46 results about "Gestational age" patented technology

The invention provides an image segmentation method, a gestational week prediction method and a diagnosis report determination method and device, and belongs to the field of medical artificial intelligence. The image segmentation method comprises the steps that a U-shaped image segmentation network is constructed, and the U-shaped image segmentation network comprises an encoder and a decoder; wherein the encoder and the decoder are connected through a jump connection layer; a self-adaptive feature gating module of each jump connection layer embedded layer of the U-shaped image segmentation network is used for strengthening the expression ability of a key anatomical structure and filtering irrelevant information; a bottleneck layer of the U-shaped image segmentation network is embedded into a bottleneck domain adaptive feature calibration module which is used for guiding deep semantic positioning by utilizing shallow space details and improving multi-section organ segmentation robustness; training the U-shaped image segmentation network by adopting the training set to obtain an image segmentation model; and inputting a to-be-segmented image into the image segmentation model to obtain an image segmentation result. According to the method, standardized, multi-section and multi-structure collaborative segmentation of the image can be realized.

The invention relates to a fetal brain tissue segmentation method based on prior information guidance, and the method comprises the following steps: obtaining a public data set, and selecting a part of data in the public data set as a training set; preprocessing the training set to obtain a grey-white matter volume ratio, an edge feature map and a gestational week feature map; constructing a prior segmentation model GA-Swin, and designing a loss function LTotal to train the model; and finally obtaining a trained fetal brain segmentation model. According to the method, on the basis of following the development law, each tissue of the fetal brain can be precisely segmented, and collaborative optimization of precision interpretability and biological rationality in medical image analysis is achieved.

The invention discloses a newborn health monitoring system based on multi-modal data fusion, and belongs to the technical field of medical monitoring. According to the system, aiming at the problem of misjudgment caused by complex background interference and newborn development difference, a background perception feature decoupling enhancement network is constructed, a dual-channel architecture is matched with an adversarial loss function, foreground skin and background features are forcibly separated, and weak pathological features are enhanced in combination with a color space attention mechanism. Meanwhile, a development stage self-adaptive classification network is utilized, the gestational age serves as prior information to generate development codes, and dynamic modulation and weighted evaluation are conducted on fusion features through a premature infant and full-term infant double-branch classifier. According to the invention, the influence of environmental noise is effectively eliminated, and differentiated accurate health state recognition and risk early warning are realized.

The invention provides a fetal hypoxia early risk identification method and system based on a large pulse pressure phenotype, and relates to the technical field of biomedical signal processing and heart sound signal analyse.The method comprises the steps that fetal heart sound signals and body surface vibration signals at the abdominal wall of a pregnant woman are collected through a multi-channel sensing array; a preprocessing means is adopted to improve the signal-to-noise ratio of a target signal and suppress an interference signal; in the sliding time window, a large pulse pressure phenotypic feature set and heart sound time phase parameters are extracted; based on the combined criterion, fetal hypoxia risk judgment is carried out; and outputting the graded early warning information, and supporting the playback of the risk event fragment. According to the method, fetal real heart sound mechanical vibration serves as an information source, hypoxia early warning is achieved based on a large pulse pressure phenotype, individualized base lines (including day and night layering) and gestational week self-adaptive correction are supported, the anti-interference capacity is high, the 7 * 24-hour continuous monitoring requirement in a hospital / family can be met, and the sensitivity and specificity of hypoxia recognition are improved.

The invention discloses a gestational diabetes mellitus early risk accurate prediction method based on multi-feature fusion, and relates to the technical field of gestational diabetes mellitus risk prediction. The risk prediction efficiency is improved. Comprising the following steps: collecting clinical data of early pregnancy (8-16 weeks of pregnancy) through a multi-center electronic health record (EHR), cleaning the data according to a preset exclusion standard, dividing the data into a training set and a test set, and screening risk factors related to a GDM diagnosis standard based on literature retrieval; preprocessing the data, including missing value filling and abnormal value elimination, and determining an optimal feature subset through mutual information (MI), variance analysis (ANOVA) and incremental feature selection (IFS); through a multi-feature fusion method, multi-dimensional data such as demographic statistics, basic measurement, medical history, laboratory indexes and transcriptome genes are combined, and the prediction accuracy of the early risk of gestational diabetes mellitus is remarkably improved.

The invention, which relates to the technical field of medical information management, discloses an obstetrical diagnosis and treatment information integrated management and leak detection early warning system comprising a data acquisition module, a diagnosis and treatment trajectory modeling module, a rule knowledge base module, a leak detection identification module, a leak detection risk calculation module, a high-risk cross analysis module and an early warning push module. A gestational week time axis and diagnosis and treatment trajectory model is constructed by collecting basic information of pregnant and lying-in women and previous antenatal care data; matching the trajectory model with a standard obstetrical diagnosis and treatment path rule set, identifying missing or overdue examination, calculating a missing detection weight value, and forming an individualized risk map; further performing cross analysis in combination with high-risk pregnancy indexes, determining high-risk omission items, generating an early warning task queue, and pushing the early warning task queue to a doctor-patient terminal to realize closed-loop management and real-time intervention; the accuracy of missed detection identification and the intelligent level of obstetrical risk management can be effectively improved.

This invention discloses a method, apparatus, device, and storage medium for medication risk assessment during pregnancy, relating to the interdisciplinary fields of medical information technology and clinical pharmacy. The method includes: receiving patient information and proposed medication regimen information, whereby the patient information includes gestational age and information on multiple comorbidities; calling the corresponding gestational age adaptive risk weight matrix based on gestational age, weighting the patient features to generate a weighted feature vector, and inputting this vector into a machine learning model to obtain an initial medication risk score; based on a constructed heterogeneous knowledge graph, using entities corresponding to multiple comorbidities and medication regimen information as query starting points, performing multi-hop reasoning to determine the existence and severity of conflicting paths; if a conflicting path is detected, revising the initial medication risk score according to the severity of the conflict to generate a final risk score. This application enables individualized, interpretable, and evidence-based intelligent risk assessment of proposed medication regimens for pregnant women with multiple comorbidities.

The present application relates to a terminal device for blood sugar management during pregnancy and its cloud server, the terminal device collects physiological data and blood sugar data of a target user including the gestation period, the cloud server obtains a plurality of blood sugar data of reference users from a plurality of terminal devices and forms an overall reference data set in a manner associated with gestational age, the cloud server calculates basic statistical indicators for each group in the overall reference data set, combines the specific percentile position of the user's blood sugar data in its own group distribution, judges the current blood sugar state of the user by comparing the user's blood sugar data with the preset healthy blood sugar range, and generates a graded diet suggestion accordingly, the diet suggestion optimizes the diet structure of the target user by selecting food categories with different GI values; the terminal device receives and visually displays personalized diet suggestions matching the blood sugar status of the target user to the target user.

The invention relates to the technical field of medical health, and discloses a cardiopulmonary ultrasound assessment method and system for premature infant BPD early warning. The method comprises the steps that premature infant heart rate variability, pulmonary artery systolic pressure and respirator parameter data are synchronously collected through an HL7 FHIR protocol interface, and a BPD early warning basic data set is obtained; performing weighted fusion on the ratio of SDNN to RMSSD, the square ratio of pulmonary artery to systemic circulation systolic pressure and oxygenation parameters in the data through an algorithm to obtain a CPDI value; the CPDI and the cardiopulmonary parameters are combined to evaluate the pulmonary vascular resistance load, and a PVRL index is obtained; the CPDI, the PVRL, the gestational age and the body weight adjustment factor are fused, and a BPD occurrence risk score is obtained; and dividing risk levels according to the scoring interval and generating an early warning result. According to the method, the problem that premature infant BPD early warning lacks cardiopulmonary system multi-parameter fusion analysis and individualized risk assessment in the prior art is solved, and continuous dynamic early warning and accurate risk layering based on CPDI and PVRL indexes are achieved.

Methods to compute gestational age and time to delivery utilizing temporal alignment methods and applications thereof are described. Generally, systems utilize analyte measurements collected at one or more time points to determine a gestational age and time to delivery, which can be used as a basis to perform interventions and treat individuals. Computational models trained utilizing temporal alignment of analyte measurements can be used to determine gestational age and time to delivery.

The fragment omics features provide various properties of a sample (e.g., urine or plasma) and / or a subject. The relative contribution or enrichment of clinically relevant DNA (e.g., the type of renal and non-renal urine cfDNA) is provided utilizing the fragment omics characteristics of urine free DNA. Such measurements may be used to reflect glomerular permeability and to monitor various diseases, such as kidney abnormalities. The fragmenomics characteristics may include corrected urine DNA concentration, length, terminal motif of the urine DNA molecule, and cfDNA molecules from open chromatin regions (OCRs) of one or more tissues. Furthermore, the nuclease activity or other fragmentation process of the cfDNA is determined based on the relative contributions associated with different cfDNA cleavage profiles, which are also used to determine the contribution, pathological grade and gestational age of the cfDNA from the tissue.

A group of marker sets capable of effectively predicting acute pain of a patient after cesarean section is screened out, and the marker sets comprise whether the patient has preoperative anxiety or not, whether the patient is gestational diabetes mellitus or not, the age of the patient, the Pittsburgh sleep quality index (PSQI) of the patient, the gestational week age of the patient and the abdominal circumference of the patient. On the basis, a visual and quantifiable column diagram prediction model is constructed, the method can be used for evaluating the risk probability of acute pain occurring after a cesarean section patient is operated clinically before an operation, targeted and individualized pain management is provided for the patient subsequently, the pregnancy safety of the patient is improved, and physical and psychological pain of the patient after the operation is relieved.

The present invention provides a data processing method, medical equipment, storage medium and computer program product. The above method includes: obtaining a target gestational age stage of a target fetus, and based on the target gestational age stage, determining multiple disease categories corresponding to the target gestational age stage in a disease database. Obtaining disease characterization information of the target fetus, the disease characterization information includes one or more of a target cross-sectional image of the target fetus and a disease description text. For each disease category corresponding to the target gestational age stage, the probability of the target fetus suffering from a disease of the disease category is calculated based on the disease characterization information of the target fetus. Based on each disease probability, a final disease category is determined from the multiple disease categories corresponding to the target gestational age stage, and the corresponding standard disease name is output. The present invention is conducive to reducing the difficulty of implementing subsequent medical data statistics.

A method for fetal risk monitoring based on gestational physiological characteristics includes the following steps: collecting physiological indicator data of pregnant women during pregnancy; preprocessing the physiological indicator data; establishing a multimodal model, inputting the preprocessed physiological indicator data into the multimodal model, which outputs the types of abnormal physiological indicators, as well as the fetal risk score and risk level; implementing targeted non-invasive intervention measures based on the risk level and the types of abnormal physiological indicators; continuously monitoring the physiological indicator data after intervention and dynamically updating the risk assessment results. The fetal risk monitoring method and system based on gestational physiological characteristics provided by this invention can monitor the health risks of the fetus based on the physiological characteristics of pregnant women during pregnancy, thereby facilitating prediction before health risks to the fetus occur and helping medical staff gain sufficient intervention time.

The invention discloses a pregnant sow closed-loop precise feeding system and method based on 3D vision and edge calculation. According to the system, a visual module composed of a depth camera and an edge calculation end is integrated on the basis of a traditional feeding station comprising an RFID identity recognition module, an STM32 main controller and a discharging execution mechanism. The method comprises the following steps: after a sow enters a feeding station and is identified by RFID (Radio Frequency Identification Device), acquiring three-dimensional point cloud data of the back of the sow by a depth camera; performing real-time processing and analysis on the point cloud data by a backfat measurement algorithm deployed on the edge calculation end, and calculating to obtain a current backfat thickness value; the thickness value is sent to the STM32 main controller in real time through serial communication; and the main controller dynamically calculates and executes the precise feeding amount according to the real-time backfat data in combination with stored sow gestation day age information. According to the method, the problem that in a traditional feeding mode, decision making is not accurate due to body condition data lag is effectively solved, and the breeding benefits and animal welfare are remarkably improved.

This application provides a method, device, and electronic device for assessing medication risks during pregnancy. Through automated knowledge graph querying and risk calculation, a risk assessment report can be quickly generated, reducing the time and cognitive load required for doctors to manually review literature and memorize complex rules. Simultaneously, it can match the current stage of medication risk to the patient's specific gestational age and perform comprehensive, real-time risk assessment based on the pregnant patient's real-time physiological indicators. This avoids the shortcomings of risk assessments that fail to consider gestational changes and individual patient differences, improving the accuracy of risk assessment. Furthermore, by acquiring medication information and physiological indicators, it can continuously and in real-time calculate the risk of medication use based on the patient's actual medication behavior and changes in physiological state during medication. Based on the real-time risk assessment results, medication guidance can be provided to patients, enhancing their understanding of the medication regimen and reducing the risks of medication use during pregnancy.

The invention relates to the technical field of medical information processing, in particular to a birth defect intelligent diagnosis, treatment, prevention and control method and system based on clinical detection driving, and the method comprises the steps: obtaining a fetus ultrasonic image, gestational week information and clinical detection data; performing organ segmentation on the ultrasonic image, generating segmentation uncertainty information, extracting morphological features and topological features, and calculating a stability index set under a preset image transformation set; constructing an organ evidence graph taking organs as nodes on the basis of the information, and generating a residual evidence set in combination with a gestational week normal development benchmark; determining a candidate mechanism set according to clinical detection data and a mechanism knowledge base, and performing evidence and mechanism bidirectional message transmission by adopting an evidence weight rule to obtain mechanism probability distribution; and determining a minimum irreplaceable mechanism set through a minimum description length criterion and an anti-fact ablation test, and outputting a risk layering result and an active acquisition plan. According to the method, the image evidence utilization reliability and the mechanism inference interpretability are improved, and repeated inspection is reduced.

The present invention discloses a method and system for predicting the development of early cortical folds in the human brain based on a neural network model, which falls into the interdisciplinary field of artificial intelligence and brain science. The method extracts a three-dimensional skeleton of cortical folds using brain negative model skeleton technology, and uses a β-VAE model to capture the complex pattern of folds changing with gestational age, achieving accurate predictions from 30 to 40 weeks of gestation. The method uses a large amount of MRI imaging data to train the model to ensure the accuracy of the prediction results. The system modular design of the method improves scalability and maintainability, and the computer implementation ensures real-time and accuracy. The present invention solves the problem of the lack of neurogenesis heterogeneity information in existing models, provides a new approach for studying the development of early cortical folds in the human brain, and has broad application prospects, such as evaluating the neurodevelopment of premature infants and early diagnosis of diseases.

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 application discloses a kind of based on diet inflammation index gestational diabetes mellitus blood sugar management method, comprising the following steps: S1: input pregnant woman information;Obtain the personalized information of pregnant woman, including: gestational age, pre-pregnancy / current weight, blood sugar risk level, dietary intake frequency, food intake, diet inflammation index initial score;S2: calculate diet inflammation index;Calculate nutrient intake, standardization processing, inflammation weight calculation, calculation;S3: predict blood sugar risk through blood sugar risk prediction module, output risk level;S4: generate diet program, three meals structure, food combination and heat distribution through individualized diet intervention module;S5: monitor blood sugar, adjust menu according to blood sugar change.Can combine carbohydrate and inflammation, establish diet inflammation blood sugar management new mode, realize accurate management.

This invention relates to the field of medical data analysis, and more particularly to a method for assessing the risk of pregnancy outcomes in pregnant women based on multifactor feature analysis. The method includes: acquiring individual measured time-series datasets and baseline reference data for physiological expectations; obtaining a gestational age-specific pathological surge penalty factor by weighting the ratio of angiogenic factors to anti-angiogenic factors; obtaining a cross-system pathological cascade time coupling factor by temporally tracing and coupling abnormal deviations in vascular system indicators and blood system indicators; obtaining a fused feature vector by correcting and fusing the initial linear regression slope features; and obtaining the pregnancy outcome risk assessment result by performing machine learning prediction on the fused feature vector. This method solves the problem that existing prediction methods based on linear regression slope extraction and feature splicing cannot effectively identify gestational age-specific early high-risk signals and cross-system short-term pathological cascade signals.

The present application relates to the technical field of pregnancy test kits, and in particular to an electronic early pregnancy test kit. The present application provides an electronic early pregnancy test kit, comprising a reagent strip, a spectral analysis system, a temperature sensor, a processor, and a display unit. First, an LED light source is used for illuminating the reagent strip, and reflected light is received by means of a corresponding photosensitive sensor; then the temperature sensor is used for monitoring the temperature of the reagent strip in real time, and the processor compensates and corrects a photosensitive signal on the basis of temperature data, and calculates the level of human chorionic gonadotropin by analyzing corrected spectral data; and finally, the gestational age is estimated on the basis of a calculation result and is presented on the display unit, thereby not only improving the accuracy and reliability of test, but also achieving active compensation for environmental factors, while providing a quantitative result and accurate gestational age estimation by means of spectral analysis.

The application discloses a uterine myoelectricity monitoring electrode distribution prompting method and device based on gestational weeks. The method comprises the following steps: inputting gestational week information W, selecting an electrode channel number N of a monitoring maternal abdominal region, wherein W and N are positive integers; constructing a 4*6 matrix and setting a navel position as a reference point; judging the size of the gestational week information W, and selecting an electrode layout according to the N value. According to the gestational week information, the electrode channel number of the monitoring maternal abdominal region is selected, and the electrode layout is selected according to the electrode channel number. The application can more accurately monitor uterine myoelectricity and improve the accuracy of monitoring results.

The invention is directed to methods of identifying woman is risk for preterm delivery. In some aspects, the methods include quantitating one or more placental or fetal-tissue specific genes in a biological sample from the woman.

The present disclosure relates to the treatment of disorders of gastrointestinal (also referred to herein as GI) tract regeneration and / or maturation, in particular in infants, such as preterm infants (also referred to herein as neonates), pregnant low-weight young children, and / or young children born after preeclampsia, by administering a therapeutically effective amount of IGF-1, in particular as a complex with IGFBP, such as IGFBP-3. The treatment may also be used in other patient populations, including adult populations with GI tract conditions / disorders / diseases / and / or infections.

The application of amniotic fluid in preparation of a medicine for treating and / or preventing macrophage-mediated diseases, a medicine for treating and / or preventing M1 type macrophage-mediated diseases, a medicine for treating and / or preventing M2 type macrophage-mediated diseases and a preparation for treating and / or preventing TLR4 / NF-kappa B signal channel-mediated diseases, and the application of amniotic fluid in preparation of a medicine for treating and / or preventing TLR4 / NF-kappa B signal channel-mediated diseases. The invention also discloses an application of the compound in a preparation for inhibiting the number of M1 type macrophage groups and improving the proportion of M2 type macrophage types, or a preparation for promoting transformation of M1 type macrophage to M2 type macrophage. The amniotic fluid comes from eggs with the embryonic age of 5-12 days or eggs of other poultry except for chickens with the development period corresponding to the development period of the eggs with the embryonic age; or from the embryo of a rodent with the gestational age of 8-14 days, or from the embryo of other non-human mammals other than the rodent with the developmental stage corresponding to the developmental stage of the rodent with the gestational age of 8-14 days.

The application provides a method for establishing a fetal fetal lung maturity recognition model based on ultrasonic imaging. +6 The method comprises the following steps: collecting normal fetal lung ultrasonic image data of a gestational age of 20 to 40 weeks; obtaining corresponding image data according to the lung ultrasonic image data and marking a region of interest in each lung ultrasonic image in the image data; performing primary screening of imaging features of the marked region of interest; performing secondary screening of the primary screened imaging features by using ANOVA F test, and performing dimension reduction processing on the features obtained after the secondary screening; taking the imaging features used for establishing the fetal fetal lung maturity recognition model as input, taking the gestational age of a pregnant woman as label as output, training an AdeptiveXGBoost classifier, saving the trained AdeptiveXGBoost classifier, and obtaining the fetal fetal lung maturity recognition model. The application can achieve the purpose of recognizing and predicting the fetal lung maturity of a fetus by using a plurality of finally screened imaging features.

The present application relates to the technical field of medical data processing, and discloses a gastrointestinal symptom prediction system, which compares the change trend of the superior mesenteric artery ultrasound data and the middle cerebral artery ultrasound data of a to-be-tested newborn with the change trend of the superior mesenteric artery ultrasound data and the middle cerebral artery ultrasound data of a group of newborns without gastrointestinal symptoms and a group of newborns with gastrointestinal symptoms in a corresponding gestational age range, so as to accurately predict whether the to-be-tested newborn has gastrointestinal symptoms, help to deeply understand the gastrointestinal condition of the newborn, provide reliable data guidance for the clinical nursing of the newborn, and reduce the probability of the newborn suffering from gastrointestinal diseases.

A placental imaging system comprises a hand held ultrasound imager for scanning over the lower abdomen of a patient and configured to generate image data. An AI algorithm is used to identify the location and shape of the placenta and cervix within the image data and determine the placenta to internal OS distance. It is thereby determined if the placenta has a positional abnormality, such as if it is low lying or not. A recommendation system receives a gestational age of the patient and the positional abnormality information to recommend a patient action.

The invention discloses a fetal brain age prediction method and system based on self-supervised structure perception fusion. The method comprises the steps of obtaining a fetal brain MRI image, and performing preprocessing and image segmentation to obtain a plurality of image blocks; calculating a structural complexity score for each image block, and generating a corresponding structural mask based on the structural complexity score; shielding the fetal brain MRI image through a structural mask to obtain a shielding image; inputting the occlusion image into a pre-trained structure perception encoder to obtain a structure perception feature map; inputting the fetal brain MRI image into a supervision encoder to obtain a high-level semantic feature map; and fusing the structure perception feature map and the high-level semantic feature map to obtain fused features, and inputting the fused features into a global average pooling layer and a full connection layer to obtain a gestational week prediction value. According to the invention, the precision and robustness of gestational week prediction are improved in a small sample and weak labeling scene.