrieval enhancement, and inputting the model training data set and the auxiliary data set into the visual text alignment and fusion model based on retrieval enhancement together for training. Constructing an inference model according to the trained visual text alignment and fusion model based on retrieval enhancement; and inputting the to-be-detected medical image and the auxiliary data set into the reasoning model for processing to obtain a radiology report corresponding to the to-be-detected medical image. According to the method, retrieval correlation is enhanced, meanwhile, a fine-grained vision-text alignment and fusion method is adopted to align and fuse features, and the problem that fine-grained region-sentence alignment is difficult due to weak supervision of an image report level in the medical report generation process is solved.

An oncological foundation model is trained with broad, multimodal data to make predictions concerning a variety of different types of cancers. For example, the foundation model may make use of medical images drawn from radiology and pathology, as well as immunohistochemistry data; the presence or absence of biomarkers for particular diagnoses; patient history data; patient demographic data; and other forms of medical data. When using medical images, whole medical images as well as feature sets derived from the medical images may be used. The foundation model may have both causal predictive abilities as well as generative abilities.

Provided herein is technology relating to radiology and radiotherapy and particularly, but not exclusively, to apparatuses, methods, and systems for multi-axis medical imaging of patients in vertical and horizontal positions.

The invention discloses a radiology report generation method based on hierarchical interactive fusion, and relates to the technical field of medical image intelligent processing, and the method comprises the steps: collecting original image data, carrying out the intensity normalization, obtaining normalized image data, carrying out the multi-level visual feature extraction and feature dimension unification of the normalized image data, and carrying out the multi-level visual feature extraction and feature dimension unification of the normalized image data; forming a hierarchical visual feature set; coding processing is carried out on the hierarchical visual feature set, cross-layer attention relations between a shallow layer and a deep layer and between a middle layer and the deep layer are constructed in the coding process, a shallow layer two-dimensional biased field and a middle layer two-dimensional biased field are formed, and a migration smoothness index and a consistency index are obtained after nonlinear resampling; and in the decoding stage, a multi-path cross attention structure is constructed based on the coded hierarchical visual feature set, an abnormal priori graph is constructed according to the shallow two-dimensional biased field and the middle two-dimensional biased field, attention bias is formed, and a cross-hierarchical cross attention aggregation vector is generated. According to the invention, association expression of multi-level visual features is realized.

A computer-implemented method uses a plurality of input examination data sets, created by performing a plurality of imaging examinations of at least one patient on at least one scanner, to learn a model of imaging protocols. The model may learn imaging protocols by capturing common features across the plurality of input examination data sets. The method may regroup examination data sets, within the plurality of input examination data sets, with common features under a common protocol tag, and learning the model may include generating a plurality of protocol tags. The model may be updated over time based on new input examination data sets.

Presented is an invention in which visible light is substituted for damaging radiation, enabling students to learn about radiology through active learning (e.g. by manipulation of a study's subject and seeing, in real time, the resulting changes in the generated image) but without exposure to damaging radiation. The invention also allows student and senior practitioners to practice radiological techniques (e.g. patient positioning, fluoroscopy) without radiation exposure. The invention also allows demonstration of the relationship between a three-dimensional object and its two-dimensional depictions, such as between a machine part and that part's views on a technical drawing.

A surgical system for computer-assisted navigation during a surgical procedure includes at least one processor that obtains a 3D radiological representation of a target anatomy of a patient and a fiducial set of a registration fixture. An attempt is made to register a position of the fiducial set in the 3D radiological representation to a 3D imaging space tracked by a camera tracking system. Based on a determination that one fiducial of the fiducial set has an unsuccessful registration to a position of the 3D imaging space, the following operations are performed: displaying at least one view of the 3D radiological representation having a graphical overlay indicating that the fiducial has an unsuccessful registration to the 3D imaging space; receiving user-provided position information identifying where the fiducial is located in the 3D radiological representation; and registering the position of the fiducial to the 3D imaging space based on the user-provided position information.

The application belongs to the technical field of image processing, and discloses a personalized CT image reconstruction system based on double physical driving, which comprises an encoder, an anatomical information capturing module, a scanning information capturing module, a personalized modulation module and a decoder; the encoder is used for feature extraction of a scanned image to obtain imaging features; the anatomical information capturing module is used for extracting anatomical modulation parameters containing anatomical information according to a radiology report; the scanning information capturing module is used for capturing potential relationships between scanning protocols and noise distributions to obtain scanning modulation parameters containing physical information; the personalized modulation module is used for modulating the imaging features obtained by the encoder according to the anatomical modulation parameters and the scanning modulation parameters to obtain personalized imaging features; and the decoder is used for generating a personalized CT image according to the personalized imaging features. The application also discloses a federated learning method suitable for personalized CT image reconstruction. Through double physical driving based on scanning parameters and anatomical information, the application can effectively realize personalized CT imaging.

Example solutions for zero-shot domain transfer with a text-to-text model train a text-to-text model for a target domain using unlabeled in-domain text training data, and concurrently train the model using labeled general-domain task training data. The in-domain training comprises masked language modeling (MLM) training, and the task training comprises both natural language generation (NLG) training and natural language understanding (NLU) training. The NLG training comprises natural language inference (NLI) training and the NLU training comprises summarization training. The trained model acquires domain-specific task competency, sufficient to perform a language task within the target domain. Suitable target domains include radiology, biomedical, and other medical, legal, and scientific domains. This approach leverages large volumes of general-domain task training data and plentiful unlabeled in-domain text, even as labeled in-domain training data may be unavailable or prohibitively expensive for certain specialized domains.

The present disclosure pertains to a method of predicting a molecular subtype of a tumor of a subject by (1) receiving preoperative imaging data of the tumor; (2) extracting one or more features of the imaging data; and (3) correlating the extracted features of the imaging data to a molecular subtype of the tumor. The methods may also include steps of outputting the molecular subtype of the tumor and implementing a treatment decision. The present disclosure also pertain to a computing device and a diagnostic test for predicting a molecular subtype of a tumor of a subject in accordance with the aforementioned methods.

A method includes receiving an image scan of a patient and extracting, from the image scan having a first image format, metadata. The method also includes standardizing the metadata extracted from the image scan having the first image format according to a format of a schema associated with a relational database, and storing the received image scan having the first image format in data storage and the standardized metadata in the relational database. The method also includes converting the image scan having the first image format into a corresponding image scan having a second image format different than the first image format, and storing the corresponding image scan having the second image format in the data storage.

The invention relates to a method for the automated planning of a radiological examination, comprising the steps: - Providing information about an upcoming examination of a patient (P) from a radiology information system (23), including at least one RIS comment (C) concerning the examination, - automated comparison of strings (Z) of the RIS comment (C) with names (N) of investigation programs (24) of a given investigation list (U), and selection of a number of investigation programs (24) for these strings (Z) based on the comparison, - Dispensing the selected investigation programs (24). Furthermore, the invention comprises a device and a medical imaging system.

This invention provides a hybrid manufacturing assembly of anatomical and workflow procedure in fabricating radiologically accurate animal phantoms. A method in which by utilizing additive manufacturing and molding technologies to fabricate the soft tissue, lungs, and hard tissue phantom of animal of interest and a way to assemble the organs such a radiologically heterogeneous phantom and structure is created and with the option to add provisions for radiological dosimetry devices is provided. The specified workflow is intended to address the need for low to medium volume production of small animal phantoms.

A radiology report automatic generation method, system, device and medium based on patient specific priori knowledge uses a special token to represent missing patient clinical context information, so that a text encoder can process complete and incomplete clinical context input in a unified manner, and then robust clinical context features are extracted; the method comprises the following steps: constructing a space-time fusion network STF, integrating previous medical images of a patient, establishing a difference mapping relation between a current image and a historical image for modeling an evolutionary process of a disease, and extracting space-time visual features with time dependence; establishing an attention-enhanced hierarchical fusion network for fusing multiple layers of hidden states in a visual encoder so as to extract multiple layers of hierarchical visual features with rich semantics; a prior perception progressive fusion network is introduced, and patient specific prior knowledge and hierarchical visual features are gradually fused in a coarse-to-fine mode to generate multi-modal features facing radiology report generation; the method comprises the following steps: designing a two-stage training strategy: in the first stage, aiming at image-text alignment, improving the accuracy of medical image-text retrieval; and in the second stage, the generation of the radiology report is taken as a target, a text decoder is optimized, and the performance of the generated radiology report in the aspects of clinical semantic accuracy and language expression quality is improved.

In one embodiment, the present disclosure provides a computer-implemented method comprising: receiving a first labeled training dataset comprising a first plurality of images each associated with a set of labels; programmatically training a machine learning neural Teacher model on the first labeled training dataset; programmatically applying a machine learning model trained for NLP to an unlabeled dataset comprising a digital electronic representation of natural language text summaries of a second plurality of images, thereby generating a second labeled training dataset comprising the second plurality of images; programmatically generating soft pseudo-labels using the machine learning neural Teacher model; programmatically generating derived labels using the soft pseudo-labels; training one or more machine learning neural Student models using the derived labels; receiving a target image; applying an ensemble of the one or more Student models to output one or more classifications of the target image.