186 results about "Abnormal thyroid" patented technology

The invention discloses an automatic diagnosis method of a thyroid pathological image, and belongs to the technical field of medical pathological image auxiliary intelligent diagnosis, and the method comprises the following steps: constructing a thyroid tumor pathological image data set, preprocessing a pathological image, carrying out multi-scale fusion, carrying out feature extraction, carrying out feature fusion, inputting a CLAM-SB multi-instance learning model, and generating a visual heat map. The invention provides a thyroid pathology image automatic diagnosis method based on weak supervision multi-instance learning and multi-scale feature fusion, which can effectively improve the accuracy of pathology tissue region segmentation and pathology image classification, reduce the workload of pathologists and improve the diagnosis efficiency and reliability.

The invention discloses a thyroid ultrasound image diagnosis method based on deep learning, and the method comprises the following steps: collecting a thyroid ultrasound original image set, and carrying out the preprocessing; performing focus segmentation on the standardized thyroid ultrasound image set; performing morphological constraint and boundary refinement; calculating the blood flow direction, blood flow velocity and blood flow power of each thyroid focus area and neighborhood; generating a preliminary fusion feature map based on a feature adaptive deep learning network, and fusing the preliminary fusion feature map with the thyroid focus blood flow feature vector set; obtaining a thyroid focus detection list through thyroid focus benign and malignant discrimination branches; and generating thyroid focus structured diagnosis data records based on the thyroid focus detection list, and writing the thyroid focus structured diagnosis data records into a computer-aided diagnosis system. According to the method, deep learning and multi-modal blood flow features are fused, intelligent diagnosis of the thyroid focus is achieved, and the method has the advantages of being high in precision, high in interference resistance and structured in result.

The invention discloses a thyroid image segmentation method and system for obtaining multi-scale feature fusion based on UNet and deformable convolution, and belongs to the technical field of medical image processing and computer vision. A model DAmUNet of a deformable convolution and multi-scale attention feature fusion module is introduced into a UNet network architecture, the deformable convolution is adopted in an encoder and a decoder to replace traditional convolution, in the deepest layer of the encoder, the multi-scale attention feature fusion module is adopted, encoding features of the encoder with different resolutions are used as input, and the multi-scale attention feature fusion module is used as input. Channel weighting is carried out on the deepest layer coding features through a double-input channel attention mechanism to generate fusion features; and taking the fusion features as decoder input, and finally obtaining a pixel-level segmentation result of the thyroid region. The method effectively improves the segmentation precision and robustness, is excellent in Dice, IoU and other indexes, and can be widely applied to the scenes of thyroid volume calculation, preoperative evaluation and the like.

The invention provides a thyroid ultrasonic signal intelligent analysis and diagnosis method and system, and relates to the technical field of ultrasonic signal analysis, and the method comprises the steps: calculating different depth noise distribution, automatically adjusting a filtering parameter value, and carrying out the filtering through anisotropic diffusion; segmenting the processed image to obtain a nodule region; acoustic characteristic data, acoustic characteristic parameter data and image characteristic data are extracted to form a comprehensive characteristic vector, and dimension reduction is carried out; and calculating a feature association weight for optimization to obtain a classification result, and generating a diagnosis report. The thyroid ultrasound image analysis precision is improved, and intelligent diagnosis of benign and malignant nodules is achieved.

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 image nodule segmentation method based on spatio-temporal characteristics and frequency enhancement, and relates to the technical field of medical image processing, and the method comprises the following specific steps: multi-scale characteristic extraction: carrying out the gray normalization and median filtering preprocessing of a thyroid ultrasound image, and carrying out the segmentation of the thyroid ultrasound image nodule; extracting multi-scale features under different resolutions by using a pre-trained backbone network, obtaining the multi-scale features under different resolutions by the backbone network through multilayer convolution and pooling operation, and generating basic and advanced features through convolution operation; by designing an intra-frame feature extraction architecture based on frequency feature fusion and an efficient spatial-temporal feature aggregation mechanism, intra-frame and inter-frame collaborative optimization is realized, intra-frame and inter-frame high and low frequency features are solved by means of octave convolution, the perceptual ability of a model to ultrasonic image details and semantic information is improved, inter-frame and inter-frame feature fusion is realized, and the perceptual ability of the model to ultrasonic image details and semantic information is improved. A local circulation neighborhood propagation mechanism is adopted, and interference of thyroid movement on time sequence feature extraction is effectively restrained.

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 present disclosure provides lysosomal targeting bifunctional molecules that target disease causing anti-TSHR autoantibodies for degradation. The lysosomal targeting bifunctional molecules include a ligand moiety that specifically binds to an asialoglycoprotein receptor (ASGPR), and which is linked via a carrier protein to a thyroid stimulating hormone receptor (TSHR) polypeptide that specifically binds target anti-TSHR autoantibodies.

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-related eye disease auxiliary verification method based on SPECT / CT and federal learning. The thyroid-related eye disease auxiliary verification method comprises the steps that A, image data standardization and orbit area extraction are carried out; b, extracting a preliminary segmentation mask of each rectus in the CT image; c, fusing the structure prior mask, and obtaining an extraocular muscle fine segmentation mask through multi-mode collaborative segmentation; d, utilizing extraocular muscle fine segmentation masks, SPECT and CT images, fusing segmentation prior and multi-modal features to perform deep discrimination, performing automatic prediction of thyroid-related eye disease activity staging, and outputting a prediction probability; and E, based on a dynamic model combination strategy of style similarity and uncertainty quantization, obtaining a dynamic combination model of segmentation and classification adapted to new domain feature distribution, and predicting TAO activity. According to the method, accurate segmentation of extraocular muscle structures and accurate prediction of TAO activity in an invisible data center can be realized, auxiliary verification data support is provided for diagnosis of thyroid-related eye diseases, and the diagnosis accuracy is improved.

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.

An ETE detection method is disclosed. The ETE detection method according to the present invention comprises the steps of: providing an ultrasound image to a first learning model pre-trained to extract a thyroid region, thereby acquiring a first boundary of the thyroid, and providing the ultrasound image to a second learning model pre-trained to extract a nodule region, thereby acquiring a second boundary of a nodule; and outputting a superimposed image in which the first boundary and the second boundary overlap, thereby enabling ETE detection.

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 a thyroid nodule home intervention system and method based on AR navigation and DRL, and relates to the technical field of thyroid nodule home management and intervention.The thyroid nodule home intervention system based on AR navigation and DRL comprises a client module arranged on a user mobile terminal and used for carrying out thyroid nodule home intervention on a thyroid nodule; the guiding module is used for guiding a user to perform self-operation in a home environment, collecting data and receiving feedback; the cloud service module is in communication connection with the client module through the Internet and is used for remote data processing, model operation and personalized intervention scheme management; according to the thyroid nodule home management system, an innovative system of thyroid nodule home management is constructed through cooperation of multiple modules, that is, the maximum acupuncture point positioning error is 3 mm through an AR navigation unit of a client by means of facial feature point fitting and a neck statistical shape model, and the problem that acupuncture points are not found accurately in traditional home operation is solved.

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.

1. The name of the design product: the thyroid postoperative neck protector. 2. The use of the design product: for the thyroid postoperative neck care. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective view 1.

The application belongs to the technical field of thyroid image segmentation, and specifically discloses a thyroid automatic segmentation method and system based on uncertain region perception. The method is used for pre-processing an original ultrasound image, constructing a double-resolution network model by using a deep learning library, training the double-resolution network model, obtaining an optimized double-resolution network model, inputting the pre-processed ultrasound image into the optimized double-resolution network model, and obtaining segmented feature information. A convolutional pyramid uncertain region identification module is constructed, the obtained feature information is input into the convolutional pyramid uncertain region identification module, a missed region in the feature information is repaired, and a multi-region is suppressed. The features output by the convolutional pyramid uncertain region identification module are input into a residual module, and final thyroid segmentation features are obtained. By using the technical scheme, the missed region is repaired, the multi-region is suppressed, and the segmentation accuracy is improved.

The invention relates to the technical field of image detection, and discloses a thyroid postoperative hematoma early warning method, system and terminal based on multi-modal imaging, and the method comprises the steps: obtaining a thyroid postoperative image, constructing a coordinate system, and defining the first swelling information of a target patient; analyzing the thyroid postoperative image by using a skin color compensation algorithm to obtain second swelling information of the target patient; obtaining third swelling information according to the blood flow velocity of the target patient; and performing time alignment processing on all the swelling information to obtain a plurality of hematoma early warning information. According to the system, multiple imaging modes such as ultrasonic imaging and optical imaging are combined, real-time monitoring and intelligent early warning are conducted on hematoma after the thyroid surgery, the size, position and form of the hematoma can be accurately judged by fusing and analyzing multi-mode information, then hierarchical decision-making of the hematoma is achieved, accurate auxiliary diagnosis information is provided for doctors, and the diagnosis efficiency is improved. The accuracy and timeliness of thyroid postoperative hematoma monitoring are remarkably improved, complications are reduced, and the rehabilitation quality of patients is improved.

The application relates to the technical field of data processing, in particular to a thyroid nodule fine-grained automatic identification and classification system based on weak supervision learning, which comprises a server, the server is pre-set with a thyroid fine-grained identification model, the server is used for acquiring an identification image, and a fine-grained identification result output by the thyroid fine-grained identification model according to the identification image; the thyroid fine-grained identification model comprises: a feature pyramid attention module, which is used for processing the identification image according to a feature pyramid attention algorithm; a spatial attention module, which is used for acquiring a binary mask, generating an attention map according to the binary mask, and positioning and discriminating a region; and a multi-network fusion module, which is used for acquiring output results of multiple backbone networks, fusing the output results, and generating a fine-grained identification result. The scheme can solve the technical problem that, when the thyroid nodule is classified in a fine-grained manner in the prior art, the classification accuracy is low due to the multi-scale and multi-network structure of an ultrasonic image.

The application discloses a thyroid disease prediction method based on machine learning, which comprises the following steps: S1, acquiring relevant sample data of a patient; S2, preprocessing the sample data to obtain processed sample data; S3, constructing a thyroid disease prediction model by using a LightGBM algorithm according to the processed sample data; S4, gradually reducing indexes and evaluating them on a test set to obtain a minimum index set; S5, reconstructing a model according to the index set; S6, analyzing test set data by using the model to obtain an analysis result; S7, judging whether the analysis result meets a preset condition, if yes, proceeding to step S8, otherwise, returning to step S1; S8, analyzing data of a to-be-detected person by using the model to obtain a prediction result; and S9, constructing a three-dimensional reference system of FT3, FT4 and TSH. The application can solve the problem of low accuracy of an existing thyroid disease prediction model and improve the three-dimensional reference system of FT3, FT4 and TSH.

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 method and system for processing thyroid function data based on a spectrum technology, and relates to the technical field of artificial intelligence, and the method comprises the steps: collecting a face video through employing a near infrared spectrum imaging technology, determining a face core region in the face video, and obtaining a face core region; the method comprises the following steps: capturing an absorption energy spectrum characteristic value of a human body trace substance in a human face core area to specific wave band light, obtaining spectrum original data associated with thyroid function, and synchronously recording age information of an acquisition object; on the basis of the preset rule, in combination with the age information of the collection object and the substance detection index related to the thyroid function, the thyroid function state of the collection object is graded and judged to obtain the graded judgment result, and the problem that thyroid function data cannot be processed based on the spectrum technology in the prior art is solved.

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.

The application relates to the technical field of ultrasonic image analysis, and discloses a thyroid nodule evaluation system based on ultrasonic images. The system obtains medical record data and medical examination data of a patient through a multidimensional acquisition module, classifies and forms a data set, analyzes the thyroid function state of each patient through an intelligent evaluation module, generates a function coefficient, analyzes the picture quality of each group of ultrasonic images again, generates corresponding detection data group Jcsj, establishes a unified evaluation standard, quickly screens out key frames from a large amount of ultrasonic image data, has high multidimensional integration efficiency, sets a fixed range threshold value for the intelligent evaluation module, analyzes and generates a nodule volume and a nodule score in combination with a medical data set, and in combination with the function coefficient, the detection data group and the nodule score, evaluates the risk grade of the thyroid nodule and the picture quality of the ultrasonic image of the patient, screens out key frames of the ultrasonic image, and outputs a treatment suggestion; and the intelligent evaluation has high precision.