34 results about "Gestational Weeks" patented technology

The gestational wheel calculator of the current invention greatly extends the function and usefulness of the basic gestational calculation wheel by providing increased accuracy of gestational dates by using an average cycle length adjuster, provides paternity information using a sperm exposure marker, provides more accurate ultrasound and other test measurements by using scales printed parallel to gestational age, and provide a means of accurately aligning these measurements with the proper calendar or gestational date by including a transparent marker arm. In addition, normal range and error functions are included on the marker arm. A one-fourth year window is used to increase the surface area of information available for viewing charts, tables, and promotions on the underlying base plate. Easier reading of the calculator is provided by special markers for the first day of each calendar month and by providing sub-marks for each day within a gestational week. Scheduling conflicts are improved by providing a weekday scale covering the entire nine month duration of pregnancy. The other side of the gestational wheel can be used to provide additional promotional or informational items including rotating transparent windows to display additional surface area, to provide means of calculating algorithm solutions for body mass index, and display observational data for EFW, delta OD 450, and AFI.

The invention provides an image processing method for automatically judging fetal hydrocephalus from ultrasonic images, comprising the following steps: firstly extracting skull ellipse of a new fetal brain image on the second plane, lateral ventricle horizontal plane; establishing a space histogram pyramid in the interior zone of the ellipse in the image; respectively calculating the distances among the space histogram pyramids of the given fetal ultrasonic image, the normal fetal brain images in the same gestational week and the image of hydrocephalus in the brain on the lateral ventricle horizontal plane in the same gestational week; and setting a threshold and giving the possibility of fetal hydrocephalus from judgment according to the relationship between the distances among the space histogram pyramids and the threshold.

A body composition management apparatus for a pregnant woman which measures body fat or body water by a bioelectrical impedance method is provided. The apparatus comprises: first input means, second input means, measurement means, first computation means, and second computation means, wherein the first input means inputs personal data of a pregnant woman such as a body height and a body weight, the second input means inputs the gestational weeks, the measurement means measures the bioelectrical impedance of the pregnant woman, the first computation means computes a body fat mass or a total body water from the personal data and the impedance, and the second computation means computes a body fat mass or total body water in a non-pregnant state from the computed body fat mass or total body water and the amount of an increase in body weight from the non-pregnant state.

The invention discloses a method for prenatal screening chromosome abnormality and an application thereof. The invention comprises the following steps: totally calculating various measurement indicators of normal pregnant women in China to obtain the median of serum marker of single pregnancy people in each gestational week, the median of serum marker of double pregnancy people in each gestational week and the median of translucent thickness of fetal nape of normal pregnant women in each gestational week; detecting the serum marker, fetal number, translucent thickness of fetal nape of the detected pregnant women to determine the gestational weeks of the pregnant women; correcting according to the weight of the detected pregnant women and finally calculating the multiples of the median value (MoM value) of serum marker; then comparing correspondingly the multiples of the median of serum marker of the detected women with the median of serum marker of normal pregnant women according to the gestational weeks of the pregnant women and fetal number, calculating the likelihood ratio of the serum marker of the detected pregnant women to obtain the risk rates of 21 trisome, 18 trisome or 13 trisome and the risk rate of NTD risk.

The invention relates to a method for jointly detecting sFlt-1/PLGF and HLA-G for predicting pre-eclampsia. The method comprises the following steps: collecting serum specimen: respectively collecting5-10 ml of venous blood from pregnant women in a pre-eclampsia group and normal pregnant women with same gestational weeks, centrifuging all specimens in a low temperature centrifuge at 3000 rpm for10 min within 4 h, taking the serum and subpackaging in EP tubes, and storing in a refrigerator at minus 80 DEG C for testing; coating: using a 0.05 M PH9.0 carbonate coated buffer solution to diluteantibodies to the protein content of 1-10[mu]g/ml, adding 0.1ml of the diluted antibodies into reaction holes of each polystyrene board, staying overnight at 4 DEG C, discarding the solution in the holes the next day, and washing for 3 times; setting up standard holes and sample holes, washing, and setting up blank holes, negative control holes and positive control holes at the same time; adding enzyme-labeled antibodies; adding 100 [mu]L of horseradish peroxidase-labeled detection antibodies into each of the standard holes and the sample holes except for the blank holes, sealing the reactionholes with microplate sealers, and incubating in a water bath kettle or an incubator at 37 DEG C for 60 min; performing board-washing; adding 50 [mu]L of a substrate A and a substrate B into each of the holes, and measuring OD values of each hole. The method is high in detection specificity and sensitivity.

According to the invention, the ratio of biomarkers leptin to ceramide related to preeclampsia is found for the first time, and early diagnosis of preeclampsia is realized by detecting the level change of the ratio of leptin to ceramide of a subject. The method can be used for evaluating the preeclampsia disease risk during pregnancy, has high accuracy and specificity, and provides an effective method for early prediction and precise prevention of preeclampsia. In the early stage of morbidity, timely and accurate auxiliary diagnosis is carried out on pregnant women with preeclampsia risks, clinical close monitoring on patients is helped, effective intervention is timely carried out to control the illness state and prolong the gestational week, and the method has important significance in reducing the preeclampsia morbidity and mortality rate and guaranteeing the safety of mothers and infants.

The invention relates to the field of obstetrics, in particular to a gestation hypertension risk dynamic evaluation method based on the change of the physical and chemical information of pregnancy. The invention is characterized in that different evaluation criteria can be selected according to the pregnancy cycle, automatic identification is carried out on the physical and chemical information, and the change tendency of the information parameters in the gestation period is monitored, and whether the information parameters are abnormal or not is evaluated according to the comparison between the physical and chemical information and the experimental values. Aiming at different pregnant women or the same pregnant women at different gestational weeks, the doctor can use the historical medical information, the current physical and chemical information and the diagnosis and treatment experience, the risk weights of the physical and chemical parameters are adjusted, and the risk that the pregnant women suffer from hypertension and related complications is evaluated, so that the medical process has good flexibility. According to the evaluation method, the pre-pregnancy medical history iscombined with the post-pregnancy information, the dynamic monitoring is combined with the static estimation, and the current detection is combined with the trend estimation, the single detection andthe overall evaluation are combined, so that the monitoring can have real-time ability, prediction performance and pertinence, and the intervention can be carried out on the pregnant woman in advance,and the harm of the mother and the baby can be reduced.

The invention relates to a detection device for judging early pregnancy time and early pregnancy abnormity. The detection device comprises multiple test strips and a cavity for accommodating the multiple test strips, wherein the six test strips are sequentially arranged in the cavity of the detection device and used for detecting different hCG (human chorionic gonadotropin) concentrations; an hCGcritical value detected by a first test strip is 25 mIU/ml, an hCG critical value detected by a second test strip is 100 mIU/ml, an hCG critical value detected by a third test strip is 500 mIU/ml, anhCG critical value detected by a fourth test strip is 2000 mIU/ml, an hCG critical value detected by a fifth test strip is 10000 mIU/ml, and an hCG critical value detected by a sixth test strip is 25000 mIU/ml. A product is lower in detection sensitivity, larger in detection range and longer in gestational week tracking cycle, and can detect the gestational week to the ninth week. Besides, each test strip of the product can complete detection only by one detection line, a result is more direct, the process is simpler, the product is more stable, and industrialization is easier to realize.

The invention discloses a method for detecting skull abnormity of a fetus based on machine learning. Particularly, whether a major tissue structure of a skull is abnormal or not and which related skull diseases will be caused by abnormity are detected during the development of the skull of the fetus. According to the method, whether the major tissue structure of the skull is abnormal or not is mainly detected through acquiring data of standard sections of skulls of different gestational week series, preprocessing the data and training a model. The method comprises the steps: extracting features by adopting a deep convolutional network, generating candidate regions by a region proposal network RPN, collecting an input characteristic graph and the candidate regions by an interested pooling layer, carrying out classifying and regression detection by using a softmax classifier, and finally analyzing whether a primary structure is abnormal or not by using a detection result; and judging that the primary structure is normal if no abnormity is detected. The method and the system have the aim of aiding diagnosis by using a computer, and whether the skull is abnormal or not is diagnosed inan aided manner under the condition that doctors or people have no need of excessively participating in diagnosis.

The invention discloses a target gene combination related to gestational age and application thereof. Based on the target gene combination, the target gene combination comprises TSPOAP1, CRYAA, CXCL11, FNDC10, HMGA2, FDCSP, OLFM2, KRT36, C13orf46, SCD5, OR12D2, OMP, CEBPE, MOBP, CCNF, FUT2, LIPH, ERICH4, GPR171 and the like. According to the method, the distribution condition of peripheral blood free DNA in some gene transcription start site areas can reflect different gestational weeks, and after the characteristics of the free DNA transcription start site areas are subjected to homogenization correction, a prediction model constructed by using a machine learning algorithm can effectively predict the fetal age of a fetus.

The invention discloses a method for hypertensive disorders of pregnancy by combining D-dimer with alpha fetoprotein and free[beta]-hCG in maternal serum during mid-pregnancy. The method comprises thesteps of dividing pregnant women into three case groups and a control group according to the existence or absence of gestational hypertension GH, preeclampsia PE and severe preeclampsia group SPE; detecting the levels of D-dimers, AFP (alpha fetoprotein) and free[beta]-HCG of the four groups of pregnant woman serum specimens, and representing the measured levels of the D-dimers, the AFP and the free[beta]-HCG by the median multiples MoM of the weight and the gestational weeks; and comparing median multiple MoM distribution conditions, and constructing a risk calculation model by using MOM values in combination with the weight and the gestational weeks. The method has the beneficial effects that the risk calculation model constructed by calibrating MOM values of the D-dimer, the AFP and the free[beta]-HCG in serum of the pregnant woman in combination with the weight and the gestational weeks has relatively high sensitivity and specificity when being used for screening the hypertensivedisorders of pregnancy HDP, and can become a new marker for predicting the hypertensive disorders of pregnancy HDP.

The invention relates to the technical field of pregnant woman supplies and health management, in particular to a big data-based pregnant woman health management system, which comprises a cloud platform used for acquiring a collection data group formed by physical sign data, environmental data, public data and third-party data of a user, and also used for generating a single monitoring report and a comprehensive monitoring report according to the acquired data group, comparing the single monitoring report and the comprehensive monitoring report with scientific data in a normal range, and forming a comparison result, and a user side comprising daily information, a monitoring report, warning information and compensation scheme release. The pregnant woman health management system simulates a fetus 3D development image on an intelligent terminal of a user according to gestational weeks and pregnancy and delivery science. A user identifies the type of diet through intelligent terminal filling or AR scanning and matches the composition and content of energy, vitamins, trace elements and the like of the diet in the database, and a nutrition compensation scheme is generated and sent to the intelligent terminal of the user for user reference.

The invention discloses a method for predicting birth weight of a full-term newborn at 21-23 weeks of pregnancy. The method comprises the following steps: S1, building a newborn weight prediction model that is EFW = 5016.51-205.71*X1+16.30*X2+102.19*X3+86.64*X4, wherein, EFW is the predicted neonatal weight in g; X1 is the accurate gestational week during the ultrasonic examination, and X2 is the body mass index (in kg/m<2>) of a pregnant woman at second trimester B-ultrasonic examination; X3 is the volume of partial thighs of a fetus at ultrasonic examination in cm<3>; and X4 is the abdominal girth (in cm) of the fetus at ultrasonic examination; S2, collecting gestational weeks and progestational body mass indexes of the pregnant woman; S3, carrying out two-dimensional ultrasonic measurement on the abdominal girth of the fetus at 21-23 weeks of pregnancy; S4, carrying out three-dimensional ultrasonic measurement on the volume of partial thighs of the fetus at 21-23 weeks of pregnancy; and S5, substituting data collected in the steps S2-S4 into the newborn weight prediction model, and calculating the predicted weight of the newborn. The method can improve the accuracy of predicting the birth weight of the full-term newborn, and be used for advancing maternal management to the second trimester, thereby scientifically improving pregnancy results.

The invention discloses an intelligent prenatal fetal heart rate monitoring model which comprises the following steps: preprocessing a fetal heart rate signal, a uterine contraction signal, the age of a pregnant woman and the gestational week to obtain a prenatal standardized fetal heart rate data set, a uterine contraction signal data set and a basic information data set of the pregnant woman; then designing a convolutional neural network to perform high-dimensional feature extraction on the preprocessed fetal heart rate signal and uterine contraction signal; a base classifier LGBM is designed and selected according to the characteristics of the prenatal fetal heart monitoring data; fusing the extracted high-dimensional features of the signals with the preprocessed basic information of the pregnant woman to form multi-modal features, and inputting the multi-modal features into a base classifier LGBM to obtain a classification judgment result; according to the intelligent antenatal fetal heart rate monitoring model, features of four different modes including fetal heart rate signals, uterine contraction signals, pregnant woman age and pregnant weeks are fused, a multi-mode feature deep learning strategy is adopted for training, a design base classifier LGBM is selected for classification and discrimination, and compared with other machine learning models based on the fetal heart rate and uterine contraction signals, the model has the advantages that the model is simple in structure and convenient to use. Compared with other traditional machine learning models based on clinical features, the method has better classification performance compared with deep learning models of different feature combinations.

The invention discloses a gestational week determination method and system. The method comprises the following steps: receiving a selection instruction, wherein the selection instruction comprises a logo of a gestational week computing method; searching the gestational computing method according to the logo so as to acquire a name of an input parameter corresponding to the gestational week computing method; generating and displaying a parameter input interface according to a preset display rule, wherein the parameter input interface comprises the name of the input parameter, and a parameter value receiving control corresponding to the name of the input parameter; receiving the input parameter value according to the parameter value receiving control; when receiving the gestational computing instruction, determining the gestational week using the input parameter value according to the gestational week computing method. Through the adoption of the method disclosed by the invention, the time is saved, the probability of inputting error information is reduced, the probability of computing error gestational week or incapable of computing the gestational week is reduced, the gestational week can be fast, simply and accurately determined, and the method and the system are suitable for application.

The invention particularly relates to a detection card and a detection method for rapidly detecting early pregnancy and gestational weeks. The detection card comprises a detection strip, a pen cap anda pen-shaped card body, wherein the detection strip comprises a matrix support plate, absorbent paper, an analysis membrane, a gold-labeled combination pad and a sample pad, a gold-labeled mouse anti-HCG-alpha antibody is fixed on the gold-labeled combination pad, wherein the analysis film is coated with a detection line and a quality control line, the quality control line containing goat anti-mouse IgG antibodies is located on the right side of the detection line, the detection line comprises a detection line T1, a detection line T2 and a detection line T3 containing mouse anti-HCG-beta antibodies with different concentrations, and the HCG concentration range in urine is rapidly detected through a detection card. According to the detection card, the number of the detection lines is increased, different sensitivities are set, HCG hormones are rapidly detected, the concentration range of HCG in a urine sample is simply determined, the week of pregnancy is calculated according to the concentration range, and therefore a purpose of judging early pregnancy and week of pregnancy is achieved, an accurate week of pregnancy detection result is provided for women subjected to early pregnancy testing, and application and popularization are facilitated.