35 results about "Craniofacial" patented technology

The invention discloses a craniofacial restoration method based on a diffusion model, and the method comprises the steps: S1, collecting a three-dimensional medical image of a head, carrying out the standardization processing and biological information labeling of the three-dimensional medical image, and obtaining a skull-craniofacial data set; s2, training a PiPNet network by using the craniofacial image marked with the craniofacial key structure mark points to obtain a craniofacial mark point recognition network; s3, constructing a craniofacial restoration network, and training the craniofacial restoration network by using the skull-craniofacial data set to obtain a craniofacial restoration model; and S4, inputting the skull image and the corresponding biological information into the craniofacial restoration model, and outputting a restored craniofacial image. According to the method, personal identity features are comprehensively captured by integrating multi-modal information; a diffusion model is used as a backbone network, the dependence of a traditional method on training data distribution is broken through, and the quality of a recovery result is remarkably improved. And meanwhile, an editable text prompt mechanism is introduced, so that the uncertainty of skull-face mapping is effectively reduced.

The invention provides a three-dimensional head shadow measurement method and device, a medium and equipment, and the method comprises the steps: obtaining a craniofacial three-dimensional image of a subject, carrying out the preprocessing, inputting the preprocessed three-dimensional image into a neural network model which is trained in advance, outputting the key information of the craniofacial in the three-dimensional image, and obtaining the three-dimensional head shadow according to the key information. And automatically calculating measurement parameters according to a predefined measurement rule, and displaying a measurement result obtained by calculation. According to the method, key points are automatically positioned through three-dimensional deep learning, a complex craniofacial structure is finely recognized, and calibration errors are reduced; the system automatically completes data preprocessing, key point detection and parameter calculation, manual calibration dependence is reduced, subjective errors and operation complexity are reduced, clinical efficiency is improved, a result is displayed through a three-dimensional visual interface, and doctors can rapidly understand and check; interactive punctuation fine tuning is provided, and efficiency and flexibility are both considered; the model can be trained based on various craniofacial data, is compatible with a common measurement system, and can add measurement items or analysis dimensions as required.

The invention provides a multi-modal fusion obstructive sleep apnea (OSA) identification method based on deep canonical correlation analysis (DCCA). For multi-modal characteristics, a whole process from data preprocessing, feature extraction and screening to feature fusion and classification is designed. Firstly, AHI indexes, voice data and craniofacial image data of a subject are collected and preprocessed, and DeepSpectrum voice features and geometric morphology image features based on MediaPipe are extracted. Then, feature screening is carried out by adopting a competitive group optimizer (CSO) and a ReliefF algorithm respectively; for a class imbalance problem, an oversampling (AMDO) method based on an adaptive mahalanobis distance is introduced to balance data. Non-linear mapping and deep fusion are carried out on high-dimensional features of the two modals through DCCA, low-dimensional high-correlation features are generated, and information complementarity between the modals is improved. And finally, inputting the fused features into a classifier and evaluating the performance by using five-fold cross validation. According to the invention, effective fusion between modals is realized by using DCCA, and a new method is provided for early screening and auxiliary diagnosis of OSA.

A method provides a palatal expansion management tool. The method includes receiving scan data of a craniofacial structure of a patient, providing the scan data as input to an artificial intelligence (AI) model trained to provide a recommended palatal expansion treatment plan for the patient; receiving, as output from the AI model, the recommended palatal expansion treatment plan; and providing, to a user device, the recommended palatal expansion treatment plan.

The invention discloses a cranio-maxillofacial CT image semantic segmentation method and equipment, and belongs to the technical field of cranio-maxillofacial CT image processing. The semantic segmentation method comprises the steps that a cranio-maxillofacial CT image is acquired, feature extraction is conducted on the cranio-maxillofacial CT image through a plurality of coding modules arranged in a cascade mode, and a loop feed correction unit is arranged in at least one coding module; acquiring a coding feature map output by each coding module, and processing the coding feature maps by using a decoding module to generate a decoded feature map; and generating a semantic segmentation map of the craniomaxillofacial CT image based on the loop feed correction feature and the decoding feature map. A heterogeneous feature extraction mechanism is arranged in the loop feed correction unit, and when the semantic segmentation map of the craniomaxillofacial CT image is generated based on the loop feed correction feature and the decoding feature map, the loop feed correction feature realizes fine adjustment of the decoding feature map, and the semantic segmentation precision is further improved.

The application provides a method for craniomaxillofacial CT image segmentation, a model training method and equipment, wherein the method for craniomaxillofacial CT image segmentation model training comprises the following steps: acquiring a craniomaxillofacial tumor CT image dataset, wherein the craniomaxillofacial tumor CT image dataset is composed of a labeled dataset and an unlabeled dataset; constructing a main network and a teacher network based on the same image segmentation deep neural network backbone model; the main network learns using the labeled craniomaxillofacial tumor CT image dataset in each training iteration; the teacher network learns using the labeled dataset in a preset starting stage and ending stage, simulates the learning process of the main network by averaging all weights of the current main network in the middle stage, and constrains the graphic data in the unlabeled dataset through segmentation consistency of different perspectives.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for anatomical feature-based medical predictions. One of the methods includes capturing a set of images depicting at least a portion of a subject body. The set of images are processed to extract a subset of images that satisfy one or more feature thresholds, such as thresholds related to anatomical pose or image quality. One or more target anatomical structures, such as craniofacial or oral cavity structures, are detected within the subset of images. One or more anatomical features are generated for the target anatomical structures, for instance, by fitting a three-dimensional model to the structures and computing geometric metrics. Using the generated anatomical features and stored medical data, a prediction is generated indicating a likelihood of a medical condition, such as obstructive sleep apnea or a comorbid condition, which can be transmitted to a device.

A method for using anatomical landmarks of a balance organ to construct a three-dimensional cephalometric coordinate system includes acquiring a CT image of a craniofacial region to be measured. The anatomical landmarks of the CT image are determined. On the basis thereof, a horizontal plane, and then a coronal plane and a median sagittal-plane plane are determined. The horizontal plane is a plane formed by certain of the anatomical landmarks. The coronal plane is determined using the horizontal plane and anatomical landmarks, and the median sagittal plane is determined using an anatomical landmark and both the horizontal and coronal. An intersection point of the three planes is used as the origin of a three-dimensional cephalometric coordinate system.

A method provides a palatal expansion previewer tool. The method includes receiving scan data of a craniofacial structure of a patient; identifying one or more parameters corresponding to a palatal expansion treatment plan for the patient; processing the data of the craniofacial structure of the patient and the one or more parameters to generate a visualization of a predicted outcome of the craniofacial structure affected by the palatal expansion treatment plan; and providing, for display in a user interface of a user device, the visualization of the predicted outcome.

A method provides a palatal expansion treatment management and visualization system. The method includes receiving data of a craniofacial structure of a patient; identifying one or more parameters corresponding to a palatal expansion treatment plan for the patient; processing the data of the craniofacial structure of the patient and the one or more parameters to generate a visualization of a predicted outcome of the craniofacial structure affected by the palatal expansion treatment plan; and providing, for display in a user interface of a user device, the visualization of the predicted outcome.

The invention relates to the technical field of oral cavity plastic surgery, in particular to a manufacturing method of a digital buckle type guide plate for dental crown lengthening, which comprises the following steps of: 1, scanning an upper jaw dental arch and a lower jaw dental arch of a patient to obtain intraoral scanning data, recording an occlusion relationship at a maximum tooth tip staggering position, and exporting the data for later use; step 2, acquiring facial three-dimensional scanning data, alveolar bone and craniofacial structure data, digital facial arch data, mandibular position and mandibular movement parameters of the patient; 3, fitting various data to determine ideal gingival margin position parameters and bone trimming contour line parameters required in the operation; and 4, on the basis of the parameters determined in the step 3, buckle type guide plates are designed in design software, the buckle type guide plates comprise a gingival resection guide plate and an osteoplasty guide plate, and finally the buckle type guide plates are printed through a 3D printer. By implementing the buckle type guide plate prepared by the method, the stability, the accuracy, the operation convenience and the operation efficiency in the operation process of the dental crown extension surgery are remarkably improved.

The invention provides a preparation method of a genetically modified mouse capable of mediating Tbx1 inherent disorder protein structural domain deletion. The method comprises the following steps: (a) constructing a targeting vector; (b) preparing a gene editing compound; (c) microinjection; (d) embryo transplantation and screening; (e) breeding and establishing a stable strain. Sequencing verifies that the gene modified mouse can stably express the Tbx1 mutant protein which lacks IDR and carries an N-terminal 3xFlag tag, and a homozygote mutant mouse shows remarkable developmental defects such as ventricular septal defect and craniofacial deformity; and an important genetic tool is provided for researching the action mechanism of the phase separation characteristic of the Tbx1 protein in the heart, craniofacial development and congenital heart disease (such as DiGeorge syndrome).

The application discloses a kind of craniofacial prosthesis systems based on digital simulation, specifically relates to digital simulation technical field, can preliminary predict and fine adjustment the three-dimensional image of bone defect area by mirror flip, functional area division and adjustment based on functional index, to more accurately reflect actual bone defect situation, provide strong support for subsequent repair scheme design;Through simulation simulation craniofacial movement and function, predict the functional recovery after bone defect repair, and output function simulation quality index, so that user can intuitively understand the repair effect, provide basis for treatment decision;Joint analysis surface smoothness index, function simulation quality index and simulation bone quality evaluation index, output bone reconstruction availability coefficient Ks;Judge the relationship between bone reconstruction availability coefficient and corresponding threshold Tza;Solve the problem that prior art cannot scientifically provide decision support for craniofacial prosthesis.

The VEM-robot emotional right-brain model construction method decomposes the robot's brain into an emotional right brain, an intellectual left brain, and a motor cerebellum, forming the embodied or humanoid robot operating system VEM-ROS. This enables emotional communication between the robot and humans or other robots. Human output is used as the perceptual spectrum, and robot output as the deductive spectrum. Both the perceptual and deductive spectra are segmented, synthesized, and aligned using the emotional rhythm of the multimodal VEM-Token. LLM-Token decomposition of the large language model calculates lexicalized logical perception, and VEM-Token decomposition calculates the micro-expressions of multimodal emotional components. VEM-ROS also includes priority and interruption mechanisms, VEM memory mechanisms, dialogue relationships, craniofacial and limb sensors and actuators and interfaces, robot cloning, and operating system encapsulation. The right-brain model supports emotional micro-expression communication between the robot and humans, similar to the Turing test, and is expected to distinguish whether the emotional test subject is a robot or a human.

The application relates to the technical field of medical image processing, and discloses a craniomaxillary dynamic reconstruction method and system based on multi-modal data fusion, which comprises the following steps: arranging multi-modal data acquisition devices in a target craniomaxillary region, acquiring static CT images, static MRI images, dynamic expression video sequences and surface electromyography signals; preprocessing the static CT images, the static MRI images, the dynamic expression video sequences and the surface electromyography signals; inputting the preprocessed data into a multi-scale finite element model and simulating the coupling relationship between muscle contraction force and skin deformation by adopting a biomechanical driving strategy to generate a dynamic craniomaxillary model; and fusing the geometric error and motion consistency score of real data by adopting a linear regression method to output a comprehensive reconstruction quality index. The application can solve the problems of low dynamic modeling accuracy and poor robustness in the prior art.

The application provides a non-contact screening system for obstructive sleep apnea syndrome. The non-contact screening system for obstructive sleep apnea syndrome only needs to collect a user's face video through a data collection device, can extract multi-dimensional biological characteristics related to obstructive sleep apnea syndrome, including craniofacial morphological risk characteristics and real-time physiological characteristics, and can realize risk screening of obstructive sleep apnea syndrome without any body contact through fusion analysis of the characteristics by a deep learning model, thereby significantly improving the convenience and comfort of screening while maintaining high diagnostic accuracy.

The application belongs to the technical field of image information processing, and particularly relates to a cranium face generation system based on edge guidance and semantic regulation, which comprises an information acquisition module, a feature extraction module, a condition control module, a diffusion generation module and an output module; the information acquisition module is used for acquiring cranium medical image information and semantic information; the feature extraction module is used for processing the semantic information to obtain latent features; the condition control module is used for acquiring structural features according to the cranium medical image information and the latent features; the diffusion generation module is used for acquiring current cranium face latent representation according to the structural features and the latent features; and the output module is used for outputting a noise prediction result according to the current cranium face latent representation, wherein the noise prediction result is used for regulating the current cranium face latent representation to obtain cranium face representation.

This invention discloses an interpretable diagnostic model, method, and electronic device for rare craniofacial diseases. The model includes a medical vision-language feature encoding network, a supervised hierarchical contrastive learning module, a KL regularized progressive unfreezing module, and an interpretable diagnostic output module. First, image-text pairs are constructed. By introducing disease semantic priors, an adaptive soft-boundary contrastive learning loss function is built, which tolerates visual overlap while preserving the feature manifold topology of phenotypic similar diseases. Second, KL divergence is used to monitor the drift of feature distribution between layers, and progressive unfreezing of the model is achieved through trust domain constraints. This invention effectively avoids catastrophic forgetting during model fine-tuning, improving the diagnostic accuracy of rare craniofacial diseases while outputting interpretable attention heatmaps and facial feature descriptions that are precisely aligned with pathological features, providing an efficient and reliable technical solution for early clinical screening and assisted diagnosis.

The invention discloses a construction method and system of a child OSA auxiliary diagnosis model based on craniofacial features, which utilizes OSA related symptoms and clinical signs of subjects of all OSA child patients. According to the invention, the basic information, birth mode, birth weight, full-term infant, feeding mode and other information of all subjects including OSA child patients and non-OSA children, as well as craniofacial features and nasopharynx side X-film parameters are fully utilized, so that a plurality of information can be integrated while the low-radiation imaging technology is utilized; the optimal model is constructed and screened based on the artificial intelligence technology, and an auxiliary diagnosis system is further realized, so that early-stage rapid triage of OSA children is realized, and guidance is provided for orthodontic treatment of OSA patients to improve the craniofacial structure.

The application discloses a kind of three-dimensional dental jaw craniofacial deformity auxiliary identification method and system based on machine learning.The face three-dimensional photo set obtained and the face three-dimensional photo to be predicted are respectively marked and the coordinate system is unified, after the respective feature information corresponding to face three-dimensional photo set is preprocessed, it is trained by machine learning model, and prediction model is obtained, the landmark point coordinates of the face three-dimensional photo to be predicted are predicted using the prediction model, and the face feature is obtained, to assist dentist to carry out dental jaw craniofacial deformity discrimination.The present application can avoid / reduce the amount of exposure to electric radiation, the process is friendly to patients, and is more acceptable.Based on the learning of larger sample size, the prediction is more accurate.

The application discloses a small data set craniomaxillofacial translation method based on GAN, which comprises the following steps: 1, collecting skull and facial CT image data; 2, performing image preprocessing, three-dimensional reconstruction and fairing treatment on the skull and facial CT image data to obtain complete three-dimensional models of the skull and the face; 3, placing the three-dimensional models of the skull and the face in the Frankfurt coordinate system to perform normalization operation; 4, performing vertical mapping of the three-dimensional models of the skull and the face on the XOZ plane in the Frankfurt coordinate system to obtain the front view images of the skull and the face; 5, introducing a Gaussian pyramid into a GAN network to construct a network model PCC-GAN for skull and facial translation; 6, training network parameters of the pyramid cycle consistency generative adversarial network model PCC-GAN; and 7, placing the skull and facial images into the craniomaxillofacial translation model PCC-GAN to generate two-dimensional skull and facial images, and more accurate and real facial images can be generated under the condition of less point cloud data.

An external distraction apparatus for performing a craniofacial distraction is disclosed. The external distraction apparatus may comprise a stationary member. The stationary member may be configured to be affixed to a head of a patient. The external distraction apparatus may also comprise laterally disposed distractors. The laterally disposed distractors may extend inferiorly from the stationary member and be moveable relative to the stationary member.

The utility model discloses an oral cranio-maxillofacial zygomatic implantation navigation system face registration support which comprises an oral interior, a plurality of retention holes are formed in the front portion of the oral interior, a metal lantern ring matched with a retention nail is arranged in each retention hole, the front outer side of the oral interior is connected with one or two out-of-oral extending portions, and the front outer side of each metal lantern ring is connected with one or two outer-of-oral extending portions. Registration conical pits which do not need registration nails are formed in one turning position and the tail end of the out-of-mouth extending part, and the tip ends of the registration conical pits face the interior of the registration support. According to the utility model, the original tedious operation of punching registration nails at multiple positions in the mouth, the caused extra trauma and the risk that the registration nails enter the maxillofacial gap and the maxillary sinus by mistake are avoided, the original titanium nail registration is abandoned, the problem of inaccurate registration point setting caused by metal artifacts is avoided, and the efficiency of the oral implant navigation operation is improved.

The invention discloses a generative skull face restoration method, and relates to the technical field of face restoration, and the method comprises the steps: scanning a tested object, obtaining craniofacial CT data, and carrying out the preprocessing of the CT data; the method comprises the following steps: segmenting outlines of a skull and a wrapper by setting a threshold value, and performing orthogonal projection on three-dimensional data of outline information along a French plane to generate a two-dimensional craniofacial outline image; training a generative adversarial network by using the sample training set, inputting the two-dimensional craniofacial contour image into the trained generative adversarial network, and outputting a facial image of the skull to be restored; the frequency domain sensing generator introduces a semi-wavelet attention mechanism into an encoder and jump connection based on a U-Net network, and the feature characterization capability of a key region is enhanced. According to the invention, by combining the synergistic effect of the half-wavelet attention mechanism in the frequency domain perception generator and the high and low frequency decomposition module, the whole network can simultaneously maintain the accuracy of the overall structure of the face and the definition of the local features in the training process.

1. The name of the design product: Absorbable bone plate. 2. The use of the design product: Used for the connection and fixation of the fracture of craniofacial and other non-weight-bearing parts of the body (such as maxilla, orbital bone, nasal bone, zygomatic bone, zygomatic arch) and other parts. 3. The design points of the design product: The combination of shape and pattern. 4. The picture or photo that best indicates the design points: front view.

Systems and methods for closed-loop vibroacoustic neuromodulation are disclosed. A device uses upper and lower transducer arrays to form a closed acoustic circuit that constructively couples energy through the torso; disabling the upper array under constant lower drive yields a large reduction in internal acceleration (>1 Ox and up to >50x). A latency engine preserves cross-modal phase alignment using per-user delay profiles and sub-second to sub-millisecond compensation. Group sessions are enabled by a synchronization server maintaining phase-coherent outputs across nodes. A craniofacial "Beauty Bar" docks into textile accessories (weighted throws, cushions, pillow inserts) to serve as an over-body array, with offline SD operation and a back-to- sleep control. Thermal safety employs a dual-exponential predictive model with redundant sensors, watchdog, and analog cutoff. Retail kiosk analytics acquire brief EEG / biometric data to generate personalized profiles and to provision portable devices for home use.

The invention discloses a construction method and system of a child OSA auxiliary diagnosis model based on acoustic immittance. According to the method, from the perspective of middle ear function and craniofacial anatomy joint analysis, a model is constructed; through systematic integration of acoustic immittance parameters (middle ear pressure, sound order value and the like), clinical signs (tonsil indexing, mouth breathing and the like) and facial imaging features (such as A / N ratio, soft palate length and the like determined based on nasopharynx side position X films), a children OSA auxiliary diagnosis model based on multi-modal data fusion is constructed. Compared with the prior art, the method has the advantages that easily obtained data parameters such as middle ear functions and craniofacial anatomy are creatively integrated, a high-precision and low-cost OSA auxiliary diagnosis tool is constructed, non-invasive and efficient screening replaces complex and expensive PSG detection, an objective and reliable solution is provided for early screening and grading diagnosis and treatment of children OSA, and the method is suitable for popularization and application. And the method is particularly suitable for large-scale application in regions deficient in medical resources.

The invention belongs to the technical field of image information processing, and particularly relates to a craniofacial generation system based on edge guidance and semantic regulation, which comprises an information acquisition module, a feature extraction module, a condition control module, a diffusion generation module and an output module, the information acquisition module is used for acquiring medical image information and semantic information of the skull; the feature extraction module is used for processing the semantic information to obtain potential features; the condition control module is used for acquiring structural features according to the skull medical image information and the potential features; the diffusion generation module is used for acquiring a current craniofacial potential representation according to the structural features and the potential features; the output module is used for outputting a noise prediction result according to the current craniofacial potential representation, and the noise prediction result is used for regulating and controlling the current craniofacial potential representation to obtain the craniofacial representation.

Laboratory Equipment – Orbital Morpho-Evolution Helm with Multi-Layer Craniofacial Projection

The subject application provides systems and methods for providing personalized virtual digital dentition of a patient. Indeed, inventors have found a way of designing a personalized virtual digital dentition of a patient that is based only on a digital image of the front face of the patient. The system (100) comprises at least one memory (110), processor-based acquiring means (120) and processor-based computer vision means (130), which are operatively coupled to each other. Processor-based computer vision means (130) may implement a machine learning classifier which is trained for predicting the likelihood that a set of cranio-facial features of a human face is similar to each of the set of reference cranio-facial features of a plurality of predetermined reference cranio-facial vectors.