211 results about "Clinical work" patented technology

Systems and methods for generating a response summarizing patient data are provided. One or more prompts, comprising 1) patient data retrieved from one or more patient databases and 2) instructions, are received. A response summarizing the patient data is generated based on the instruction using a large language model. The response is output.

The invention discloses a cervical cancer close-range radiotherapy high-risk target area sketching method fused with a multi-modal image. The method comprises the following steps of collecting an MRI image scanned before radiotherapy and a CT image during radiotherapy of a cervical cancer close-range radiotherapy patient and annotation data of the MRI image and the CT image; the method comprises the following steps: preprocessing an MRI image scanned before radiotherapy and a CT image during radiotherapy, and converting annotation data into a three-dimensional tag image; on the basis of the preprocessed MRI image, the preprocessed CT image and the three-dimensional label image of the preprocessed MRI image, the preprocessed CT image and the three-dimensional label image of the preprocessed MRI image, registration of the MRI image and the CT image is conducted through a pre-constructed registration neural network model, feature extraction and fusion are conducted on the registered MRI image and the registered CT image through a pre-constructed high-risk target area automatic segmentation neural network model of a multi-scale cross-modal attention mechanism, and the high-risk target area automatic segmentation neural network model of the multi-scale cross-modal attention mechanism is obtained. Automatic delineation of a cervical cancer close-range radiotherapy high-risk target area is realized; according to the method, automatic segmentation of HR-CTV in close-range radiotherapy of cervical cancer is realized, high efficiency, accuracy and generalizability are realized, and intelligent support can be provided for clinical work.

The invention discloses a method for predicting intestinal preparation of a patient before colonoscopy based on machine learning, and the method comprises the following steps: S1, collecting clinical data of the patient, and carrying out the preprocessing of the clinical data; s2, performing feature selection on the collected clinical data by using a Boruta algorithm, and identifying important features related to insufficient intestinal preparation before colonoscopy; s3, constructing a prediction model by utilizing a machine learning algorithm, evaluating the prediction model by adopting a cross validation method, and selecting an optimal prediction model; s4, explaining the optimal prediction model by adopting an SHAP explaining tree model, and identifying a feature which has the greatest influence on a prediction result; s5, clinical data of a to-be-detected patient are transmitted to the optimal prediction model, and intestinal preparation of the patient before colonoscopy is predicted; according to the method, the risk of insufficient intestinal preparation can be accurately predicted, personalized guidance can be provided, the clinical working efficiency is remarkably improved, and the method has important clinical significance.

The invention provides a heart great vessel postoperative pulmonary complication prediction method and device and processing equipment, which are used for constructing a heart great vessel postoperative pulmonary complication prediction model on the basis of a machine learning model, so that the heart great vessel postoperative pulmonary complication prediction model has high pertinence to heart great vessel postoperative pulmonary complication and is high in accuracy. And a series of optimal configuration schemes are provided in the aspect of details, so that a good prediction effect can be realized, the high-quality prediction requirement on the high-precision pulmonary complications after the heart great vessel operation in clinical work is met, and high-quality data support is provided for the clinical work.

Various methods and systems are provided for an automated clinical exam workflow. In one example, method comprises performing a signal quality check of an electronic stethoscope at a first recording location on a subject, recording physiological data for an exam at the first recording location via the electronic stethoscope in response to the signal quality check satisfying a quality threshold, and outputting a signal quality alert in response to the signal quality check not satisfying the quality threshold. In this way, clinically relevant data may be obtained with reduced user effort and fewer manual inputs.

According to the modularized ultrasonic-guided lumbar puncture teaching model provided by the invention, on the basis of the detachable modularized ultrasonic-guided lumbar puncture teaching model, the pathological bony structure module for simulating the corresponding pathological conditions of the lumbar structure is introduced, so that the lumbar puncture process in different pathological states is simulated; therefore, trained medical staff can be helped to better understand the anatomical basis and operation key points of lumbar puncture, the understanding of different pathological changes is deepened, and an experimental platform is provided for studying the improvement of lumbar puncture operation, the occurrence mechanism of puncture-related complications and the like. Furthermore, better lumbar puncture operation in actual clinical work is promoted through a better teaching and training effect, and the medical service experience and personal safety of a patient are guaranteed.

A smart intravenous (IV) pole is disclosed, designed to maintain communications and seamlessly integrate medical devices even as a patient is relocated. The IV pole includes at least one antenna, and a network interface adapted for seamless communication during patient movement. The IV pole also includes a circuitry configured to obtain patient data associated with a patient. The IV pole retrieves one or more electronic medical health records associated with the patient. The IV pole retrieves medical data associated with the patient. The IV pole applies artificial intelligence (AI) models to at least one of the patient data, the electronic medical health records, or the medical data. The IV pole generates alerts associated with a medical condition of the patient and renders the alerts. The IV pole ensures uninterrupted data connectivity, monitoring, and communication during and after relocation, enhancing patient safety and supporting dynamic clinical workflows in modern healthcare environments.

The invention relates to the technical field of cardiovascular disease diagnosis, and discloses a myocardial perfusion and survivability evaluation method based on cardiac ultrasound contrast intelligent analysis. The method comprises the following steps: firstly, acquiring cardiac ultrasound contrast dynamic image data and pre-processing the data, then performing myocardial region segmentation by adopting an improved U-Net network in combination with an attention mechanism and a dynamic region growing algorithm, and constructing a myocardial perfusion zoning model. Then, myocardial perfusion dynamic characteristics are extracted, key time sequence characteristics are screened, and a multi-modal fusion evaluation model is constructed to generate a myocardial survivability probability distribution diagram. And finally, performing quantitative evaluation based on the probability distribution diagram, outputting a myocardial perfusion defect index and a survivability score, and generating a visual three-dimensional evaluation report. The accuracy of myocardial perfusion and survivability evaluation is improved, multi-modal information is integrated, quantitative evaluation and visualization are achieved, model robustness and clinical working efficiency are improved, and powerful support is provided for diagnosis and treatment of cardiovascular diseases.

The invention discloses a chest radiograph report generation and focus positioning method and system based on reinforcement learning, and belongs to the technical field of artificial intelligence and medical image analysis crossing. According to the method, firstly, a basic model is supervised and fine-tuned by using a multi-source chest radiograph image and a query text; and then optimizing the strategy model by adopting a GRPO reinforcement learning framework, introducing an indicator function guided by token entropy into a target function, and finally realizing parallel generation of an end-to-end chest radiograph report text and focus bounding box coordinates by the optimized strategy model. According to the method, visual positioning of the focus is realized while the generation quality of the chest radiograph report is improved, and the integrated output form of the method better fits the clinical actual working process.

The invention relates to the technical field of radiation dose calculation, in particular to a radiation dose calculation method and device based on a V-Net network. The method comprises the following steps: acquiring a CT image and light beam flux information for a focus area of a patient to be treated, wherein the light beam flux information represents parameter information of a light beam for performing radiotherapy on the patient to be treated; and on the basis of the CT image and the beam flux information, through a pre-trained V-Net network, performing radiotherapy dose calculation for a focus area of a patient to be treated, and generating a dose distribution diagram. According to the method, the dose distribution diagram can be automatically generated, human intervention is reduced, and the clinical working efficiency and the adaptability of a treatment plan are improved. Moreover, the pre-trained V-Net network can effectively capture features in the CT image, the calculation speed is greatly improved, the precision of the generated dose distribution diagram is equivalent to the calculation precision of the Monte Carlo method, and both the speed and the precision are taken into account.

A computer-implemented method for multi-modality medical image clinical workflow guidance comprises a step of receiving a user input (306-4) in relation to a first medical image data set (306-2) acquired by means of a first medical imaging modality (304-A). By means of a clinical-concept-to-medical-image linking algorithm (314), the received user input (306-4) is assessed in view of at least one second medical image data set (308-1) acquired by means of at least one second medical imaging modality (304-B). An indication of a clinical workflow guidance in relation to the at least one second medical image data set (308-1) is output.

The invention provides an ear endoscope image automatic description system based on a large language model, and the system comprises a data annotation module which carries out the annotation of an abnormal condition in an ear endoscope image, and converts the data of the ear endoscope image into a picture text pair data set; the model training module comprises a multi-modal large language model and is used for training the multi-modal understanding capability of images and texts of the model by using the picture texts; and the model reasoning module is used for reasoning the new ear endoscope image to obtain the text description of the ear endoscope image. According to the system, visual understanding and language generation capabilities of a large language model are combined, accurate otoendoscope image description can be automatically generated, and clinical work of otolaryngologists is effectively supported.

The invention discloses a method and a system for automatically analyzing chest respiratory muscle volume based on a deep learning network, and belongs to the field of medical images. The method for automatically analyzing the chest respiratory muscle volume based on the deep learning network comprises the steps that a CT image and a chest respiratory muscle image manually marked by a doctor are collected, and the CT image and the image manually marked by the doctor are preprocessed to obtain a standard sketching graph; performing data enhancement operation on the standard sketching picture; constructing a U-Net model, and training the constructed U-Net model by using the standard sketching graph after data enhancement; and automatically sketching the chest respiratory muscle in the medical image by using the trained U-Net model to obtain the contour of the chest respiratory muscle. A U-Net network model is constructed, an efficient, reliable and easy-to-implement solution is provided for three-dimensional medical image segmentation, the method has wide application prospects and clinical values, time consumed in clinical work is greatly shortened, respiratory muscle related information is helped to be obtained clinically and rapidly, treatment of a patient is guided, and prognosis of the patient is improved.

The utility model relates to the technical field of arterial sheathing canals, in particular to a femoral artery sheathing canal with a fixing patch, which solves the technical problems that two persons cooperatively control the femoral artery sheathing canal in the prior art, time and labor are wasted, and the operation time is prolonged, and comprises a sheathing canal body, a hemostasis sheathing canal seat, a three-way valve and a flexible fixing patch, the middle of the flexible fixing patch is fixedly connected with a hard special-shaped clamping groove, and the hard special-shaped clamping groove is matched with the hemostasis sheath tube base, the end of the sheath tube body connected with the hemostasis sheath tube base and the end of the connecting tube. A notch is formed in the flexible fixing patch, and after the hemostasis sheath tube base is clamped and matched with the hard special-shaped clamping groove, the end of the hemostasis sheath tube base corresponds to the notch in position; the lower surface of the flexible fixing patch is provided with a bonding layer used for being pasted on the skin. The femoral artery sheath tube can ensure that necessary passages of a femoral artery approach nerve interventional operation are smooth, manpower resource waste in clinical work is reduced, the probability that the femoral artery sheath tube is disengaged is greatly reduced, and it is ensured that the nerve interventional operation is carried out safely and smoothly.

The present invention discloses a method for detecting highly toxic and drug-resistant Klebsiella pneumoniae, the method comprising preparing a concanavalin A-magnetic nanoparticle complex; enriching Klebsiella pneumoniae using the concanavalin A-magnetic nanoparticle complex; putting the target solution into the RPA reaction system to amplify the Klebsiella pneumoniae nucleic acid; putting the RPA reaction solution into the CRISPR Cas12a detection system to cut and amplify the nucleic acid obtained to obtain a detection solution; performing detection limit determination or fluorescence detection on the detection solution, and outputting the detection result. The present invention can detect the target bacteria Klebsiella pneumoniae nucleic acid more quickly and specifically in actual clinical work, with high determination efficiency, simple operation and short detection time.

A method, computer program product, and computing system for generating an internal state prompt with medical content and a multi-action task to perform on a healthcare system. A first output healthcare system command is generated by processing the internal state prompt using a trained multimodal generative artificial intelligence (AI) model. The first output healthcare system command is converted into a first healthcare system-executable command associated with the multi-action task for a first target healthcare subsystem. Modified medical content is generated by executing the first healthcare system-executable command on the medical content using the first target healthcare subsystem. The internal state prompt is updated with the modified medical content generated by executing the first healthcare system-executable command and the first output healthcare system command listed as a past action performed during execution of the multi-action task.

A method for cardiac diagnostic analysis is provided. The system includes a processor and memory coupled to the processing unit, receiving data from standard 12-lead ECGs from an external device; at least one computer-readable storage medium containing instructions, including program code that causes the execution of procedures according to the programs encoded on a disk or other media capable of using advanced algorithms such as deep learning networks, so that they can be processed during any given procedure without substantially altering the existing clinical workflow.

The invention discloses a business-driven medical service item charging management quality control method and system, and belongs to the technical field of medical service management.The business-driven medical service item charging management quality control method and system comprise the following specific steps that firstly, a business dictionary and a commodity price dictionary are built, the business dictionary comprises business behavior entries, and the commodity price dictionary comprises a commodity price dictionary; the commodity price dictionary comprises publicized financial amount entries; 2, constructing a mapping relation, and enabling the business behavior entries to correspond to the financial amount entries; 3, instructions are obtained, each instruction corresponds to one business behavior entry, and after medical staff select the instructions, corresponding financial amount entries are obtained through mapping relation processing. According to the invention, the medical service charging management knowledge base is driven by combing the business, and the business is associated with the charge, so that the clinical satisfaction is improved, and the clinical staff can use more time to treat and cure the patient.

A clinical workflow optimization system includes a data processing and analysis platform electrically connected or communicatively coupled to a user device and an HIS server, and a generative AI model set communicatively coupled to the data processing and analysis platform. The data processing and analysis platform provides one or more assistant tools. When the user device selects an assistant tool to execute a task instruction, the data processing and analysis platform uses one model of the generative AI model set to interpret and execute the task instruction. The analysis platform presents the execution result of the task instruction to the user device, wherein data required to execute the task instructions is recorded in the content of the task instructions or provided by the HIS server, the user device, or an external database. On the platform, users can independently develop and build clinical data processing tools using natural language.

The invention discloses a clinical medicine practice skill evaluation system, and relates to the technical field of clinical medicine education, and the system comprises the following components: a comprehensive data processing module which collects and preprocesses real patient electronic medical record data and examinee multi-modal data, fuses all modal data features, cleans the electronic medical record data, and stores the cleaned electronic medical record data; providing data for subsequent modules; the personalized intelligent virtual patient is generated based on the electronic medical record data of the real patient, rich and diversified clinical simulation scenes are provided for examinees, the complexity and diversity of the real patient are achieved, the illness state can be adjusted in real time according to decisions of the examinees, the uncertainty and complexity of the clinical scenes are simulated, and the clinical simulation experience of the examinees is improved. The system has the advantages that the system is simple in structure and convenient to use, clinical thinking of examinees can be cultivated and improved in practice, the examinees can learn to make accurate judgment and decisions when the illness state changes, and meanwhile, the examinees can exercise the strain capacity of the examinees through interaction with virtual patients so as to better adapt to various challenges in actual clinical work.

The invention discloses a method, a system, equipment and a medium for screening depression based on fundus color photograph, and the method comprises the following steps: collecting a fundus color photograph image data set containing healthy individuals and depression patients, carrying out the feature measurement of the image data set, building a model based on a random forest, and classifying the image data. And verifying and evaluating the model, and applying the model to a clinical working process. According to the method, model training and verification can be carried out on patients diagnosed with depression and healthy people of the same age and gender through fundus color images, depression diagnosis and clinical risk factors, and a trained random forest model is obtained and is used for distinguishing the healthy people from the depression patients; and a method and a tool are provided for early screening and identification of depression.

The invention belongs to the technical field of osteoarthritis management, and particularly relates to an osteoarthritis management system based on artificial intelligence and a computer storage medium. The invention provides an osteoarthritis management system based on artificial intelligence. The system integrates an LLM-DL hybrid architecture, a multi-specialist module cooperation framework and an RAG knowledge base system, and can cover a complete clinical process from patient preliminary diagnosis to treatment scheme formulation. The system can comprehensively and accurately evaluate the disease state of a patient, provides personalized disease progress prediction, not only predicts 2-year and 4-year functional results and iconography progress, but also can identify specific risk factors of the patient, and provides a basis for accurate intervention. Through the design of the whole system, the clinical working efficiency and the prediction accuracy are remarkably improved, the dependence on specialist doctors and high-end equipment is reduced, high-quality KOA management can also be implemented in resource limited areas, and therefore the overall medical resource requirement is effectively reduced.

The invention discloses a genetic disease pathogenic site sorting and diagnosis auxiliary method and system based on multi-modal artificial intelligence. The method comprises the following steps: firstly, collecting genetic disease data to construct a heterogeneous knowledge graph; the method comprises the following steps: acquiring patient data, and executing an analysis process: generating multi-modal feature representation of candidate pathogenic sites from four dimensions of variation features based on rules, a tissue specificity mechanism, a protein three-dimensional structure and real-time literature evidence; carrying out fusion sorting on the features by using a sorting model, and generating an interpretable report and a clinical examination suggestion based on the uncertainty of a sorting result; and after the doctor executes examination according to the suggestion and feeds back newly added data, the analysis process is repeated until a preset iteration target is achieved. According to the method, the limitation of a static analysis model is broken through, multi-dimensional evidence fusion and clinical workflow embedding are realized, and the interpretation accuracy and diagnosis efficiency of the pathogenic site of the genetic disease are remarkably improved.

The present invention discloses an intelligent handover method, device and equipment. The intelligent handover method includes: obtaining the medical record information of all patients from the hospital information system; for each first patient, obtaining the attention tag of the medical record information of the first patient; if the attention tag is yes, outputting the first handover log of the first patient. The present application automatically identifies the patients who need attention based on the medical record information of the patients, and automatically outputs the necessary medical record information of the patients who need attention, so as to avoid the problem of wasting the energy of medical staff and delaying clinical work due to completing the handover log during the handover, and the problem of unclear description and unreadable handover log.

The utility model discloses a multi-way hose structure for oral cavity flushing, which comprises a pipe body, two ends of the pipe body are respectively connected with a tooth gun and a device for supplying cleaning fluid, one end of the outer wall of the pipe body, which is close to the device, is provided with a pressurizing empty bottle, the outer wall of the pipe body is connected with at least one branch pipe, and the end part of the branch pipe is provided with a connecting structure for butt joint. According to the multi-way hose structure for oral cavity irrigation, the design is more flexible and diversified, by arranging the branch pipes and the connecting structures, irrigation equipment can be connected with a plurality of tooth guns in a supporting mode, the universality and flexibility of the irrigation equipment are improved through the design, clinical working efficiency and convenience are expected to be improved, and the requirements in different clinical scenes are met.

The invention discloses an olfactory function detection kit and an olfactory scoring method, and the olfactory function detection kit is characterized in that six flavors of garlic, bromhidrosis, vinegar, lemon, fennel and mint are prepared into solid olfactory elements, and the solid olfactory elements are small in volatility, easy to store, strong in durability and simple to prepare. On the basis of clinical research on normal people and people with abnormal olfaction, a set of adaptive calculation formula is integrated, operation and calculation are more convenient, specificity and sensitivity are both improved, and compared with a conventional olfaction detection method, clinical working efficiency is improved, preparation is more convenient, storage is convenient, cost is low, and the method is more suitable for evaluation of olfaction functions of Chinese people. The detection kit provided by the invention has good detection performance and clinical applicability, and can effectively improve the detection accuracy.

Disclosed herein are methods for selecting and preparing patient data to facilitate the adoption and customized training of machine-learning models in clinical settings, particularly for radiation therapy treatment planning. The disclosed embodiments streamline the customized training process through an automated workflow that includes prefiltering patient metadata, retrieving relevant DICOM files, optional data anonymization, and generation of training data. The data is then organized into a format suitable for machine-learning training. The embodiments discussed herein reduce manual labor, minimize errors, and accelerate the integration of machine-learning into clinical workflows, enabling clinics to train and implement predictive models that replicate specific clinical practices, thereby enhancing treatment precision and improving patient outcomes.

A robotic surgical simulation system for clinical workplace simulation includes a virtual reality headset configured to display the clinical workplace simulation, a processor, and a memory. The memory includes instructions stored thereon, which, when executed by the processor, cause the robotic surgical simulation system to: generate a virtual operating room within a three-dimensional coordinate space. The virtual operating room includes a virtual surgical console. The instructions when executed by the processor further cause the system to compute, based on the captured image, a position of the user within the three-dimensional coordinate space relative to the virtual surgical console; and determine whether a user is engaged with or disengaged from the virtual surgical console based on the computed position.

The present disclosure describes various clinical workflows and other methods that utilize a telemedicine device in a healthcare network. According to various embodiments, a healthcare practitioner may utilize a remote presence interfaces (RPIs) on a remote access device (RAD), such as a portable electronic device (PED) to interface with a telemedicine device. The healthcare practitioner may directly interface with a display interface of a telemedicine device or utilize the RPI on a RAD. The present disclosure provides various clinical workflows involving a telemedicine device to view patient data during a telepresence session, perform rounds to visit multiple patients, monitor a patient, allow for remote visitations by companions, and various other clinical workflow methods.

The invention discloses an anti-slip and anti-seepage puncture drainage tube, and mainly relates to a medical instrument. The anti-slip and anti-seepage puncture drainage tube mainly comprises a drainage tube main body, an intracavity fixing device, an external fixing device and a buckle stretching device, a medical worker selects a proper position to puncture the drainage tube, adjusts the position and combines the fixed buckle and the sliding buckle in an involution mode, then the bendable blunt-end thin tube is pulled to be bent, the thin film attached to the bendable blunt-end thin tube is unfolded, the anti-seepage curved surface is formed, and the drainage tube is pulled outwards. The drainage tube is placed in the circular adhesive sheet, tightly surrounded and adhered to the outside of the puncture opening, the drainage tube is bent and placed in the ribbon, and the ribbon is tightened for fixation; after drainage is finished, the connecting stay wire is cut off, and the drainage tube and the circular sticking piece are pulled out together; the double-insurance drainage tube is suitable for drainage of cavity parts such as thoracic cavity and abdominal cavity in clinical work, the curved surface formed by bending the bendable blunt-end hose and the external fixing device are used for fixing and seepage prevention, double insurance is formed, and the fixing and seepage prevention effects are improved.