13 results about "Perinatal period" patented technology

The invention discloses a non-contact fetal movement monitoring identification method and system based on a millimeter wave radar, and the radar technology and deep learning are combined and applied to the field of fetal movement monitoring, and the millimeter wave radar is used for transmitting a detection signal to the abdomen of a pregnant woman and receiving an echo signal; then a series of operations such as scattering separation, time-frequency spectrogram generation, feature extraction, fetal movement recognition and storage are performed, and the advantages of the radar technology and deep learning are fully exerted. Complicated features in fetal movement signals are automatically learned and extracted through a deep learning algorithm, the recognition capability and accuracy of the fetal movement signals are improved, the method adapts to individual differences of different pregnant women and fetuses, more personalized fetal movement monitoring services are provided for the pregnant women, and higher convenience and reliability are achieved. The fetal movement condition can be accurately monitored in real time, abnormal fetal movement can be found in time, timely and accurate fetal health information can be provided for pregnant women and doctors, the risk of fetuses can be reduced, and the quality and level of fetal monitoring in the perinatal period can be improved.

The invention discloses a health data linkage management system for pregnant and lying-in women in a large perinatal period, and belongs to the technical field of intelligent data management. The system comprises a multi-source heterogeneous data access and standardization gateway which is used for connecting household equipment and a hospital system and standardizing original data through a deep cleaning engine; a data fusion and association unit of the central data processing server fuses multi-source data according to a time sequence by relying on a gestational week management unit, and a multi-modal health analysis engine is combined with a clinical rule and a machine learning model to carry out early warning, risk prediction and personalized analysis; and the multi-terminal interaction platform provides a fusion data view and a closed-loop management tool based on role permission. According to the invention, equipment and hospital information islands are broken through, and through high-quality data management and'rule + AI 'fusion analysis, accurate and foresight management and efficient cooperation of the full-cycle health state of pregnant and lying-in women are realized.

The invention discloses application of oridonin or a derivative thereof in preparation of a medicine for inhibiting premature delivery. Aiming at the perinatal period problem that the survival rate of newborn piglets is low due to premature delivery of sows, a large number of experimental exploration shows that the premature delivery of the sows can be effectively inhibited by applying oridonin or derivatives thereof to the sows at the end of pregnancy, and the survival rate of the piglets is increased. The composition and the method can effectively improve sow delivery and increase the survival rate of piglets, have great value in promoting batch production and efficient breeding of sows, and have great significance in solving the problem of premature delivery in the perinatal period in the reproductive field.

The invention discloses an intelligent perinatal maternal and infant health monitoring and early warning system, which relates to the technical field of medical health monitoring and comprises a data acquisition module, an evaluation module, an early warning module and a feedback module, the collected maternal and infant physiological data are transmitted to the data processing center in real time; the evaluation module obtains maternal and infant health data, constructs a maternal and infant health evaluation model by combining the maternal and infant physiological data and applying a machine learning algorithm, and calculates the maternal and infant physiological data deviation degree; the early warning module is used for comparing and analyzing the maternal and infant physiological data deviation degree with a preset threshold value, and sending out mild, moderate and severe early warning information according to a comparison and analysis result; and the feedback module is used for medical staff to check the early warning information and the maternal and infant physiological data deviation degree, formulate a personalized treatment scheme in combination with clinical experience and feed back through the system. According to the invention, the response speed to the maternal and infant health problem is improved, and the health risk is reduced.

The application provides a placenta perfusion and fetal development prediction method and system based on ultrasound images, and relates to the technical field of medical image analysis and diagnosis, which comprises the following steps: obtaining a placenta ultrasound image and pre-processing and quality control; segmenting the placenta region through a deep neural network, extracting multi-dimensional features of gray scale, texture, shape and blood flow; inputting a machine learning model to predict the perfusion and fetal development state; visualizing the results and key factors, combining clinical verification and generating intervention suggestions. The system comprises image processing, segmentation, prediction, visualization and verification interpretation modules. The application realizes objective quantitative evaluation, solves AI problems, forms full-chain clinical support, improves early warning accuracy, helps precise intervention and reduces perinatal risks.

1. Name of the product in this design: Ultrasonic Doppler Fetal Monitor (FS Series). 2. Purpose of this design: For continuous monitoring of fetal heart rate, fetal movement and maternal uterine contraction pressure during the perinatal period. 3. The key design features of this product are its overall shape. 4. The image or photograph that best illustrates the design's key points: a 3D model.

The utility model discloses a perinatal tissue transportation suit which comprises a tissue storage bag, a liquid adding assembly is arranged at one end of the tissue storage bag, and a sealing assembly is arranged at the other end of the tissue storage bag. The liquid adding assembly comprises a spiral opening, a movable clamp and a hose, and the sealing assembly comprises a sealing clamp and a double-sealing strip. According to the utility model, the hose is arranged at one end of the tissue preservation bag, and the spiral opening and the movable clip are arranged on the hose, so that tissue preservation liquid can be firstly guided into the tissue preservation bag, and a transportation suit containing the preservation liquid is directly submitted to a collection person; the sealing clip is additionally arranged on one side of the double-sealing strip, the sealing effect is enhanced while sealing is convenient, the sealing clip is provided with scale marks, and whether the umbilical cord length meets the collection standard or not can be preliminarily judged; the easy-to-tear spout is additionally arranged on the tissue preservation bag, an operator in a laboratory can tear the easy-to-tear spout along a dotted line, liquid is poured into a waste liquid cylinder, and the phenomenon of liquid splashing caused when the tissue and the liquid are transferred together can be effectively avoided.

This invention provides a perinatal full-cycle intelligent intervention method based on multi-source heterogeneous data fusion, comprising: step S100, simultaneously collecting subjective self-reported data from pregnant women, real-time physiological data from wearable devices, and heterogeneous clinical examination data to construct a multi-dimensional spatiotemporal data pool; step S200, establishing a unified time base axis with gestational age and absolute time as dual coordinate axes, and extracting feature vectors from the data in the multi-dimensional spatiotemporal data pool at fixed time steps; step S300, calculating the real-time global comprehensive risk index RI based on the feature vectors; step S400, when RI exceeds the first dynamic threshold T1, the system triggers a level-one warning; when RI exceeds the second dynamic threshold T2 and a preset acute-critical feature operator is identified, the system triggers a level-two warning, and simultaneously generates an admission preparation list and sends a rescue instruction to the hospital's emergency dispatch system.

The invention relates to the technical field of medical health information, in particular to a perinatal period grading management system based on AI risk assessment, which comprises a data acquisition layer, a feature engineering layer, an AI risk assessment module, a five-color dynamic grading module and an intervention push management module. According to the scheme, a multi-scale decision table is constructed by adopting a multi-scale data fusion method, modeling is carried out on data of different sources and different scales, information gain is introduced as a scale selection standard, a high-fidelity fusion feature vector is formed, discriminative information of each data source under the optimal scale is reserved, and a high-fidelity fusion feature vector is formed. High-quality input with a clear structure and a uniform scale is provided for a subsequent AI risk assessment module; an improved Transform model is designed, a time dependence matrix and a dynamic weight adjustment mechanism are embedded, deep modeling of time series data and generation of interpretable dynamic rules are achieved, online self-updating of the rules is supported, and the accuracy and real-time performance of risk assessment are improved.

The present invention provides a data processing method and system for perinatal depression monitoring. The data processing method for perinatal depression monitoring includes: extracting the current pregnancy stage of a perinatal patient, setting a data collection frequency according to the current pregnancy stage of the perinatal patient, and dynamically adjusting the data collection frequency of the current pregnancy stage of the perinatal patient in real time during the data collection process; collecting data from the perinatal patient according to the data collection frequency, obtaining the behavior data and physiological data of the perinatal patient, and performing data quality assessment and data quality anomaly warning on the behavior data and physiological data; and storing the behavior data and physiological data that meet the data quality requirements of the perinatal patient in groups. The system includes modules corresponding to the method steps.

The invention provides a fetal growth limitation intelligent prediction system and method based on double networks, and relates to the technical field of perinatal medical monitoring, and the system comprises a data collection and preprocessing module which is used for processing collected prenatal fetal heart related data and outputting high-quality data; the fetal heart signal feature extraction module is used for outputting fetal heart feature vectors by adopting a parallel architecture of a convolutional neural network and a long-short-term memory network; the multi-modal feature fusion module is used for outputting a fusion feature vector based on the double-tower structure architecture; the hierarchical classification prediction module is used for modeling a hierarchical structure of FGR clinical diagnosis by adopting a graph neural network GNN, and outputting prediction probabilities of FGR diagnosis, subtype classification and perinatal outcome in combination with an MC loss function optimization model; and the clinical decision support module is used for generating decision result suggestions and visual reports. And the clinical requirements of early accurate identification, subtype classification and perinatal outcome prediction are met.

The invention discloses a method for establishing a multi-dimensional BPD nursing prediction model for a premature infant. The method comprises the following steps: collecting samples; incorporation and exclusion: formulating an incorporation rule and incorporating the samples conforming to the incorporation rule; formulating an exclusion rule to exclude samples conforming to the exclusion rule; data collection and sample inspection: collecting baseline data by using a standardized data collection table; the baseline data comprises demographic characteristics, perinatal period factors, newborn scores and resuscitation conditions; checking the sample; grouping and BPD diagnosis: the samples are divided into a normal group, a mild rising group, a moderate rising group and a severe rising group according to the EC peak level; statistical analysis: carrying out statistical analysis by adopting SPSS 26.0 software; and constructing a model. According to the invention, the BPD risk of the premature infant can be effectively identified.

The present application relates to the technical field of intelligent medical treatment and maternal and child health, and particularly relates to a pregnant woman perinatal risk intelligent evaluation method based on multi-index fusion, which acquires clinical, behavioral, environmental and real-time physiological data through multi-source acquisition and preprocessing, dynamically calculates multi-dimensional feature weights based on analytic hierarchy process and entropy weight method, uses attention mechanism and graph neural network for multi-modal fusion and risk correlation analysis, integrates data with the help of space-time alignment engine, adopts integrated learning to construct an evaluation model, carries out four-level grading early warning of low, medium, high and extremely high risk, generates and continuously optimizes personalized intervention scheme combined with case reasoning and reinforcement learning, and completes the intelligent dynamic evaluation and management closed loop of perinatal risk. The present application realizes comprehensive data integration, personalized index dynamic adjustment and intelligent risk correlation analysis of perinatal risk evaluation, effectively solves the problems of data island, rigid index and low fusion efficiency, and improves the accuracy and timeliness of the evaluation.