76 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.

An anthropometric growth apparatus (101, 201, 501) for measuring the anthropometry of an infant is provided. The apparatus comprises a base (103, 203, 503) with a digital scale assembly (105, 205, 505) mounted to the base and configured to measure a weight of the infant. A digital length measurement assembly (107, 207, 507) is mounted to the base and configured to measure a length of the infant. A head circumference measurement assembly (109, 209, 509) is mounted to the base and includes an array of distance sensors (111, 211, 511) provided on a support (113, 213, 513) configured to position the array of distance sensors at a selected distance from the head of the infant for non-invasive measurement of the head circumference in use. The apparatus may further be used for body composition and gestational age determination.

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 provides a data processing method, medical equipment, a storage medium and a computer program product. The method comprises the following steps: acquiring a target gestational week stage of a target fetus, and determining a plurality of disease categories corresponding to the target gestational week stage in a disease category database based on the target gestational week stage; acquiring disease characterization information of the target fetus, wherein the disease characterization information comprises one or more of a target section image and a disease description text of the target fetus; and for each disease category corresponding to the target gestational week stage, based on the disease characterization information of the target fetus, calculating the disease probability that the target fetus suffers from the disease of the disease category. And based on each disease probability, determining a final disease category in a plurality of disease categories corresponding to the target gestational week stage, and outputting a standard disease name corresponding to the final disease category. According to the invention, the realization difficulty of subsequent medical data statistics can be reduced.

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.

Provided is a method for diagnosing or predicting preeclampsia in a pregnant woman or determining if a pregnant woman has a risk of preeclampsia, comprising: a) obtaining at least one (such as two or more) maternal or fetal parameter or factor of the pregnant woman, selected from a group comprising: (i) one or more of mean artery pressure (MAP), BMIsampling, ΔBMI, Gestational age and ΔBMI / GA; (ii) Maternal age; (iii) one or more of Urinary protein level and the ratio of Urinary protein level (such as Urine Total Protein (UTP) ) / Urinary Creatinine level (Cre) (UTP / Cre); (iv) one or more of preeclampsia in previous pregnancy (PEhis) and having received antihypertensive medication (Antihyper); and (v) any combination of (i) - (iv), b) determining if the urine of said pregnant woman contains misfolded proteins or misfolded protein aggregates, and c) combining the at least one maternal or fetal parameter or factor obtained in step a) and the content level of misfolded proteins or misfolded protein aggregates in the urine of said pregnant woman to diagnose or predict preeclampsia or determine the risk of preeclampsia.

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 present disclosure provides biomarker panels, methods and kits for determining the probability for preeclampsia in a pregnant female, including preterm preeclampsia or preeclampsia at any gestational age. The disclosure is based, in part, on the discovery that certain proteins and peptides in biological samples obtained from a pregnant female are differentially expressed in pregnant females that have an increased risk of developing, in the future, or presently suffering from, preeclampsia relative to matched controls. The disclosure is also partially based on the unexpected discovery that panels combining one or more of these proteins / peptides can be utilized in methods of determining the probability for preeclampsia in a pregnant female with relatively high sensitivity and specificity. These proteins and peptides disclosed herein serve as biomarkers for classifying test samples, predicting a probability of preeclampsia, monitoring of progress of preeclampsia, either individually or in a panel of biomarkers.

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.

Embodiments of the disclosure concern methods and compositions related to optimization and selection of cord blood units for production of immune cells, such as NK cells, for adoptive cell therapy use. In specific embodiments, particular characteristics of the cord blood units and / or characteristics of cells derived therefrom are analyzed. When a threshold measurement for one or more characteristics is met for the cord blood unit(s) and / or characteristics of cells derived therefrom, the cord blood unit(s) are utilized as a source for production of immune cells. Specific characteristics for measurement include, for example, cord blood cell viability, time from birth to cord blood unit cryopreservation, total nuclear cell recovery, baby weight, baby gender, mothers age, gestational age, CD34 positive cell percentage, and / or nucleated red blood cell content. Characteristics may be determined prior to and / or post-cryopreservation.

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.

The present invention relates to an immune device for determining pregnancy or abnormal pregnancy and, more specifically, to an immune device including a novel multi-binding anti-hCG monoclonal antibody capable of commonly recognizing intact hCG and βcf hCG, wherein the immune device includes two detection lines and a control line to separately detect intact hCG and βcf hCG contained in a specimen, and the ratio of the color intensity of βcf hCG to the color intensity of intact hCG and βcf hCG (the distribution ratio of βcf hCG) are digitalized, thereby accurately and quickly determining gestational age, normal pregnancy, and abnormal pregnancy including ectopic pregnancy, and blight ovum.

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 present invention provides a risk prediction model for complications during pregnancy and its application, involving ABHD17A, CFAP157, CTNNBIP1, CTR9, FAM227A, LILRA5, NPTN, NSD1, OBP2B, OR10H4, PAQR9, PLK3, PRSS58, SLC45A1, SP140L, SSR3, STAT1, STK31, TLE1, TMEM238 genes and age, gestational age, ffDNA, T21, T18, T13, BMI, conception method, non-invasive indication, adverse pregnancy and delivery history, whether multiple births, and clinical indications of the number of previous cesarean sections. The model provided by the present invention can efficiently and quickly predict complications during pregnancy and has broad application prospects.

The invention provides a growth curve fitting method and device, equipment and a storage medium, and the method comprises the steps: receiving a plurality of measurement samples sent by at least one ultrasonic machine, the measurement samples comprising measurement results of biological indexes which are related to the gestational age in pregnant woman information and represent the growth state of a fetus; according to the measurement result of each biological index, adding the plurality of measurement samples into corresponding biological index sets, and grouping the measurement samples in each biological index set according to gestational age; for each biological index, determining probability distribution parameters corresponding to each group of measurement samples in the corresponding biological index set, and fitting the probability distribution parameters to obtain a fetal growth curve under the biological indexes; manual participation is not needed, the efficiency of establishing the fetal growth curve suitable for a certain region is improved, and the accuracy and prediction effect of doctor diagnosis are improved.

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.