128results about How to "Reduce missed diagnosis rate" patented technology

The invention belongs to the technical field of medical image identification and discloses an intelligent canceration cell identification system and a method thereof, a cloud platform, a server and acomputer, which comprise an image acquisition module, a front-end processing module, an expert cloud platform, a diagnosis and suggestion module and a display module. The image acquisition module is used for acquiring the image information of a to-be-identified cell specimen. The front-end processing module is used for carrying out the compression processing on the acquired image data of the to-be-identified cell specimen. The expert cloud platform is used for building a software platform on a cloud server side to establish an image analysis system, establishing a cell image training databaseby utilizing a deep learning convolutional neural network, identifying the acquired image information to find out determined lesion cells, and outputting the determined lesion cells to the display module. The diagnosis and suggestion module is used for displaying a symptom result, uploading the result to the expert cloud platform and providing the condition identification reference information with a certain maximum probability. The display module is used for displaying the symptom result which is accurately identified. According to the invention, a lot of manual time is saved and the missed diagnosis rate is further reduced. Meanwhile, the labor cost is reduced.

The invention discloses an image processing method and system, and image processing model training method and system. The image processing method comprises: obtaining a to-be-detected image; inputtingthe to-be-detected image into a neural network model for processing to obtain a bone segmentation result, a bone center line segmentation result and a bone fracture detection result; wherein the neural network model is determined by machine training learning based on a training image. The bone segmentation, bone center line segmentation and bone fracture detection functions are achieved at the same time through the trained deep learning network, the total consumed time can be shortened by 50%, the memory space of the model can be saved by 40%, and meanwhile a doctor can be helped to reduce the film reading burden, accelerate the film reading time, reduce the missed diagnosis probability and reduce the contradiction between the doctor and the patient.

The invention discloses an antigen antibody complex dissociation liquid kit, which is prepared by blending reagents of 0.3 volume percent of Triton-X100, 1.5 volume percent of lauryl sodium sulfate and 1.5 volume percent of 3-[(3-cholane amidopropyl)-dimethylammonium]propanesulfonic acid in equal amount. The invention also discloses application of the kit in separating a pathogenic antigen antibody complex. The kit of the invention effectively realizes rapid dissociation of the pathogenic antigen antibody complex in a serum or blood plasma sample to be detected, increases the absolute concentration value of an antigen and an antibody, and greatly improves the detection sensitivity of the sample to be detected.

The invention provides a working method of a medical diagnosis auxiliary system, and the method comprises the following steps: inputting symptom information into a server, and sampling patient examination data; enabling the server to identify keywords and related sentences in the symptom information and extract the corresponding keywords; according to sample training and statistics obtained from historical medical record data, obtaining detailed features of the disease, finding out potential patients, and depicting a disease model; and matching the depicted disease model with a diagnosis modelin a database to obtain a diagnosis model result closest to the symptom information of a patient. The accuracy of data analysis is further improved through continuous data accumulation; compared withInternet inquiry in the prior art, the method improves the accuracy by 70%; the new analysis vocabularies and logic algorithms are continuously expanded according to logic operation of data, the diagnosis capacity of doctors is improved through auxiliary diagnosis, and the misdiagnosis rate and the missed diagnosis rate are reduced. Doctors apply medicine according to symptoms and actual conditions and query results of the method, and the accuracy rate can reach 100%.

The invention discloses a capsule endoscopy with automatically adjusted shooting frequency and a method thereof. The capsule endoscopy comprises a capsule endoscopy shell, a battery, a camera unit, a control module, a wireless signal transceiving module, a coil, a current inductor, a frame rate control module and a magnetic field generator, wherein the coil, the current inductor and the frame rate control module are arranged in the shell; the external magnetic field generator generates a magnetic field covering an activity range of the capsule endoscopy; the current inductor are connected with two poles of the coil to form a capsule motion signal; one end of the control module is connected with the current inductor and receives the capsule motion signal, while the other end is connected with the frame rate control module to transmit a control signal for adjusting the shooting frequency; and the frame rate control module is connected with the camera unit and directly controls the shooting frequency. The shooting frequency of the capsule endoscopy is controlled through a device for automatically adjusting the shooting frequency according to the movement of the capsule endoscopy, so that the electric quantity of the capsule endoscopy is saved, the miss shooting rate is reduced and the number of repeated images is reduced.

A thickness balancing method and device for a breast image. The thickness balancing method for the breast image includes the steps of: obtaining a breast image; filtering the breast image to obtain a low frequency breast image and a high frequency breast image; performing gray scale transformation on a preset area in the low frequency breast image to obtain a first image, a gray value of a preset area in the first image tending to be consistent with a gray value of a neighborhood, and the preset area referring to an area at a predetermined distance from an edge of the low frequency breast image; and reconstructing the first image and the high frequency breast image to generate a thickness-balanced breast image. The breast image after balancing which is obtained through the technical scheme of the invention has uniform gray scale while details of the breast image are not lost, satisfies actual clinical needs, the breast image after balancing is adopted to be diagnosed on a window level of a certain window width, and loss of edge information of the breast image does not occur, thereby lowering the rate of missed diagnosis, and improving the accuracy rate of diagnosis.

The invention provides a single-lead electrocardio abnormal signal identification method based on generative adversarial networks and convolution recurrent neural networks, and mainly solves the problem that data concentration samples are unbalanced. Categories being low in data concentration data volume are subjected to data enhancement, and then identification and classification of electrocardioabnormal signals are performed, so that reference is provided for assistance of doctors, the wrong diagnosis rate and missed diagnosis rate are reduced, and the workload of the doctor can be alleviated. The generative adversarial networks are used for enabling the data concentration samples to achieve the relative balance, so that training the convolution recurrent neural networks can be realized, and good classification effects can be achieved.

The invention discloses a high-speed milling chatter on-line identification method based on an AR model. The method comprises the steps that (1) the state information of the milling process is acquired; (2) forced vibration frequency filtering is carried out on a signal; (3) chatter sensitive frequency band filtering is carried out on the signal; and (4) a model characteristic root index R(k) is constructed based on the difference of the AR model of the signal in a stable milling state and a chatter milling state, time varying AR(1) modeling is carried out on the signal in the stable milling process, and a recursive least square method with a forgetting factor is used for identification to obtain the variation of the model characteristic root R(k) of the model in the whole cutting process to identify chatter. Compared with a traditional chatter detection method, according to the method, characteristic information reflecting chatter and characteristic information irrelevant to the chatter are separated, and substantive characteristic parameters of a milling system are obtained; and the physical property of the milling chatter is represented substantially, sensitivity, precision and reliability of chatter detection are effectively improved, and the misdiagnosis rate and the missed diagnosis rate are decreased.

The invention discloses an automatic milling fluttering alarming threshold value setting method based on the 3 sigma rule. The method comprises the steps that firstly, the state information in the milling process is obtained; secondly, forced vibration frequency filtering is conducted on signals, and fluttering sensitive frequency band filtering is conducted on the signals; thirdly, the obtained signals are subjected to feature extraction, and fluttering identification indexes are selected according to needs and calculated; fourthly, the indexes are subjected to the normal distribution check through the normal distribution ration assuming checking method; fifthly, after the normal distribution check, the threshold value section [mu-3 sigma, mu+3 sigma] is set according to the 3 sigma rule, wherein mu is the average value of the fluttering indexes, and the sigma is the standard deviation of the fluttering indexes; and sixthly, fluttering identification is conducted, and the time when continuous three points of the fluttering identification indexes exceed the threshold value section serves as the fluttering alarming time. The reliability of milling fluttering identification is high, and the misdiagnosis rate and the diagnosis missing rate are reduced.

The invention provides a clinical history database diagnostic system based on a four-modal medical image. The clinical history database diagnostic system based on the four-modal medical image comprises a CT image device, a PET image device, an SPET image device, an FMI image device, an image gathering module, a network communication module and a terminal processor, wherein the input end of the image gathering module is respectively connected with the CT image device, the PEDT image device, the SPET image device and the FMI image device, the output end of the image gathering module is connected with the network communication module, the image gathering module gathers four-modal medical images, encapsulates four paths of image signals into a video stream and sends the video stream to the network communication module, the network communication module is respectively connected with the image gathering module and the terminal processor so as to send the received video stream to the terminal processor, and the terminal processor diagnoses illnesses and establishes a clinical history database through detection of the similarity between the medical images and a feature database. The clinical history diagnostic system based on the four-modal medical image can establish a perfect clinical history database system and has a broad application prospect.

The invention provides an innovative structured form labelling method using support documents, so that the documents can achieve the processible effect through medical research and development software. The structured labelling method makes up the shortage that the resource integration of a clinical decision support system is not timely and the sorting of information is not smooth; the data information is labelled and then quickly classified to facilitate query and use, and help a doctor to diagnose the condition of a patient in detail and accurately; the system implements whole-process monitoring from receiving an electronic medical record to diagnosing a patient finally, and respectively does the prompt record, alert analysis and missed and delayed diagnosis analysis before, during and after the diagnosis, so as to achieve the true control in the whole medical process. The structured form enables the doctor to have a basis in the diagnosis, controls the wrong action in the diagnosis process fundamentally, and effectively prevents the occurrence of the adverse events such as the frequent medical accidents, continuous medical disputes and tense doctor-patient relationship.

The present invention discloses a bone image processing method, an electronic device and a computer readable storage medium. The method includes the following steps: splitting a bone image, and acquiring a bone part in the bone image as a label image; inputting the label image into a fused neural network to obtain a fused output result, wherein the fused neural network includes a classification neural network and a detection neural network; and outputting the fused output result, wherein the fused output result includes the fracture location and fracture probability of the bone image. A doctorcan know the possible location of the fracture in the bone image according to the fused output result including the fracture type, location and probability of the bone image without full-coverage untargeted examination of the bone image, so the manual workload of the fracture detection is greatly reduced, and the rate of missed diagnosis is also greatly reduced.

The invention provides a method and a system for analyzing 18-lead dynamic electrocardiograms on the basis of Wilson lead. The method includes arranging 10 electrode slices at preset positions of the body surface of a tested person, acquiring electrocardiogram signals of the tested person by the aid of each electrode slice and processing the electrocardiogram signals to obtain corresponding electrocardiogram data; acquiring 12-lead electrocardiogram data of the tested person according to the electrocardiogram data of the tested person; computing X-axis, Y-axis and Z-axis data on the basis of Wilson lead systems according to the 12-lead electrocardiogram data of the tested person; deriving lead data for representing the left ventricle of the tested person on the basis of the Wilson lead systems and deriving lead data for representing the right ventricle of the tested person on the basis of the Wilson lead systems; generating the 18-lead electrocardiograms of the tested person. The method and the system have the advantages that 12-lead electrocardiograms can be recorded and synchronized by the aid of the improved Wilson lead systems, and the 18-lead electrocardiograms can be calculated from the 12-lead electrocardiograms.

The invention discloses a G6PD (Glucose-6-Phosphate Dehydrogenase) deficiency disease gene detection kit, comprising a gene chip, wherein a mutation detection probe designed for the gene mutation site of glucose-6-phosphate dehydrogenase and a corresponding normal control probe are arranged on the gene chip; the mutation detection probe is at least one sequence among SEQ ID Nos: 1-13 or reverse complementary sequences of the sequences; the normal control probe is at least one sequence among SEQ ID Nos: 4-14 or reverse complementary sequences of the sequences. The G6PD deficiency disease gene detection kit further comprises a group of primers for amplifying the gene sequence of the clinical sample G6PD, wherein the DNA sequences of the primers are as shown in SEQ ID Nos: 25-34. The G6PD deficiency disease gene detection kit is capable of directly diagnosing the genotype of a target to be detected and greatly reducing the rate of missed diagnosis of female heterozygote; and the detection time is shorted to about 3.5 hours so that quick detection of G6PD gene is realized.

The invention belongs to the technical field of medical diagnosis, and particularly discloses a double check comprehensive diagnostic apparatus guiding ultrasound by infrared thermography and a detection method of the diagnostic apparatus. The diagnostic apparatus comprises a worktable which is provided with an infrared thermographic collecting device for general check-up and an ultrasonic detection device for precise check of local areas as well as a computer and an image display connected with the two devices. The computer has software which can simultaneously or respectively control the two devices. The software has the function of analyzing and processing infrared and ultrasonic data image information and is provided with a database. The diagnostic apparatus can obtain and mutually corroborate functional and structural image check results by adequately using advantages of the infrared thermographic technology and the ultrasonic detection technology so as to quickly and effectively diagnose or pre-warn diseases more specifically, so that patients can be immediately and accurately treated in the early stage of diseases. The medical cost is lowered, and the misdiagnosis rate, the rate of missed diagnosis and the death rate of the diseases can be greatly reduced.

The invention discloses a thyroid CT image abnormal density detection method. The method comprises the steps of acquiring CT images, reading the CT values of the pixels of the CT images and obtaining the judgment result of the anomalies of the CT values. According to the thyroid CT image abnormal density detection method of the present invention, a judgment algorithm is adopted to automatically search a location area where abnormal density exists in a thyroid CT image, and therefore, whether an abnormal density value exists in the thyroid CT image can be automatically judged, and abnormal density information can be provided for a doctor rapidly and timely, and the doctor can be automatically assisted to determine the location area where the abnormal density value exists; and a threshold optimization algorithm is adopted to calculate the optimal value of the threshold value of the CT value of the thyroid CT image, and therefore, the accuracy of the recognition of the abnormal density area can be further improved, the missed diagnosis rate of the doctor can be effectively reduced, and the workload of the doctor can be greatly reduced. The method of the invention is not affected by man-made subjective factors, and therefore, interference on pathological examination and other checks caused by the man-made subjective factors can be avoided, and the operation efficiency of a detection system and the work efficiency of the doctor can be greatly improved.

The invention relates to the technical field of medical instruments, and discloses an intelligent assistant judgment system for a cervical image and a processing method thereof. The intelligent assistant judgment system for a cervical image comprises a colposcope detection device and an assistant judgment device. Through combining the colposcope detection device and the assistant judgment device, a to-be-detected cervical image is acquired by using the colposcope detection device, the assistant judgment device is matched to carry out comparison and analysis on the to-be-detected cervical image and feature data thereof, whether the current to-be-detected cervical is a normal cervical can be judged, the feature data of the to-be-detected cervical image can also be used to obtain which lesion type the current to-be-detected cervical possibly belongs and feature parameters of the lesion, a doctor can be assisted to make a right diagnosis and judgment, the diagnosis level of the doctor is improved, and the error diagnosis possibility and the missed diagnosis possibility are reduced.

The invention relates to a mammary gland electronic medical record entity recognition system based on multi-standard active learning, and the system is characterized in that the system comprises a preprocessing module; an entity identification module; and an active learning module. According to the invention, the active learning selection strategy for text sequence annotation is designed by considering three aspects of annotation data volume, sentence annotation cost and data sampling balance, so the total annotation workload is reduced. On the one hand, the system can be used for constructingsystems such as breast disease risk patient identification marks, disease medicine recommendation and auxiliary decision diagnosis, doctors are helped to improve the execution efficiency of breast disease standardized diagnosis and treatment, and scientific bases and suggested schemes are provided; on the other hand, doctors can be assisted in finding out potential abnormal conditions in the diagnosis and treatment process, the misdiagnosis and missed diagnosis rate is reduced, the curing probability of breast disease patients is increased, and important value is achieved for intelligent development of breast disease research.

The invention provides an 18-lead holographic dynamic and static electrocardiographic analysis method and system, comprising: placing 8 electrodes at preset positions on the body surface of a subject to collect electrocardiogram signals of the subject; based on Wilson lead system recordings I and II and X-axis and Z-axis data of Frank lead system, using I and II data and Y-axial data as basis; acquiring I, II, III, avR, avL and avF based on the Wilson lead system; acquiring V1-V6 lead data representative of the chest of the subject by using a cross section composed of X-Z axes; deriving V7-V9 lead data representative of the left ventricle of the subject by using Y-Z axes to form a side, and deriving to the front right, V3R-V5R lead data of the right ventricle of the subject by using V1 lead data as a basal point; generating 12 and 18 lead electrocardiograms of the subject. The invention implements the detection of 18-lead electrocardiograms based on the modified Wilson lead system and Frank orthogonal lead system.

The invention discloses a boosting algorithm-based construction method and construction system of a Kawasaki disease risk assessment model. The construction method includes the following steps that: valid samples that can be used for modeling assessment are extracted from a sample data set; 10 features that meet on-site medical auxiliary diagnosis application are selected from the feature set of the valid sample; the incomplete data set of the valid samples is randomly divided into a training set and a verification set; a boosting method is adopted to fit the training set, so as to perform model construction, and a ten-fold cross-validation method is adopted to record optimal model parameters; and the verification set is adopted to calculate a model classification threshold t according toan ROC curve, and therefore, the Kawasaki disease risk assessment model is constructed. The invention also provides a corresponding Kawasaki disease risk assessment system for assessing data to be assessed so as to obtain a KDx score. With the system and method of the invention adopted, the rate of misdiagnosis and the rate of missed diagnosis of the Kawasaki disease can be decreased, and patientscan obtain effective prevention, intervention and treatment in the early stage of the disease.

The invention relates to a colloidal gold chromatographic test strip for cytokeratin 19 (CK19) and a preparation method thereof. The colloidal gold chromatographic test strip comprises a base plate, a sample pad, a combination pad, a nitrocellulose membrane, and a water absorbing pad. The sample pad, combination pad, nitrocellulose membrane, and water absorbing pad are sequentially pasted on one end of the base plate. The test strip is characterized in that the combination pad is made of a glass fiber membrane, a CK19 monoclone antibody-colloidal gold conjugate is painted on the glass fiber membrane; a detection T line and a quality control C line are embedded in the nitrocellulose membrane; the T line is the polyclonal antibody of CK19, and the C line is the corresponding second antibody of the CK19 monoclonal antibody. The prepared colloidal gold chromatographic test strip has the advantages of user-friendliness, fastness, high sensitivity, and good specificity, can be used during the operation, can provide guides for the post operation scheme, and has an important clinical meaning.

The invention discloses a fecal occult blood latex enhanced immunoturbidimetric kit comprising a R1 reagent, a R2 reagent and a human hemoglobin calibrator; the R1 reagent comprises a glycine buffer,PEG6000 and ProClin-300; the R2 reagent comprises a glycine buffer, rabbit anti-human hemoglobin-coated latex microspheres, bovine serum albumin, Tween-20, trehalose, ProClin-300; the latex microspheres in the reagent R2 have a particle size of 60 to 220 nm, and a hemoglobin antibody is rabbit anti-human hemoglobin polyclonal antibody, and is linked to the polystyrene latex microspheres by covalent coupling. Latex enhanced immunoturbidimetric detection results objectively reflect the amount of bleeding, the fecal occult blood latex enhanced immunoturbidimetric kit facilitates observing of changes in conditions of diseases; due to a full automatic operation mode, the detection process is more convenient and quick, and the fecal occult blood latex enhanced immunoturbidimetric kit can test inbatches, and is more suitable for screening experiments of large-scale populations.

The invention discloses an online monitoring method for a low-cycle fatigue crack of an aero-engine turbine disc. An eddy current displacement sensor and a key phase sensor are arranged on a wheel disc shaft of a low-cycle fatigue tester of the aero-engine turbine disc so as to acquire a wheel disc shaft radial displacement vibration signal and a key phase pulse signal on line; a vibration signalof the wheel disc at a preset rotating speed in each load cycle is obtained; a real part and an imaginary part of the crack index are extracted from the vibration signal by adopting full-period discrete Fourier transform; and crack indexes of a plurality of cycles at the beginning of the test are selected as baselines, relative amplitudes and relative phases of the crack indexes are formed by obtaining vector differences to the baselines in the subsequent cycles, an alarm threshold is set according to a 3sigma criterion, and the fatigue crack of the turbine disc is monitored. According to theinvention, damage to the tester caused by burst of the wheel disc is avoided, shutdown inspection in the low-cycle fatigue test process is not needed, the test period can be shortened, and the test cost is reduced.

The invention relates to a diagnostic kit for early liver cancer. The diagnostic kit comprises the following components of G1: 5% of SDS (sodium dodecyl sulfate); G2: 2.2 microgram / muL glycanase; G3A: 100mM trisulphonate fluorescent marker; G3B: 1M organic matter reducing agent; G4: sialidase; GW: deionized water. The invention also discloses a use method of the diagnostic kit. The diagnostic kit has the advantages that the problem of high missed diagnosing rate of alpha fetoprotein on the liver cancer is solved; the liver cancer can be quickly, simply, conveniently and accurately detected, and the therapy effect of the liver cancer can be tracked.

The invention discloses a construction method and a construction system for a Kawasaki disease risk assessment model based on a neural network algorithm. The construction method comprises the steps: extracting valid samples which can be used for modeling and assessment from a sample data set; selecting ten features meeting field medical auxiliary diagnostic application from the feature set of thevalid samples; randomly dividing the incomplete data set of the valid samples into a training set and a validation set; fitting the training set by using the neural network method to construct the model and recording the optimal model parameters by using the ten-fold cross-validation method; and calculating the model classification threshold t by using the validation set according to the ROC curveso as to construct the Kawasaki disease risk assessment model. The corresponding Kawasaki disease risk assessment system is also constructed to be applied to assessment of the data to be assessed soas to obtain the KDx score. The misdiagnosis rate and the missed diagnosis rate of the Kawasaki disease can be reduced so that the patients can obtain effective prevention, intervention and treatmentin the early stage of the disease.

The invention relates to the technical field of image processing, and concretely relates to a gastric cancer focus detection method and device based on a convolutional neural network. The gastric cancer lesion detection method based on the convolutional neural network comprises the following steps: S1, preprocessing a general image of a gastric cancer sample to be detected; S2, performing focus target extraction and confidence analysis based on a target detection algorithm model, and outputting a focus detection result; or S3, finely segmenting and outlining the focus target based on the semantic segmentation algorithm model, and outputting a focus detection result. According to the method, the general image of the gastric cancer sample is utilized for the first time, the cancer lesion and intragastric or perigastric metastatic cancer lesion in the gastric resection specimen can be automatically positioned, meanwhile, the confidence coefficient of an analysis result is given, an examination doctor is assisted in accurately cutting a lesion part of the specimen, the cancer lesion detection efficiency is improved, and the missed diagnosis rate is reduced.

The invention discloses a carotid artery stenosis degree calculation method and device and a storage medium, and the method comprises the steps: S1, receiving a neck MRA image, and carrying out the interlayer registration of a first sequence and a second sequence in the neck MRA image, so as to form an interlayer corresponding relation; S2, transverse blood vessels are extracted from all layers ofimages of the registered first sequence, and the blood vessel area of each transverse blood vessel is calculated; S3, extracting a transverse blood target from each layer of image of the registered second sequence, and calculating the blood area of each transverse blood target; S4, according to the interlayer corresponding relation, the blood vessel area of each transverse position blood vessel and the blood area of each corresponding transverse position blood target are substituted into a calculation formula, and a plurality of carotid artery stenosis degrees are obtained through calculation; and S5, outputting the maximum value in the plurality of carotid artery stenosis degrees as the carotid artery stenosis degree of the neck MRA image. According to the embodiment of the invention, the carotid artery stenosis degree can be automatically calculated, so that the diagnosis efficiency and accuracy of doctors are improved.

The invention discloses a device and a method for diagnosing a blood vessel blockage part. The device comprises a controller, a display module, a laser device, a coupling device for collecting laser light emitted from the laser device, a transmission optical fiber connected with the coupling device, and a detector for collecting reflected laser light from the coupling device, wherein the output end of the controller is connected with the display module and the laser device respectively; and the input end of the controller is connected with the detector. The device has the following advantages: the positioning operation is accurate, a blood vessel blockage point can be quickly obtained, a plurality of blood vessel blockage points can be detected at the same time, a clear blood vessel blockage curve can be obtained and is simple and clear, the detection range is wide, the missed diagnosis rate is low, a contrast agent is not required, the non-blockage situation at the far end of the blockage part can be also confirmed at the same time, a reliable diagnosis basis is provided for surgical selection, and the detection of the blockage part cannot be influenced due to change of blood flow speed and blood flow waveform caused by limb extrusion. The device is simple in structure, convenient in operation, minimally-invasive and high in safety.

The invention discloses an SNP (Single Nucleotide Polymorphism) marker for detecting the diseasing risk of gestational diabetes mellitus. The SNP marker comprises 12 SNP sites of a personal genome, wherein the 12 SNP sites are rs7903146, rs12255372, rs 5219, rs7754840, rs4402960, rs1387153, rs10830963, rs2237892, rs1801278, rs1799884, rs1801282 and rs1063192. The SNP marker disclosed by the invention has the beneficial effects that the SNP sites selected in the invention summarize the scientific article results published at home and abroad since 1994-2016, find risk gene sites with consistentresults as detection target points, simultaneously consider the mutual action between genes and solve the problems of imperfect early screening methods and high missed-diagnosis rate for GDM on the current clinics; and after introduction of gene detection, the missed-diagnosis rate of early screening can be greatly reduced, the benefit for early intervention can be achieved and the diseasing riskof the gestational diabetes mellitus can be effectively reduced.