10 results about "Computer aided diagnostics" patented technology

This invention belongs to the field of computer-aided diagnostic technology, specifically a real-time diagnostic method and system for bruxism based on non-verbal audio feature recognition. The invention includes: acquiring audio in a sleep environment; converting non-stationary signals into short-time stationary signals through frame segmentation; extracting multiple time-domain and frequency-domain features from the audio samples, and summarizing the variance and mean distribution feature groups by combining multiple feature arrays from each different frame; and optimizing the best predictive model for bruxism diagnosis using a machine learning model. This invention can accurately identify the occurrence of bruxism and other sleep states in complex sleep environments without requiring additional computational resources for preprocessing such as noise reduction and filtering of the audio signals. It enables non-contact monitoring and diagnosis of bruxism with a simple process, avoiding patient discomfort while providing practical reference and theoretical analysis for subsequent interventions for bruxism.

Nondestructive evaluation (NDE) of objects can elucidate impacts of various process parameters and qualification of the object. Computed tomography (CT) enables rapid NDE and characterization of objects. However, CT presents challenges because of artifacts produced by standard reconstruction algorithms. Beam-hardening artifacts especially complicate and adversely impact the process of detecting defects. By leveraging computer-aided design (CAD) models, CT simulations, and a deep-neutral network high-quality CT reconstructions that are affected by noise and beam-hardening can be simulated and used to improve reconstructions. The systems and methods of the present disclosure can significantly improve the reconstruction quality, thereby enabling better detection of defects compared with the state of the art.

This invention discloses a privacy-preserving computer-aided diagnostic method based on inner product function encryption. The steps include: 1) A key generation center calls a group generation algorithm to generate a master key msk and public parameters pp; 2) Calculates and returns a key to the data owner based on msk, the data owner's identifier ID, and the input vector; 3) The model owner calculates D = e(h1, h2) based on pp, ID, and the input vector. r And then send the ciphertext to the computer-aided diagnostic device; 4) The data owner obtains the inner product operation result based on the ciphertext and the key, and then calculates the prediction result.

The invention discloses a bone marrow cell morphology identification method and system based on a cross-domain adaptive joint convolutional neural network, and relates to the technical field of computer-aided diagnosis, the method comprises the following steps: S1, obtaining bone marrow cell image data, and carrying out image segmentation through a dual-path mask optimization segmentation module to obtain a single cell segmentation image; s2, extracting and fusing pathological features of the single cell segmentation image through a four-dimensional feature fusion module to obtain a spatial fusion feature vector; s3, performing feature alignment on the spatial fusion feature vector through a cross-domain adaptive joint module to obtain an aligned domain invariant feature vector; and S4, outputting a cellular morphology recognition type from the multi-center feature distribution vector through a dynamic element classifier. According to the method, high-precision and high-robustness automatic recognition of the bone marrow cellular morphology in a high inter-domain difference environment is realized, and the method has a significant clinical application value.

Method for planning maintenance measures for a railway technical system The invention comprises a method for planning maintenance measures for a railway technical system (BTS), comprising a functional component (FK) that performs a function belonging to railway operation, a monitoring device (UV) that monitors the function of the functional component (FK) and generates monitoring data (UD) describing this function, a diagnostic device (DGN) that receives messages containing monitoring data (UD) generated by the monitoring device (UV) and generates computer-aided diagnostic data (DD) that describe a need for maintenance measures.A computer-aided planning module (PLM) is used, which employs a Large Language Model (LLM) to evaluate messages containing diagnostic data (DD) and to generate analysis data (AD) that describe the diagnostic data (DD) in human language. The evaluation is performed with regard to maintenance requirements and / or maintenance measures. A description of the maintenance requirements and / or maintenance measures is generated in human language. Output data (OD) representing the description of the maintenance requirements and / or maintenance measures is output via a first interface (S1). Furthermore, the invention comprises a railway engineering system, a computer program product, and a computer-readable storage medium. The advantage lies in the fact that various types of documentation describing the railway engineering system can be evaluated automatically.

This invention relates to the field of computer-aided diagnostic technology, and specifically discloses a psychological trauma treatment system based on HRV monitoring and transcutaneous vagus nerve stimulation. The system eliminates the interference of time overlap on physiological indicators through HRV rhythm correction, eliminates the limitations of single indicator assessment through multi-dimensional data fusion, and achieves dynamic adaptation of intervention programs through subtyped taVNS treatment and closed-loop feedback, thereby reducing the bias in assessing the severity of psychological trauma and improving the accuracy of treatment.

The invention relates to a computer-implemented diagnostic method learning for determining clinical interpretation of renal graft alterations by applying a trained machine-learning model and a method for training a model for determining clinical categories based on renal transplant lesions, clinical signs and routine laboratory test results.

A system for intelligent monitoring of attention and recognition of the results of a computerized diagnostic system. The system may include an endoscope, a computerized diagnostic module, a camera, a memory unit, and a control unit. The endoscope may include an elongated element that encompasses a distal section and a process camera attached to the distal section. The process camera can record a video data stream during a procedure. The computerized diagnostic module may be configured to detect anomalies within the video data stream using a diagnostic algorithm and transmit a signal.The control unit can be configured to use a gaze algorithm to determine the physician's gaze point during the procedure, to ascertain, by comparing the signal from the computer-aided diagnostic module and the physician's gaze point, whether the physician has viewed the detected anomaly, and to trigger a countermeasure based on the finding that the physician has not viewed the detected anomaly.

Introduced here are diagnostic platforms able to produce visualizations that visually highlight the pixels considered as evidence of a medical condition by a computer-aided diagnostic (CADx) model. A CADx model may be based on, for example, a multi-headed neural network designed to detect the presence / progression of multiple medical conditions. By explaining how each output is produced by the multi-headed neural network, a diagnostic platform can identify the latent variable (s) responsible for producing each output. For example, a diagnostic platform may create a multi-variable heatmap that visually distinguishes the pixels considered as evidence of each multiple condition by a multi-headed neural network. To accomplish this, the diagnostic platform may create multiple heatmaps by producing, for each output, a separate heatmap that distinguishes the pixels in the image considered as evidence of the corresponding medical condition by the multi-headed neural network, and then compiling the multiple heatmaps into the multi-variable heatmap.

The invention belongs to the technical field of medical artificial intelligence and computer-aided diagnosis, and particularly relates to a brain disease image classification method and device based on a multi-modal graph hierarchical network, and the method comprises the steps: integrating resting-state brain function magnetic resonance imaging data and non-imaging data, and constructing a brain region graph; performing hierarchical graph structure construction, multi-hop neighborhood information aggregation and residual error and jump connection processing through the brain region graph to obtain global graph representation; predicting the global graph representation through a plurality of full connection layers to obtain an image classification result of the to-be-detected sample, and the technical scheme of the invention improves the accuracy of brain disease classification and improves the flexibility of brain disease classification.