720 results about "Dermatological diseases" patented technology

The invention discloses an efficient skin disease segmentation method based on multiple scales and a mixed attention mechanism, and the method comprises the steps: firstly carrying out the preprocessing of a skin mirror image through data enhancement, and then inputting the skin mirror image into a convolutional neural network for feature extraction, thereby obtaining a skin lesion segmentation region prediction map. According to the method, the convolutional neural network is applied, multi-scale features of local information and global information can be extracted from a skin mirror image, and the feature representation capability is enhanced through feature fusion. The objective of the invention is to solve the problems of changeable lesion areas, irregular shapes and the like of skin disease images, and improve the accuracy and robustness of skin disease image segmentation.

The invention relates to the technical field of skin disease health evaluation, in particular to a skin disease health evaluation system integrating multiple clinical evaluation tools. Comprising a patient information management and dynamic risk assessment module, a skin microbiome analysis module, a symptom quantification and assessment module, a physiological index monitoring module, an environmental adaptability monitoring module, an intelligent decision support module and a feedback and optimization module. The method comprises the steps of performing dynamic risk assessment in combination with a machine learning algorithm, performing quantitative assessment on the skin health state of a patient in combination with microbiome analysis and real-time physiological index monitoring, performing quantitative assessment on symptoms through an image analysis technology and a standardized questionnaire, and generating a personalized health assessment report and a treatment scheme by using a deep neural network and an optimization algorithm. Continuously improved closed-loop management is realized through a feedback optimization module; according to the invention, not only is a scientific evaluation basis provided for doctors, but also a patient can obtain customized treatment suggestions.

The invention discloses a deep interpretable skin disease identification method based on small sample learning, which belongs to the field of image processing, and comprises the following steps of: respectively inputting similar or heterogeneous skin disease images into a double-branch small sample contrast learning network, segmenting a lesion area of the skin disease image to generate lesion segmentation masks, extracting shape information in the lesion segmentation masks, and identifying the shape information of the lesion segmentation masks; positioning a minimum square frame containing a lesion area, and performing clipping and zooming to obtain a lesion image; extracting the depth features of the branches corresponding to the lesion images of the two branches, calculating the contrast loss of the depth features of the double branches to optimize network parameters, and extracting multi-scale texture information at the same time; metadata containing shape information and texture information and depth features of an image are subjected to multi-modal fusion for skin disease identification, and through combination of a multi-stage deep learning network and medical diagnosis logic, features such as form and texture of a lesion area are extracted and fused step by step. The classification accuracy of skin disease identification and the interpretability of the model are improved.

The invention provides application of UBR5 in preparation of drugs for treating pigmented skin diseases, and further the pigmented skin diseases comprise various types of pigmented nevus (mainly congenital pigmented nevus and megalous with the risk of malignant transformation). Various pigmented spots (mainly chloasma, freckles, Taitian nevus, post-inflammation hyperpigmentation and the like) and hypopigmentation / depigmentation spots (mainly leucoderma, post-inflammation hypopigmentation and the like). Experimental results show that UBR5 is highly expressed in disease tissues such as pigmented nevus. The UBR5 expression level and the melanogenesis ability are in significant positive correlation, targeted knockout / knockdown of UBR5 in melanocytes can significantly inhibit the expression level of melanogenesis related genes such as MITF, TRP1, TRP2, TYR and other proteins, the melanin content of melanocytes and tyrosinase activity, vice versa, and the melanogenesis ability of melanocytes after overexpression of UBR5 is significantly increased.

The invention discloses a virulence-attenuated bovine nodular skin disease virus LSDV DQ and application thereof, and belongs to the technical field of attenuated strains. The invention aims to provide a bovine nodular skin disease virus vaccine strain which is sufficient in attenuation and good in immune effect. The invention provides a virulence-attenuated bovine nodular skin disease virus strain, which is named as sheep pox virus LSDV-DQ, has the preservation number of CCTCC NO: V2024113, and is preserved in the China Center for Type Culture Collection on December 2, 2024. The vaccine strain provided by the invention can be used as a vaccine candidate strain for safely and effectively preventing and controlling bovine nodular dermatosis.

The invention provides a dermatoscope image segmentation method based on an attention mechanism and multi-scale information interaction, and belongs to the technical field of medical image segmentation. The technical problems that a traditional segmentation method is inaccurate in skin lesion area boundary recognition, insufficient in multi-scale feature extraction capacity and large in noise interference influence are solved. According to the technical scheme, the method comprises the following steps of 1, constructing a high-precision skin disease image data set; step 2, performing enhancement and standardization processing on the data set; 3, constructing a segmentation model based on an attention mechanism and multi-scale information interaction; 4, training the segmentation network by using the preprocessed data set, and optimizing the model performance by using a BCE-Dice combination loss function; and 5, inputting a test set to evaluate the performance of the model, and finally outputting an optimal segmentation result. The method has the advantages of being high in segmentation precision, high in boundary recognition capability and excellent in model robustness.

The invention discloses a skin focus segmentation method based on Mama edge information and an attention mechanism, and the method comprises the steps: S1, obtaining a data set, and dividing the data set; s2, connecting an encoder and a decoder in a jumping connection mode, and introducing edge information and an attention mechanism to construct an SMEANet network for skin focus segmentation; training parameters are set, and model training is carried out to obtain an optimal model; and S3, inputting the to-be-detected data into the Mama-based edge information and attention mechanism skin focus segmentation model to realize segmentation, and finally outputting a segmentation result. According to the segmentation method, the encoder and the decoder are connected in a jumping connection mode, edge information and an attention mechanism are introduced, the SMEANet network for skin focus segmentation is constructed, the accuracy of skin focus segmentation is improved, and therefore a more reliable basis is provided for clinical diagnosis.

The invention discloses an application of betulinic acid 28-O-beta-D-glucoside (BA-6 for short) in preparation of a medicine for preventing and treating skin inflammation. The problems that an existing medicine for treating dermatitis is limited in curative effect, slow in effect taking, multiple in side effect, high in long-term use risk, large in individual difference, high in treatment cost, likely to cause immunoreaction and the like are solved. The invention discloses application of BA-6 in prevention and treatment of atopic dermatitis, contact dermatitis, eczema, rash, psoriasis, lupus erythematosus, papule squamous skin disease, pityriasis rosea, seborrheic dermatitis, pemphigus, celestial abscess, acne, tinea corporis, impetigo, SAPHO syndrome, hidradenitis suppurativa, dermatitis caused by drugs, lichen planus, pityriasis rubra pilaris, vitiligo, prurigo nodularis, dermatitis caused by drugs, and the like for the first time. The traditional Chinese medicine composition can be used for treating skin diseases, systemic sclerosis, hidradenitis suppurativa, granulomatous skin diseases, vitiligo, dermatomyositis, chronic skin itch and alopecia areata, and has the technical progress of obviously improving skin erythema, papule, edema, scale, moss and ulceration symptoms.

Provided are methods and composition for treating skin barrier functions and associated disorders. Formulated compositions may be applied on human skin to maintain a desired skin surface pH over an extended period of time without irritating the skin. Maintaining the desired surface pH aids in treatment of eczema, in addition to other skin conditions.

Provided herein is a lipid nanoparticle (LNP) composition for the localized treatment of skin diseases and a method of treating skin diseases with the LNP composition.

The invention discloses a multi-modal image fusion method and system for skin disease diagnosis, and belongs to the technical field of medical image processing, the system comprises a computer readable storage medium, the computer readable storage medium is provided with a de-noising processing unit, the de-noising processing unit is connected with an image alignment unit, and the image alignment unit is connected with the computer readable storage medium. The image alignment unit is connected with the scale normalization unit, the scale normalization unit is connected with the feature extraction unit, the feature extraction unit is connected with the feature fusion unit, and the feature fusion unit is connected with the classification and diagnosis unit. According to the multi-modal image fusion method and system for skin disease diagnosis, the advantages of all technologies are fused, more comprehensive and accurate diagnosis of skin diseases is achieved through multi-level and multi-angle image information comprehensive analysis, the diagnosis accuracy can be improved, and the diagnosis efficiency is improved. The time for multiple examinations of the patient can be shortened, and the utilization efficiency of medical resources is improved.

Disclosed is a topical anhydrous, homogenous pharmaceutical composition for treating skin disorder and wounds comprising cod liver oil, hempseed oil, monolaurin, medium chain triglycerides (“MCT”), free fatty acids, sea salt, and vegetable oil selected from the group consisting of red palm concentrate, coconut oil, and red palm oil and combination thereof and optionally fish collagen, ascorbyl palmitate, thickening agent, such as beeswax, red palm concentrate and cetyl ester.

The invention relates to the technical field of medical blood inventory management, in particular to a blood product intelligent inventory management method and system based on medical big data. The method comprises the following steps of generating historical treatment desensitization record data through collection and desensitization processing of historical treatment record data of the dermatology department, and further extracting treatment dosage data of skin disease blood products among different cases. And based on the desensitized historical data, carrying out recovery capability difference simulation evaluation, generating recovery capability difference evaluation data, and predicting future usage demand data based on the recovery capability difference evaluation data. Then, pre-allocation optimization is carried out on the current inventory in combination with the prediction data, and pre-inventory allocation optimization data is generated; and finally, performing automatic management architecture design on the optimized data, and importing the management architecture into a medical management system. The medical blood inventory management technology is optimized, so that the medical blood inventory management technology is more perfect.

The invention discloses a skin disease diagnosis method based on image recognition, and the method comprises the following steps: obtaining a to-be-detected skin image, and recording an equipment identifier; carrying out white balance correction, reflection suppression, hair suppression and scale normalization to generate a standardized image; positioning the focus to generate a focus mask; generating an enhanced view set in the focus mask by the enhanced view set according to the enhanced times and the enhanced view set; calculating a color invariant graph, a sign response graph and an artifact confidence graph, and synthesizing a potential energy field set according to a fusion weight; calculating a local structure tensor and generating a Riemannian metric matrix; constructing an improved Morse-Smal complex under a Riemannian metric matrix, carrying out cross-complex clustering on the critical point set to obtain a stable score, carrying out complex simplification to generate a stable Morse-Smal complex, and calculating the confidence coefficient of the cell boundary; and extracting structured feature vectors, diagnosing, judging and outputting disease categories, grading results and re-checking and marking. According to the invention, the anti-artifact and cross-device stability is improved.

The invention provides a skin lesion auxiliary detection method and system based on multi-modal information fusion, and belongs to the technical field of computer vision and application. The method comprises the following steps: firstly, acquiring and constructing bimodal data including a dermatoscope image and a text of a patient, and preprocessing the data; secondly, respectively adopting an improved convolutional neural network and a text coding model, and extracting deep and high-dimensional features from the skin disease image and the self-described text of the patient; afterwards, an improved symmetric gating cross attention fusion module is constructed, fine-grained interaction and alignment between visual and text features are realized through a bidirectional cross attention mechanism, and a gating unit is utilized to adaptively adjust a fusion weight; and finally, inputting the deeply fused multi-modal feature vector into a classifier to obtain a confidence score of each skin lesion type so as to provide reference for dermatologists. According to the method, the diagnosis accuracy, robustness and interpretability are synergistically improved, and the method has great clinical application value.

The invention belongs to the technical field of medical image and big data analysis, and provides an automatic skin disease diagnosis method and system based on a multi-modal big language model, and the method comprises the steps: obtaining a dermatoscope diagnosis report of a historical patient, and extracting an original image and a diagnosis text in the dermatoscope diagnosis report; constructing a multi-modal large language model, wherein the multi-modal large language model comprises a visual encoder, a linear projection layer and a large language model; training the multi-modal large language model; obtaining an external medical knowledge base, extracting text information in the external medical knowledge base, and constructing a vector database based on the text information; in response to an input image and a query text of a user, N vectors with the highest similarity are retrieved in the vector database according to the query text, the retrieved vectors are decoded into a text, and the text and the query text jointly construct complete prompt word input; and based on the input image and cue word input, obtaining a final dermatoscope diagnosis report by using the trained multi-modal large language model. The diagnosis report is automatically generated.

The invention discloses a culture medium and a method for performing in-vitro 3D cell model construction on skin tissues. The invention provides a culture medium for constructing a skin tissue in-vitro 3D (three-dimensional) cell model. The culture medium is prepared from an antibacterial and antifungal agent tri-antibody, HEPES, GlutaMax, MEM non-essential amino acid, human recombinant protein EGF, bFGF, HGF, FGF-10, Wnt-3a, Noggin, R-spondin 1, IL-2, IL-15, IGF-I, CHIR99021, SB202190, A83-01, Primocin, N-acetyl-L-cysteine, nicotine, an N2 additive, B27, ITS-X, Y-27632, dexamethasone, calomel and a basal culture medium. The skin tissue in-vitro 3D cell model constructed by the invention has a wide application prospect in the fields of basic scientific research and clinical diagnosis and treatment of skin diseases.

The invention discloses a laser medical equipment control method and device, equipment and a storage medium, and belongs to the field of laser medical equipment. Comprises: an information acquisition module for acquiring an image of a pigmented skin disease of a patient and a result of a skin pigment analyzer; the auxiliary diagnosis module is used for obtaining the type of the pigmented skin disease of the patient through image classification; the laser control module obtains a proper laser spot size, a proper laser energy density and a proper laser pulse width through a machine learning method; the laser positioning module is used for positioning the position of the pigmented skin disease of the patient through target detection; the skin feedback module is used for detecting the skin of the laser treatment part in real time through a camera; the laser treatment module positions the affected part according to the laser positioning module and treats the pigmented skin disease according to the parameters set by the laser control module. According to the invention, the accuracy and safety of laser treatment are improved by adopting a machine learning method, and the operation difficulty of laser treatment is reduced.

The invention belongs to the technical field of image recognition, and particularly relates to a skin disease classification recognition system based on deep learning, which comprises a skin image acquisition module, a skin image preprocessing module, a skin state classification module and a state classification optimization module, according to the system, a skin state recognition model is constructed, and a multi-head self-attention mechanism is introduced, so that skin state differences can be distinguished; local features are extracted through a space exploration CNN block, an AssemFormer block learns a global dependency relationship, the local features and the global features are fused, and a lesion mode can be understood; scale variant attention blocks are used for highlighting skin features of suspected lesion areas, Monte Carlo attention blocks are used for carrying out random sampling pooling calculation on feature representation, attention map enhancement blocks are used for generating attention maps, skin state categories are output, and skin state prediction labels are generated for each skin image, so that skin lesion details can be highlighted; quantitative reference is provided for medical staff, and skin state judgment is effectively assisted.

The invention relates to the technical field of intelligent medical assistance, in particular to a skin disease treatment electrode positioning system based on potential distribution. The system comprises a potential gradient severe region identification module, a lesion minimum surrounding region identification module, a similar potential region grouping module, an optimal electrode position prediction module, an electric field distribution calculation module, a sweat gland dense region identification module, an optimal electrode placement point screening module, an enhanced feature generation module and a final electrode position determination module. By fusing multi-source information such as skin surface potential distribution, lesion geometric characteristics, tissue conductivity, historical treatment data and the like, systematic optimization is performed on electrode positioning in skin disease treatment, the effectiveness of electrode stimulation is maximized on the premise of ensuring the treatment safety, and the treatment efficiency is improved. An accurate electrode placement scheme is provided for treatment of different types of skin diseases, and the method is suitable for accurate determination of the electrode placement position in electrical stimulation treatment.

The invention provides a multi-enhancement semi-supervised medical image classification method based on a dynamic threshold value, and the method comprises the steps: obtaining a labeled image and a non-labeled image based on a skin disease image data set, building an initial semi-supervised image classification model through the labeled image and the non-labeled image based on deep learning, and carrying out the classification of the initial semi-supervised image. Respectively processing and merging the images with the labels and the images without the labels, obtaining a loss function based on a merging result, optimizing the initial semi-supervised image classification model based on the loss function to obtain a target semi-supervised image classification model, and evaluating and adjusting the target semi-supervised image classification model based on a performance evaluation index to obtain a target semi-supervised image classification model. The accuracy and generalization ability of the skin lesion image classification model are improved, and the dependence on large-scale manual annotation data is reduced by designing an effective learning mechanism, so that the annotation cost is reduced, the training efficiency is improved, the robustness of the model to the image data distribution change is enhanced, and the stable and consistent classification performance is kept.

The invention discloses a natural component composition for conditioning skin micro-ecology, a preparation method of the natural component composition and application of the natural component composition in skin care products for children. The composition comprises a synergistic combination of a sphingomonas fermentation product extract, a haematococcus pluvialis extract rich in astaxanthin, an abelmoschus esculentus fruit extract rich in polysaccharides, and a yeast / zinc fermentation product. The present invention provides improvements in astaxanthin in terms of chemical stability and transdermal permeability. The composition aims to enhance the immature skin barrier, provide a good effect, and support the healthy development of skin microecology, thereby solving the unique physiological needs of children's skin. The invention also describes a preparation method of the composition and application of the composition in cosmetics for children and dermatological products.

Disclosed is a topical anhydrous, homogenous pharmaceutical composition for treating skin disorder and wounds comprising cod liver oil, hempseed oil, monolaurin, medium chain triglycerides (“MCT”), free fatty acids, sea salt, and vegetable oil selected from the group consisting of red palm concentrate, coconut oil, and red palm oil and combination thereof and optionally fish collagen, ascorbyl palmitate, thickening agent, such as beeswax, red palm concentrate and cetyl ester.

The invention relates to the technical field of medical artificial intelligence and computer vision, in particular to a skin disease feature recognition method based on image processing, and the method comprises the following steps: S1, obtaining multi-modal data of a skin lesion region, including an RGB image, a thermal imaging image and a patient medical history text; s2, performing illumination correction, noise suppression and lesion region segmentation on the image data; the skin disease feature recognition system based on image processing comprises the following contents: a multispectral image acquisition module supporting synchronous acquisition of visible light, infrared and depth data; in the using process of the method and system, the diagnosis efficiency and accuracy of medical staff can be improved, the medical staff can be effectively assisted to analyze and confirm the type of the illness state, and meanwhile the doctor seeing efficiency of the medical staff can be effectively improved.

The invention discloses an intelligent skin disease recognition system and screening method based on image features, and relates to the technical field of artificial intelligence. Skin disease image data are collected and uploaded to the labeling system, and label labeling is completed through assistance of professional doctors. The constructed skin disease data set covers dermatoscope type data and clinical shot image data. Performing data enhancement, standardization, normalization and image size unified adjustment on the labeled data, and inputting the data into a deep learning model for training; a deep neural network DCSNeXt model suitable for skin disease image feature recognition is designed and constructed, a special classifier is designed according to data categories, and model training and performance testing are carried out on the two types of data; a plurality of existing common image recognition models are further used for carrying out training testing on the data set, and the performance of the data set is compared with the performance of a DCSNeXt model on a test set; in addition, two types of skin disease image data in related literatures in recent two years are collected, and a model with good performance is selected to carry out label-free prediction analysis on the skin disease image data. According to the invention, intelligent characterization analysis of dermatoscope and clinically shot dermatosis images can be realized, technical support is provided for feature extraction and classification of dermatosis images, the method can be widely applied to the fields of dermatology department clinical auxiliary analysis, telemedicine and medical teaching, and development of dermatosis intelligent analysis is promoted.

The invention provides a skin disease auxiliary evaluation system and device, a storage medium and a program product, and relates to the technical field of edge computation.The skin disease auxiliary evaluation system is deployed on a terminal device and comprises a feature extraction module, a feature extraction module and an evaluation module, obtaining semantic feature information corresponding to a disease description text in the skin disease information and image feature information corresponding to an affected part image in the skin disease information; the feature fusion module is used for associating the semantic feature information with the image feature information based on a cross attention mechanism to generate first multi-mode fusion feature information of the skin disease to be evaluated; and the large language model evaluation module is used for generating an evaluation result corresponding to the to-be-evaluated skin disease through a first large language model based on the first multi-mode fusion feature information.

Various implementations of the invention relate to a silicon-embedded multi-spectral lidar system and method for non-invasive, remote monitoring of pathological conditions of an individual. The system integrates multiple laser sources, photodetectors, and a processing module onto a silicon chip, enabling the capture and analysis of spatial, spectral, scattering and temporal data from a target subject (e.g., individual). By utilizing lidar signals at various wavelengths, various implementations of the invention detect three-dimensional (“3D”) surface and subsurface features such as micro-movements, vascular patterns, pigmentation changes, and tissue differences and / or irregularities. Machine learning algorithms process this information from the lidar to identify and classify abnormalities, enabling applications such as heart rate and respiration monitoring, eye movement tracking for neurological and mental health assessment, and detection of pathological skin conditions like cancer. Various implementations of the invention operate in real-time, adapt to environmental conditions, and support dynamic monitoring over time, thereby providing a comprehensive, non-contact solution for diagnostics and telemedicine, for example. Various implementations of the invention may comprise a compact silicon chip form factor suitable for portable and / or hand-held, scalable health monitoring applications across diverse environments.

The invention relates to the technical field of animal cub culture, in particular to an animal cub culture device for animal husbandry veterinarians, which comprises a culture box, a transparent door plate slidably connected to the culture box, a hollow plate inserted into the culture box, an excrement groove hung at the rear end of the culture box, a diversion groove formed in the lower end in the culture box, and a discharge groove formed in the rear wall of the culture box. The driving assembly is located in the incubator and comprises two movable blocks, the upper ends of the movable blocks are fixedly connected with push blocks, a guide rail is fixedly arranged on the top face of the upper end of the incubator, the upper ends of the two push blocks are slidably connected with the guide rail, and the outer walls of the two sides of each movable block are communicated with a plurality of nozzles. The flushed and disinfected area is dried through hot air, the cleaning effect of excrement of the cubs is improved, excrement residues are reduced, bacterium breeding is reduced, a healthy environment is provided for cub culture, and the morbidity of diarrhea, skin diseases and the like of the cubs is reduced.

The invention relates to the technical field of immunodetection, and discloses a bovine nodular skin disease virus chimeric antigen and application thereof in an ELISA (enzyme-linked immuno sorbent assay) detection kit. According to the chimeric antigen, a gene of a dominant region of a bovine nodular skin disease virus antigen is selected to design a chimeric antigen gene, meanwhile, sequence optimization is performed, a chemical synthesis method is used for synthesizing a coding gene of the chimeric antigen of the bovine nodular skin disease virus, and then the chimeric antigen is obtained in a prokaryotic expression mode. An ELISA antibody detection kit developed on the basis of the chimeric antigen has extremely high sensitivity and specificity, can rapidly and specifically detect bovine nodular skin disease virus antibodies in serum, is suitable for monitoring and checking the nodular skin diseases of cattle in large-scale breeding, and has relatively high practical value.

The present disclosure provides methods of treating an inflammatory skin disease (e.g., psoriasis) in a subject by administering an inhibitor of Hypoxia Inducible Factor 1α (HIF1α) or an inhibitor of glucose transporter 1 (Glut1). The present disclosure also provides topical formulations comprising an inhibitor of HIF1α or an inhibitor of Glut1.