6 results about "Craniofacial" patented technology

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.

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 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.

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 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.

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