110 results about "Abnormal thyroid" patented technology

The invention discloses a disease diagnosis method and system based on multi-modal space-frequency domain adaptive fusion, and relates to the field of artificial intelligence and biomedical engineering.The method comprises the steps that multi-modal data are standardized, the unified and standardized multi-modal data are coded, and multi-modal initial feature representation is obtained; after projection and gating alignment and cross-modal interactive attention alignment are carried out on the initial feature representation of each modal, enhanced representations of each modal are obtained, and then the enhanced representations of each modal are fused into a shared feature representation; performing deep feature extraction on the enhanced representation of each mode to obtain deep features of each mode, and performing adaptive multi-domain feature enhancement processing to obtain multi-domain enhanced features of each mode; performing semantic alignment on the multi-domain enhanced features of each mode, and then performing fusion through a hierarchical attention mechanism to obtain fusion features; and the fusion features are input into a diagnosis network for prediction, a disease diagnosis result is obtained, and the intelligent diagnosis precision and robustness of papillary thyroid carcinoma are improved.

The invention discloses a thyroid ultrasound diagnosis report intelligent generation method based on a multi-mode large language model, and relates to a thyroid ultrasound diagnosis report intelligent generation method. The objective of the invention is to solve the problems of lack of term standardization and insufficient complex focus feature analysis in the prior art. According to the method, a full-flow technical system of double-flow coding, cross-modal alignment, dynamic man-machine cooperation and multi-dimensional evaluation is constructed. Multi-scale feature fusion of a thyroid global form and a nodule ROI region is realized through ResNet-50 and ConvNeXt double-flow coding networks, image-text semantic alignment is optimized by adopting a CLIP symmetry loss function, and training resource consumption is reduced in combination with an LoRA parameter fine tuning technology. A dynamic man-machine collaborative closed-loop mechanism is innovatively introduced, model parameters are iteratively optimized through doctor correction data, and a four-dimensional clinical evaluation system comprising ROUGE-L, BLEU-4, CIDEr and expert blind evaluation is established. The invention belongs to the technical field of medical artificial intelligence auxiliary diagnosis.

The invention relates to the field of medical health management, and discloses a thyroid-oriented whole-cycle health management method, which comprises the following steps of: firstly obtaining thyroid multi-source information data of a target population to form a data set, then extracting fusion features to generate a time sequence feature vector, and analyzing and calculating occurrence risk factors and scores of feature dimensions in a preset time window according to the time sequence feature vector; a comprehensive risk score is obtained through time sequence analysis, risk grades are divided, high-risk individuals are marked, and a personalized screening strategy is generated; then combining historical data of high-risk individuals, analyzing key driving paths with high risk scores, associating an external knowledge base to predict recurrence frequency, and finally dynamically adjusting and optimizing screening and intervention strategies according to the recurrence frequency; precise evaluation, grading and personalized management of individual thyroid health risks are achieved, then risk key driving paths are found out, recurrence is predicted, and the strategy is dynamically optimized to improve the disease prevention and treatment effect.

The invention provides a thyroid-related eye disease intraorbital soft tissue volume measurement method and system, and is applied to the technical field of data processing. Three-dimensional medical image data and system abnormal state information are obtained, the three-dimensional medical image data comprise CT images and MRI images, and the system abnormal state information comprises artifacts, isolated noisy points and irregular boundaries; data preprocessing is carried out on the three-dimensional medical image data, standardized image data is generated, and the standardized image data is formed by carrying out ternary processing on spatial registration, noise suppression and gray level normalization; processing the standardized image data and the system abnormal state information to generate an initial segmentation mask; processing the initial segmentation mask to generate an optimized mask; processing the optimized mask and the three-dimensional medical image data to generate soft tissue volume data; and processing the soft tissue volume data, the system abnormal state information and the historical volume data to generate a volume dynamic change report.

The invention belongs to the cross field of multiple subjects such as artificial intelligence, computer vision, natural language processing and medical image processing, and discloses a thyroid ultrasound-assisted diagnosis method and system based on text-driven vision pre-training and cross-modal feature fusion. Wherein the model building and training stage comprises the following steps of: preprocessing to obtain an unlabeled image-text pair data set and a labeled image-text pair data set; an image encoder and a text encoder are constructed, pre-training is carried out, and text-driven visual pre-training is realized; using a cross-modal splicing attention fusion module (CMCAF) to realize fusion of text features and image features, inputting the fused text features and image features to a full-connection layer classifier, and performing training optimization through supervision; the application stage is to predict benign and malignant thyroid nodules for the to-be-predicted image-text pairs. The medical image multi-modal fusion method has the advantages of high accuracy, low dependence on pathological labels, high model interpretability, good clinical deployment feasibility and the like, and the problem of the existing AI model in the aspect of medical image multi-modal fusion is effectively solved.

The invention discloses a thyroid tumor evaluation system based on combination of an ultrasonic image and pathological data, and relates to the technical field of auxiliary decision making. Comprising the steps that an image acquisition module preprocesses a thyroid ultrasound image; the pathology acquisition module is used for extracting structured data containing pathology malignancy scores and molecular marker expression parameters; the feature extraction module generates feature vectors from the images and the pathological data respectively; the region screening module dynamically generates an image region sensitivity threshold value based on the molecular marker and the pathological malignant score; a weighted extraction module recognizes an image suspicious region and generates weighted features according to the image suspicious region; the multi-modal fusion module fuses bimodal features through a weight distribution rule; and the evaluation decision module outputs benign and malignant prediction results and confidence scores. Dynamic sensitivity regulation and control of deep coupling of pathological semantics and image features are realized, and image suspicious region identification precision and cross-modal decision reliability precision are improved.

The invention discloses a method for constructing a secondary subclinical hypothyroidism screening model based on conventional physical examination data, which comprises the following steps of: constructing a high-quality data set containing the conventional physical examination data, and defining a sample tag in the high-quality data set as a G2SCH positive group or negative group; performing feature selection on the data set by adopting different feature selection methods, and screening out key feature subsets; an oversampling method is adopted to synthesize minority class samples in the data set according to different class balance proportions; adopting a plurality of machine learning algorithms to carry out model prediction training with sample labels as supervision on the basis of data sets of different classes of balance proportions and different key feature subsets, and constructing a plurality of candidate G2SCH screening models; and evaluating and selecting an optimal G2SCH screening model. According to the method, G2SCH screening can be carried out based on conventional physical examination data, dependence on thyroid hormone specialized detection indexes in an existing medical scene is overcome, and the method is suitable for the medical scene with limited resources.

The invention discloses a thyroid eye disease activity evaluation method and system based on an eye image, and aims to solve the problems that the existing clinical activity score cannot reflect continuous change of an illness state and is susceptible to subjective influence. The method comprises the following steps: acquiring original image data containing an eye region; performing standardized preprocessing on the image data; extracting multi-scale inflammatory features and structural features from the preprocessed image; carrying out cross-modal fusion on the extracted multi-scale features; calculating an activity quantitative score based on the fused comprehensive features; and outputting a visual report of the quantitative score and the evaluation conclusion. The system comprises an image acquisition module, an image preprocessing module, a feature extraction module, a feature fusion module, an activity evaluation module and a result output module. According to the technical scheme, objective quantitative evaluation of thyroid eye disease activity can be achieved, the consistency and repeatability of evaluation results are improved, remote image acquisition and automatic analysis are supported, and the space-time limitation of traditional clinical evaluation is broken through.

The present disclosure relates to methods of administering thyroid hormone receptor agonists. The present disclosure provides methods wherein the methods maintain the activity of a given thyroid receptor agonist in ameliorating or curing obesity, hyperlipidemia, hypercholesterolemia, diabetes, non-alcoholic fatty liver disease, non-alcoholic steatohepatitis, atherosclerosis, cardiovascular disease, hypothyroidism, and related conditions, meanwhile, thyroid-related and thyroid axis-related side effects are reduced or eliminated.

The invention discloses an optical positioning method and system for papillary thyroid cancer cells of a terahertz near-field system, and relates to the field of terahertz near-field imaging, and the method comprises the following steps: carrying out the imaging observation of a thyroid tissue section under a first magnification factor through a microscope, carrying out low-magnification imaging partitioning on an area where imaging observation is located according to the morphological characteristics of the cells to form partitioned images; selecting an area of suspected papillary thyroid cancer cells, performing observation and comparison under a second magnification, comparing the observed morphology with the known optical characteristics of the papillary thyroid cancer cells, and selecting an area near the area with the suspected characteristics as an optical positioning point and performing marking; and carrying out positioning according to the optical positioning point, and carrying out scanning imaging by using a terahertz near-field system. The problems that a terahertz near-field system is long in time consumption and difficult to locate when finding papillary cancer cells in multiple cells of thyroid tissue slices are mainly solved.

The present application relates to medical teaching aid technical field, especially intelligent feedback type thyroid ablation operation simulation training device. The model includes a simulation person, a cervical vertebra module, a thyroid module and a simulation nodule. The simulation nodule is made of a thermal response material, has a heating element inside, an ablation needle integrated with a temperature sensor, and a control unit triggers heating according to the sensor signal, so that the physical state of the nodule changes and the real ablation effect is simulated. The present application has a simple structure, can provide intuitive visual and tactile feedback, and is used for medical staff surgical skill training and objective examination.

The utility model discloses a kind of thyroid ablation post-neck ice compress band, including front cloth band, the middle part of front cloth band is equipped with rectangular through-hole, heat conduction layer is sewn in the inside of through-hole, the left side of front cloth band is sewn and has the storage assembly for placing ice bag, the both ends of front cloth band are sewn and have connecting band;The utility model connects first fixed band and second fixed band by connecting band at the both ends of the front cloth band with hollow, make overall form scarf style around patient neck, not only guarantee the reliability of ice compress, but also suitable for different population use, while setting heat conduction layer and storage assembly in the middle part of front cloth band, storage bag in storage assembly is divided into two areas by separation cloth for placing ice bag, so that it corresponds with heat conduction layer, heat conduction layer can efficiently transfer the temperature of ice bag to improve ice compress effect.

The invention relates to the technical field of medical teaching appliances, in particular to an intelligent feedback thyroid ablation operation simulation training device. The model comprises a simulation person, a cervical vertebra module, a thyroid gland module and a simulation nodule block. The simulation nodule is made of a thermal response material, a heating element and an ablation needle integrated temperature sensor are arranged in the simulation nodule, and the control unit triggers heating according to a sensor signal, so that the physical state of the nodule generates observable change, and a real ablation effect is simulated. The device is simple in structure, can provide visual visual and tactile feedback, and is used for surgical skill training and objective examination of medical staff.

The invention discloses a 3D printing-based thyroid puncture model manufacturing method capable of being trained under the guidance of B-type ultrasound, and relates to the field of medical teaching model manufacturing. The core of the method is that a 3D printing technology and a traditional casting technology are fused, and firstly, a split thyroid shaping mold capable of being customized in a personalized mode is printed through digital design; then, a polyvinyl alcohol-graphite powder composite material with a specific ratio is adopted to simulate thyroid tissues and nodules respectively, so that the high simulation degree of the model on acupuncture handfeel and B-type ultrasonic images is ensured; and then through innovative two-stage casting and a special nodule fixing structure, the precise positioning problem of the suspended nodule in the model is solved. And finally, performing freeze-thaw cycle treatment to obtain a finished product. The high-simulation thyroid model manufacturing method suitable for ultrasonic guided puncture training has the advantages of being low in cost, high in simulation degree and high in individuation.

Provided are a set of fully human TSH receptor (TSHR)-blocking monoclonal antibodies targeting a CHK36 homolog cluster, a preparation method therefor, and an application thereof. The method comprises the following steps: using flow cytometry to sort plasma cells and single memory B cells that specifically recognize TSHR in peripheral blood of a patient with a high titer of TSH-blocking antibodies (TBAb), performing in vitro cloning of antibody light and heavy chains and performing recombinant expression, and using hTSHR-CHO cells to perform screening and validation of antibody properties to obtain a blocking monoclonal antibody that specifically targets human TSHR. The fully human TSH receptor-blocking monoclonal antibodies specifically bind to a TSHR, and effectively block signal transduction after a TSH binds to a receptor, inhibit the synthetic secretion of thyroid hormones, and significantly reduce and inhibit TSHR expression and fibrosis in orbital fibroblasts of effector cells associated with thyroid-related ocular diseases. The fully human TSH receptor-blocking monoclonal antibodies have broad application prospects in the treatment of Graves' disease (GD) and other diseases caused by hyperthyroidism, such as thyroid eye disease (TED).

This invention relates to the technical field of ultrasound image feature extraction for thyroid nodules, specifically to a method and system for ultrasound image feature extraction of thyroid nodules. The method includes the following steps: acquiring an ultrasound image containing thyroid nodules; performing multi-scale image analysis on the ultrasound image to construct a multi-scale image representation of the ultrasound image; using the multi-scale image representation to identify low-echo regions in the ultrasound image, and combining feature analysis, judgment correction, and spatial relationship verification to determine the acoustic shadowing region in the ultrasound image; performing suppression processing on the acoustic shadowing region, wherein the suppression processing includes unifying the signal within the acoustic shadowing region and softening the boundary of the acoustic shadowing region to obtain an optimized image; identifying the contour of the thyroid nodule based on the optimized image; and quantifying the morphology and internal structure of the thyroid nodule based on its contour. This application can effectively identify and suppress acoustic shadowing regions in ultrasound images.

The invention provides a thyroid nodule auxiliary diagnosis model construction method and system based on multi-modal data and a storage medium. The method comprises the following steps: acquiring multi-modal thyroid nodule medical data; performing classification processing on the thyroid nodule medical data of each mode by using a single-mode classification model to obtain each thyroid nodule intermediate classification result corresponding to the thyroid nodule medical data of each mode; and carrying out weighted fusion on all the thyroid nodule intermediate classification results to obtain thyroid nodule benign and malignant classification results based on the multi-modal thyroid nodule medical data. According to the embodiment of the invention, the single-mode classification model is adopted to perform classification processing on the medical data of each mode, so that the step of converting the multi-mode data into the structured data is omitted, and the data processing efficiency is higher; in addition, the thyroid nodules can be diagnosed, monitored and treated, clinicians are assisted in improving the diagnosis efficiency of the thyroid nodules, and clinical burdens are reduced.

The invention discloses an MRI (Magnetic Resonance Imaging)-based thyroid eye disease activity assessment method and system, and aims to solve the problems that the deep state is difficult to assess and the subjectivity is strong in the existing clinical activity score. The method comprises the following steps: acquiring and preprocessing a magnetic resonance image and generating a standardized three-dimensional image; automatically segmenting an orbit structure through a deep learning network; extracting multi-parameter image features; calculating a disease activity probability score by using a machine learning classifier; and mapping the probability score into an activity grading result and generating an evaluation report. The system comprises an image data acquisition and preprocessing module, an orbit structure automatic segmentation module, a multi-parameter image feature extraction module, an activity evaluation model construction and reasoning module and a clinical report generation module. According to the technical scheme, image evaluation efficiency and repeatability can be remarkably improved, subjective deviation is reduced, active-stage inflammatory edema and inactive-stage fibrosis are accurately and quantitatively distinguished, evaluation specificity is improved, and support is provided for individualized treatment.

The invention provides a thyroid nodule detection system, and the system comprises a receiving module which is configured to receive a thyroid ultrasound video; the multi-target instance segmentation module is configured to perform frame-by-frame detection on target objects in each video frame through a trained target instance segmentation model to obtain detection data of each target object in each video frame, the detection data comprises a detection frame and confidence, and each target object comprises a carotid artery, a thyroid gland and a thyroid nodule; the instance tracking module is configured to track each target object in the thyroid ultrasound video by adopting a multi-target tracking algorithm based on target detection based on each detection data to obtain each tracking trajectory of each target object; and the determination module is configured to determine a target thyroid nodule detection result corresponding to the thyroid ultrasound video based on each tracking trajectory of each target object. According to the invention, the video can be processed for an end-to-end screening scene, and the detection accuracy is improved.

An apparatus and a method for enabling a quantitative thyroid SPECT without CT using a neural network, according to one embodiment, are disclosed. The apparatus for enabling a quantitative thyroid SPECT without CT using a neural network, according to one embodiment, comprises: a generation unit for generating an attenuation map by inputting a first single photon emission computed tomography (SPECT) image and a second SPECT image of the thyroid into a first model; a segmentation unit for segmenting a thyroid map by inputting the attenuation map and the first SPECT image into a second model; and a calculation unit for calculating the radiopharmaceutical uptake amount of the thyroid by using the first SPECT images sinogram, the second SPECT images sinogram, the attenuation map, and the thyroid map.

The application discloses a kind of based on mixed domain enhancement's thyroid nodule semi-supervised segmentation method and device, it is related to image segmentation technical field.The method includes: constructing semi-supervised segmentation model, including unified bidirectional copy and paste enhancement module, cross-teaching model, patch enhancement module based on information entropy and frequency domain enhancement module based on Fourier transform;Using labeled data and unlabeled data to train the model constructed, by unified bidirectional copy and paste, generate intermediate sample between labeled data and unlabeled data in space domain;Using the uncertainty estimation based on information entropy, exchange low-entropy patch in labeled data and high-entropy patch in unlabeled data;The low-frequency component of labeled data and unlabeled data is exchanged by fast Fourier transform, and the low-frequency prediction is supervised in consistency based on the pseudo-label generated based on full frequency;The thyroid nodule ultrasound image to be segmented is input into the trained segmentation model, and the nodule segmentation result is obtained.The present application can improve segmentation accuracy.

This application provides a multimodal ultrasound intelligent identification method and precision diagnosis system for thyroid lesions. The method determines image data blocks with multimodal spatial registration and grayscale-contrast temporal alignment. Based on the grayscale ultrasound image sequence in the image data blocks, a first depth feature map of the target user's thyroid region is determined. A second depth feature map of the target user's thyroid region is determined based on the elastography map in the image data blocks. The hemodynamic spatial distribution characteristics of the parametric map in the image data blocks are captured to determine a third depth feature map of the target user's thyroid region. Cross-modal fusion is performed on the first, second, and third depth feature maps, and then the lesion segmentation mask, benign / malignant probability value, and risk classification of the target user's thyroid region are inferred and output in parallel. Using the scheme of this application, high-precision spatiotemporal alignment and cross-modal depth feature fusion of multimodal ultrasound images can be achieved to complete end-to-end intelligent identification of thyroid lesions.

The invention relates to the technical field of medical image processing, in particular to a thyroid nodule ultrasonic detection device based on masks and dense prompts, which comprises a MaskSAM module and a training strategy, the invention firstly proposes that a prediction result of an auxiliary module is converted into sparse prompts and dense prompts by freezing the weight of the auxiliary module, and the sparse prompts and the dense prompts are combined to obtain a sparse prompt and a dense prompt. The method provides more prompt information for the model, improves the segmentation performance of the model, and achieves the mutual cooperation of a backbone network, a cascaded pixel encoder, a Transfomer encoder based on mask attention, a prompt encoder, a mask decoder and an auxiliary module. A mask attention mechanism is utilized to focus on a thyroid nodule area. On the multiple performance indexes, the image segmentation effect of thyroid nodule ultrasound is greatly improved, the model can adapt to thyroid nodule ultrasound images with different resolutions and complexities, and robustness is enhanced.

This invention relates to the field of ultrasound image analysis technology, specifically to a method for classifying and rating thyroid images. The method includes: acquiring thyroid images from a patient; preprocessing the thyroid images to obtain preprocessed images; inputting the preprocessed images into a feature extraction network to obtain nodule extraction features; and feeding the nodule extraction features into corresponding classifiers to obtain corresponding evaluation results. Addressing the problem that existing thyroid nodule identification models are black-box and lack interpretability in assisted diagnosis, this invention introduces a specific feature extraction network to extract nodule features and echogenicity features across multiple dimensions. Each type of feature is labeled to facilitate subsequent diagnosis by doctors, and the output is based on relatively sufficient evidence and has strong interpretability.

This utility model belongs to the field of medical auxiliary device technology, specifically an internal auxiliary traction device for endoscopic thyroid surgery. It includes two support arms and a limiting component. The rear ends of the two support arms are screwed together. The limiting component is located between the two support arms, with one end screwed to the middle of one support arm. The limiting component has multiple slots for fixing, and correspondingly, the other support arm has a locking slot that matches the slots. By engaging the locking slots in different slots on the limiting component, the two support arms maintain different opening angles. The outer side of the front end of each support arm has a contact portion for contacting the area requiring traction to expose the surgical field. The support arm also has a clearance opening to facilitate the movement of the limiting component and the folding of the device. The overall structure of this utility model is simple, foldable for easy storage, and does not require skin puncture, thus avoiding additional damage.

Disclosed herein are pharmaceutical compositions for the treatment of neurologic disorders comprising an effective amount of one or more mTOR inhibitors and / or one or more BDNF inducers and optionally an effective amount of one or more thyroid hormones. Further disclosed herein are methods of treating or preventing neurologic disorders comprising administering an effective amount of a pharmaceutical composition comprising one or more mTOR inhibitors and / or one or more BDNF inducers and optionally an effective amount of one or more thyroid hormones.

The application provides an AI-assisted thyroid scanning system and method, and relates to the technical field of ultrasonic examination.The application comprises the following steps: acquiring a video stream, dividing the video stream into multiple video clips in chronological order and associating time intervals, extracting optimal thyroid ultrasonic images from the video clips, uploading the optimal thyroid ultrasonic images to a cloud analysis system to obtain corresponding cloud analysis results, and distributing each video clip to multiple AI analysis lightweight models to obtain corresponding local analysis results; establishing a cache queue, adding video clips that have obtained local analysis results and cloud analysis results to the cache queue, adjusting the order, and then sequentially outputting each video clip and analysis result in the cache queue.The beneficial effects are as follows: the cost is reduced, the popularity of the system is improved, the real-time performance is improved, the data transmission pressure is reduced, the delay of diagnosis results caused by poor network conditions is reduced, and the reliability of diagnosis results is improved.

This invention relates to the field of image analysis technology, specifically to a method for prognostic assessment of breast and thyroid diseases combined with AI analysis of pathological images. The method includes the following steps: acquiring pathological slide images and performing grayscale statistics to generate tissue coordinate data; calculating the edge gradient and contour features of pixel neighborhoods; quantifying the morphological aberration features of cell nuclei and constructing a nuclear population potential field; analyzing the potential difference and gradient inversion regions; correcting directional consistency based on the gradient magnitude distribution direction; and finally generating a disease prognostic assessment result. In this invention, stable tissues are screened and coordinate data is generated through grayscale statistics and threshold segmentation of pathological slide images; edge gradients are calculated and closed contours are extracted; cell nucleus axis ratios and contour roughness are quantified; a regular grid is established and distance attenuation is introduced to form a nuclear population potential field; and the potential difference and gradient inversion regions are statistically analyzed and directional consistency is corrected, thereby improving the stability and consistency of the prognostic assessment.

The application relates to the technical field of medical data processing, and discloses a thyroid disease management and health data statistical analysis system. A physiological kinetics model containing a drug metabolism and endogenous secretion mechanism is constructed, discrete detection data are filled in time sequence blanks by using an unscented Kalman filtering algorithm, a continuous physiological panorama conforming to a human mass conservation law and a neural humoral regulation mechanism is generated, a time lag causal tensor construction mechanism with an adaptive causal window is introduced into the system, nonlinear lagging effects of hormone fluctuations on nodule morphological evolution are accurately captured, and deep causal chains of pathological causes and morphological results are restored, finally, an entropy-driven dynamic scheduling module based on information thermodynamics replaces a traditional fixed follow-up mode, a Lyapunov cognitive failure critical point is calculated as an optimal review time by quantizing an uncertainty accumulation process of a disease condition prediction and combining a risk adaptive mechanism.

This application provides a YOLOv11-based multimodal detection system and method for papillary thyroid carcinoma cells, relating to the field of image detection technology. It addresses the problem in existing technologies of achieving both high accuracy and robustness in papillary thyroid carcinoma cell detection under limited computing power. The system specifically includes: an improved backbone network that extracts first-modal features from images at different scales by introducing C3k2_Faster and C2MSLA modules; a priori pyramid module for STEM processing and progressive downsampling of the image, and outputs second-modal features aligned with the first-modal features at different scales through convolutional projection; a gated fusion module for channel-level weighted fusion of the first and second-modal features at corresponding scales, outputting fused features; a neck module for transferring and aggregating the fused features; and a head module for outputting the target detection results. This application is used for detecting papillary thyroid carcinoma cells.