7 results about "Thyroid disease" patented technology

The present application provides a novel thyroid hormone β receptor agonist of formula (I) having better activity, selectivity or safety and the use thereof for preventing or treating diseases related to the action of β receptor agonists, including, for example, obesity, hyperlipidemia, hypercholesterolemia, diabetes, liver diseases (fatty liver, NASH, NAFLD, etc.), cardiovascular diseases (atherosclerosis, etc.), thyroid diseases (hypothyroidism, thyroid cancer, etc.), and the like.

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.

The present application belongs to the technical field of medical image intelligent analysis and auxiliary diagnosis, and specifically discloses a thyroid nodule auxiliary diagnosis method and system based on multi-modal fusion, which is used for intelligent classification and auxiliary diagnosis of thyroid nodules. The method comprises the following steps: collecting and labeling medical image data and clinical structured data; standardizing and normalizing the data for pretreatment; constructing an image feature extraction branch and a clinical feature extraction branch to extract the feature representation of each modality; jointly modeling the image features and the clinical features through a strip perception module (SAC) to form a fused clinical-image context representation; generating channel-level adaptive parameters through a feature-level linear modulation method to dynamically adjust the image features individually; and finally completing the classification of thyroid nodules based on the fused multi-modal features. The present application realizes an image feature modeling method involving clinical structured data, which enables dynamic fusion of patient individualized clinical background for image interpretation, and provides more comprehensive, stable and reliable technical support for computer-aided diagnosis of thyroid diseases.

This invention discloses a method and system for automatically screening thyroid diseases using infrared thermal images, belonging to the application of deep learning methods in medical diagnosis within artificial intelligence technology. Based on infrared thermal images and combined with deep learning methods, this invention provides a thyroid disease screening method. This method automatically assesses the risk level of thyroid diseases in examinees based on acquired infrared thermal images of their thyroid glands, improving screening efficiency, providing doctors with objective and accurate screening results, and guiding them in selecting appropriate treatment plans. Furthermore, based on this method for automatically screening thyroid diseases using infrared thermal images, this invention designs a thyroid disease screening system that facilitates the visualization of screening results and the generation of screening result reports.

This invention provides a multimodal fusion diagnostic method and system for thyroid diseases based on artificial intelligence, belonging to the field of artificial intelligence and intelligent diagnostic technology. The method involves acquiring and preprocessing ultrasound, CT, photoacoustic images, and clinical biochemical data. A dual-branch feature extraction network is used to extract deep structural feature vectors and clinical feature vectors from the images. A corrected image is generated through image geometric line analysis, cross-modal fusion, and clinical feature correction, and a first comprehensive feature vector is extracted. Deep image features are then fused to obtain a second comprehensive feature vector. Based on the clinical feature vector, the difference type between the two vectors is determined and approximated. A fourth comprehensive feature vector is then input into a diagnostic model to output the result. This invention improves the comprehensiveness and accuracy of thyroid disease diagnosis and reduces reliance on physician experience through deep fusion of multimodal data, enhancement of lesion features, and precise reconciliation of differences.

The present invention relates to: an imaging composition comprising a fluorine-18-labeled trifluoroacetic acid; and a use thereof for diagnosing solid cancers or thyroid diseases.