175 results about "Gold standard" patented technology

The invention provides a liver tumor segmentation method and device based on a CT (Computed Tomography) image. The method comprises the following steps: performing Gaussian denoising on CT image data of a liver, converting the denoised CT image data into standardized data of which a gray average is 0 and a variance is 1, and performing down-sampling operation; extracting a lesion slice and a normal tissue slice from a gold standard image of the CT image of the liver, and classifying the lesion slice and the normal tissue slice into a positive sample and a negative sample; constructing a multi-level depth convolutional neural network, training a model through a stochastic gradient descent to obtain a network model, and acquiring a coarse segmentation binary image of a tumor and a pixel-classification probability image through a classifier; performing morphological erosion operation on the coarse segmentation binary image of the tumor to obtain a foreground image needed by graph cut, performing subtraction operation on the binary image of a liver and the coarse segmentation binary image of the tumor, and performing the morphological erosion operation to obtain a background image corresponding to normal tissues of the liver; and constructing an undirected graph, and obtaining a finial segmentation region of the tumor through a graph cut optimization algorithm.

The invention belongs to the technical field of image processing and computer vision, and relates to a novel biomedical image automatic segmentation method based on a U-net network structure, including dividing a biomedical data set into a training set and a test set, and normalizing the test set and augmented test set; inputting the images of the training set into the improved U-net network model, and generating a classification probability map by output image passing through a softmax layer; calculating the error between classification probability diagram and gold standard by a centralized loss function, and obtaining the weight parameters of network model by a gradient backpropagation method; entering the images in the test set into the improved U-net network model, and outputting the image to generate a classification probability map through the softmax layer; according to the class probability in the classification probability graph, obtaining the segmentation result graph of theimage. The invention solves the problems that simple samples in the image segmentation process contribute too much to the loss function to learn difficult samples well.

The invention relates to a method for simultaneously detecting microsatellite locus stability and genome change on the basis of second generation sequencing, especially an application of the detection method in assisting in diagnosing a colorectal cancer patient and a corresponding kit. According to the method provided by the invention, the sequence analysis for a plurality of samples can be completed at one time; the detection for genome change, such as, gene mutation and chromosome variation, is completed while the detection for the microsatellite state is completed; no additional experimental operation is required; the analysis time and the cost for detecting the patient are greatly saved; compared with PCR electrophoretic MSI detection gold standard, the detection method provided by the invention has the advantages of matching accuracy, high detection speed, lower price and no dependence on a matched normal sample, so that the detection mode is more convenient.

A method for segmentation of paint cracks on an ICGA image based on a conditional generative adversarial network includes: (1), collecting an original ICGA image, extracting a complete fundus oculography image, labeling it with gold standard, normalizing fundus oculography image and gold standard, splicing it into a group of images as sample data, distributing the sample into a training set and atest set according to proportion; (2) based on the principle of conditional generative countermeasure network, constructing the network of generators and discriminators; (3) inputting that data of thetraining set into the network for adversarial train, defining a loss function, and generating a paint crack image correspond to the original picture by the training generator; (4) in the testing phase, inputting the data of the test set, and getting the corresponding paint crack segmentation result diagram through the trained generator G. The segmentation method provided by the invention can be used for solving the problems that the sample size of the ICGA image is small and the acquisition of the contrast image is difficult, and has the characteristics of high accuracy of the segmentation result.

The invention relates to the retinal blood vessel segmentation technology and especially relates to a multi-characteristic fusion monitored retinal blood vessel extraction method. The method comprises steps of step 1, retinal blood vessel image pre-processing; step 2, retinal blood vessel image characteristic extraction; step 3, random forest classifier training; and step 4, retina retinal blood vessel image post-processing. The method is advantaged in that through experiment verification of a DRIVE and STARE eyeground image database, susceptibilities are respectively 0.8354 and 0.8452, accuracies are respectively 94.82% and 95.34%, compared with existing retinal blood vessel segmentation methods in the prior art, the multi-characteristic fusion monitored retinal blood vessel extraction method is integrally better, moreover, disadvantages of the other methods at adjacent blood vessel portions, blood vessel cross sections and capillaries are solved, and segmented blood vessel structures are relatively closer to gold standards and actual blood vessel dimension values.

A traditional Chinese medicine pulse-taking sensor device comprises a pulse-taking array sensor, and the sensor array position is designed according to the gold standard of diagnosis of the portion above the wrist end, the wrist end, the processus styloideus radii, the elbow end and the portion below the elbow end.The traditional Chinese medicine pulse-taking sensor device comprises a difference closed-loop sampling system, a pulse-taking instrument, a traditional Chinese medicine pulse-taking diagnosis and treatment system and a traditional Chinese medicine pulse-taking system health service platform.The health service platform comprises a cloud storage and cloud processing platform, is used for conducting cloud storage on detection results, and matches later-period diseases and selecting treatment schemes by using cloud computing.Stability and repeatability of taking patient pulses are ensured through mechanical design, pulse data of a patient is collected more objectively and accurately through the difference closed-loop sampling system, traditional Chinese medicine resources are optimally configured through an Internet of Things implementing scheme, and the cognition degree of people to traditional Chinese medicine is improved.The 25-point array is set according to the portion above the wrist end, the wrist end, the processus styloideus radii, the elbow end and the portion below the elbow end, and array arranging space with a good diagnosis and treatment effect is obtained.

The invention relates to a biochip information reading device and an information analyzing method, discloses a biochip information reading instrument and belongs to the technical field of clinical diagnosis instruments. The traditional 'gold standard' for diagnosing the flora-imbalanced bacterial vaginitis is difficult in actual operation, and is also rough and complicated to operate although certain new technologies are adopted. According to the biochip information reading instrument, a liquid crystal display is arranged on a casing; a mainboard is arranged in the casing below the display; a photoelectric signal processing and data storing chip, a circuit system, a light source and a photoelectric sensor are fixedly arranged on the mainboard; a biochip detecting channel passing from the instrument in the front-back direction is arranged below the mainboard; and a cutter-shaped telescopic clamping tongue is respectively arranged at two sides of a position below the photoelectric sensor in the biochip detecting channel. A mechanical transmission component and a motor are not arranged in the biochip information reading instrument, so that the biochip information reading instrument is small in volume. The biochip information reading instrument can be used for rapidly obtaining multi-index comprehensive judgment results, thereby being comprehensive, accurate and convenient. Thus, the biochip information reading instrument is suitable for the bedside operation by outpatient service doctors in hospitals or the household use by patients.

The invention discloses a segmentation model training method, an OCT image segmentation method and device, equipment and a medium, and the method comprises the steps: obtaining a training sample imageset, and inputting an original OCT image into a preset generator model for segmentation processing; Comparing the first segmented image with a gold standard image through a preset discriminator model, calculating a loss function of the generator model according to a comparison result, and updating the generator model; Converting the first segmented image into a second segmented image, inputting the second segmented image and the gold standard image into a discriminator model, and updating the discriminator model according to the binary cross entropy; And performing iterative training on the updated generator model and the updated discriminator model, and determining the updated generator model after iteration training is stopped as an image focus segmentation model. According to the segmentation model training method, the performance of the segmentation model is improved in a countermeasure training mode, and the accuracy of the segmentation model is improved.

Decades of investigations were focused on finding “gold standard” for evaluation of plasma dilution and osmolality, blood loss evaluation and prediction of bleeding or transfusion induced changes in hematocrit and hemoglobin concentration. Addressing deficiencies of existing methods, the current invention created new combined mathematical-physiological model applicable to manually operated nomograms and software in medical monitors. The mathematical model HBS Trends is used in blood transfusion and infusion therapy nomogram—HBS Nomogram—which is based on blood hemoglobin concentration and hematocrit. It is also an easy and practical tool for recording and dynamical interpretation of plasma osmolality, blood hemoglobin concentration, hematocrit and mean corpuscular hemoglobin concentration. The HBS Nomogram is a practical system for organizing blood test results in a patient's medical records. It can be used alone or, in line with existing guidelines for infusion and transfusion therapy making them more practical, cost effective and time saving in decision making.

Biopsy of the prostate using 2D transrectal ultrasound (TRUS) guidance is the current gold standard for diagnosis of prostate cancer; however, the current procedure is limited by using 2D biopsy tools to target 3D biopsy locations. We have discovered a technique for patient-specific 3D prostate model reconstruction from a sparse collection of non-parallel 2D TRUS biopsy images. Our method can be easily integrated with current TRUS biopsy equipment and could be incorporated into current clinical biopsy procedures for needle guidance without the need for expensive hardware additions. We have demonstrated the model reconstruction technique using simulated biopsy images from 3D TRUS prostate images of 10 biopsy patients. This technique of model reconstruction is not limited to the prostate, but can be applied to the reconstruction of any tissue acquired with non-parallel 2-dimensional ultrasound images.

The invention discloses a biological sample signal amplification method adopting the combination of terahertz metamaterials and nanogold particles. The method comprises the following steps: preparing a plurality of biological sample solutions with different concentrations and gold standard avidin solutions with different concentrations; dropwise adding the biological sample solutions onto the surfaces of the terahertz metamaterials, and airing at the room temperature; dropwise adding the gold standard avidin solutions on the surfaces of the terahertz metamaterials, and airing at the room temperature; collecting terahertz time-domain signals of all sample points to be measured on the surfaces of the terahertz metamaterials and reference sample points; calculating the transmissivity or the reflectivity of all the sample points to be measured and the reference sample points according to the terahertz time-domain signals; and calculating to obtain the frequency shift of transmission peaks or reflection peaks according to the frequency value corresponding to the lowest point of the transmissivity or the reflectivity. According to the method, the terahertz metamaterials and the nanogold particles are used for modifying; the sample signals are amplified by using an electric field local enhancement effect of the metamaterials; the electric field distribution effect is changed by nanogold, and the sample signals are further amplified by nanogold modifying; and the detection is high in sensitivity, the operation is convenient and fast, and fast detection requirements increased day by day are met.

A physics-based mathematical model is used to estimate central pressure waveforms from measurements of a brachial pressure waveform measured using a supra-systolic cuff. The method has been tested in numerous subjects undergoing cardiac catheterisation. Central pressure agreement was within 11 mm Hg and as good as the published non-invasive blood pressure agreement between the oscillometric device in use and the so-called ''gold standard.'' It also exceeds international standards for the performance of non-invasive blood pressure measurement devices. The method has a number of advantages including simplicity of application, fast calculation and accuracy of prediction. Additionally, model parameters have physical meaning and can therefore be tuned to individual subjects. Accurate estimation of central waveforms also allow continuous measurement (with intermittent calibration) using other non-invasive sensing systems including photoplethysmography.

The invention discloses a multiplex fluorescence PCR (Polymerase Chain Reaction) detection kit for typhoid / paratyphoid saimonella. The detection method comprises the following step of: specifically amplifying tailed primers of a ViaB gene sequence, a fliC-a gene sequence, a fliC-b gene sequence and a ssaR gene sequence of typhoid saimonella, C type paratyphoid saimonella, Atype paratyphoid saimonella and B type paratyphoid saimonella respectively, wherein the base sequences of the tailed primers are shown as SEQ ID NO:1, 2, 4, 5, 7, 8, 10 and 11; and the molecular beacon probes for detecting the ViaB gene sequence, the fliC-a gene sequence, the fliC-b gene sequence and the ssaR gene sequence of the typhoidsaimonella, the C type paratyphoid saimonella, the A type paratyphoid saimonella and the B type paratyphoid saimonella are shown as SEQ ID NO: 3, 6, 9 and 12. The detection method and the detection kit provided by the invention reach the sensitivity level of a gold standard culture method in the aspect of detection sensitivity index, and have the advantages of quickness, sensitivity, easiness and convenience in operating, objective and correct result and the like compared with the conventional culture method.

The invention overcomes the defects that the routine C-reactive protein (CRP) quantitative detection method is not suitable to single detection and takes long time, and provides a quick, convenient and accurate method for detecting the colloidal gold of C-reactive protein quickly and quantitatively. The reaction principles of the method is the gold-standard dot immune filtration assay of the solid phase double antibody sandwich method; a sample to-be-detected is diluted 40-480 times and then mixed evenly with immune colloidal gold in a liquid phase homogeneous medium and finally put on a reaction board; wherein, the contained sample to-be-detected which is mixed with the immune colloidal gold can be specifically captured by the monoclonal antibodies or the polyclonal antibodies of another determinant of anti sample to-be-detected, which is fixed on a piece of membrane; and the to-be-detected sample is shown like red spots, and the brightness of the color of spots is in direct proportion to the CRP concentration in the sample.

The invention relates to a method for using a plasma atomic emission spectrometer to measure impurities in high-purity gold, which comprises the following steps: weighing one unit of sample in a bunsen beaker, adding aqua regia to dissolve, replenishing hydrochloric acid deionized water to do constant volume to a scale after dissolving, respectively getting sample solution in more than two colorimetric tubes, solution in each tube is 15ml, adding gold standard solution which is mixed by elements according to a multiple relation in turn, doing constant volume by deionized water, starting the plasma atomic emission spectrometer, starting a circulating water pump, opening analytic control software, entering a 'standard addition method' control procedure, choosing a spectral line for measuring in a spectral line base, setting the concentration value of curved lines, scanning a whole band, sucking sample solution in each colorimetric tube in an instrument in turn according to the sequence that the concentration is from low to high, clicking a feeler switch to get an initial curve point of the point after sucking in each time, assuring curved lines, and leading the linear coefficient to be more than 0.99, outputting the concentration value result of each determined element, and calculating the result of the percentage content of each determined element according to the calculating formula.

The invention discloses an image classification method, a device, a storage medium and equipment, and belongs to the technical field of machine learning. The method comprises the following steps of acquiring a to-be-classified three-dimensional imaging graph, performing scaling treatment on the to-be-classified three-dimensional imaging graph, and inputting obtained three-dimensional imaging graphs of at least two resolution ratios into a detection model, wherein the detection model is obtained through the machine learning process based on the multi-scale features of a manual labeling sample;acquiring a disease source region in the to-be-classified three-dimensional imaging graph output by the detection model; inputting the determined disease source region into a classification model, wherein the classification model is obtained through the machine learning process on the basis of a gold standard sample, and the gold standard sample is an image sample which is used for correctly distinguishing the attributes of disease sources; acquiring the graph category of the to-be-classified three-dimensional imaging graph outputted by the classification model, wherein the graph category comprises the disease attributes of diseases. According to the invention, the model is high in accuracy after being trained based on the multi-scale features and the gold standard sample. Meanwhile, the accuracy of graph classification is greatly improved.

The invention discloses an electrocardiogram analyzing system based on a gold standard database. The system at least comprises a database subsystem (1) and an application service subsystem (2), wherein the database subsystem (1) at least comprises a gold standard electrocardiogram case database (4) and an index database (5); the application service subsystem (2) is used for receiving electrocardiogram data to be diagnosed, which is derived from an electrocardiogram data source (12) and calculating the characteristics of an electrocardiogram to be diagnosed by waveform analysis, characteristic calculation and index matching; the application service subsystem (2) is used for retrieving the index database (5) and the electrocardiogram case database (4) by utilizing the characteristics of the electrocardiogram to be diagnosed to obtain a gold standard electrocardiogram case set which is matched with the characteristics of the electrocardiogram to be diagnosed; the system also comprises an index configuring subsystem (3); the index configuring subsystem (3) comprises an index editor (22) and an index evaluating tool (23); the system can also comprises a knowledge management subsystem (25); the knowledge management subsystem (25) is used for constructing an diagnostic model for representing a causal relationship of a characteristic code and an diagnostic code by scanning the gold standard case database (4); and the application service subsystem (2) is used for directly converting the characteristics of the electrocardiogram to be diagnosed into a diagnostic result by utilizing the diagnostic model.

The invention discloses a fluorescence PCR (Polymerase Chain Reaction) detection kit for group B streptococcus. The fluorescence PCR detection kit comprises the following components: a PCR reaction liquid, an enzyme mixed liquid, a group B streptococcus reaction liquid, an internal reference plasmid, a positive reference and a negative reference. The fluorescence PCR detection kit is used for overcoming the defects that more time is wasted by using a gold standard bacterial culture method, a serological diagnosis method has hysteresis and a gene sequence determination method is more complex. The fluorescence PCR detection kit has the advantages of high sensitivity, good specificity, strong repeatability, rapid and objective detection result and the like so as to have a wide application prospect in the field of in-vitro diagnosis of the group B streptococcus.

The invention provides a lung tissue image segmentation method based on deep learning, and belongs to the technical field of medical image segmentation. The lung tissue image segmentation method comprises the steps that an X-ray chest radiograph image is input into a segmentation model, the segmentation model is obtained through training of multiple sets of training data, and each set of trainingdata in the multiple sets of training data comprises the X-ray chest radiograph image and a corresponding gold standard used for identifying lung tissue; and output information of the model is obtained, and the output information comprises a segmentation result of the lung tissue in the X-ray chest radiography image. According to the lung tissue image segmentation method, the segmentation of the lung tissue of the X-ray chest radiography is realized through an improved Deeplabv3+ deep learning method, and the problems of local convergence and false positive segmentation when the lung tissue issegmented by using a traditional method are solved; the lung tissue image segmentation method respectively obtains 95.3% of MIoU and 94.8% of MIoU on the public data set and the pneumoconiosis data set; and the false positive problem of the FCN network is solved, and the segmentation accuracy of ribs at the thoracic diaphragm angle and on the X-ray chest radiography in the SCAN network method isimproved.

The invention discloses an ATRP method for constructing a series of low-toxicity and high-efficiency cationic gene vectors with PGMA (polyglyceryl methacrylate) as a skeleton in the technical field of the non-viral gene vectors. The ATRP method is smooth in polymerization reaction and easy to control; according to the ATRP method, a variety of high-performance cationic gene vectors with different molecular weights and narrow molecular weight distribution can be prepared as required; and the prepared cationic gene vectors are good in storage stability, have a transfection efficiency higher than the gold standard PEI in Hepg2, C6, Cos7, HEK293 and other cells, are easy to use and have a commercial potential.

Biopsy of the prostate using 2D transrectal ultrasound (TRUS) guidance is the current gold standard for diagnosis of prostate cancer; however, the current procedure is limited by using 2D biopsy tools to target 3D biopsy locations. We have discovered a technique for patient-specific 3D prostate model reconstruction from a sparse collection of non-parallel 2D TRUS biopsy images. Our method can be easily integrated with current TRUS biopsy equipment and could be incorporated into current clinical biopsy procedures for needle guidance without the need for expensive hardware additions. We have demonstrated the model reconstruction technique using simulated biopsy images from 3D TRUS prostate images of 10 biopsy patients. This technique of model reconstruction is not limited to the prostate, but can be applied to the reconstruction of any tissue acquired with non-parallel 2-dimensional ultrasound images.

The embodiment of the invention discloses a medical image quality evaluation method, a device, equipment and a storage medium. The method comprises the following steps: acquiring a reference image anda target image which are scanned and reconstructed by a subject and a trained complete image quality evaluation machine learning model, the reference image and the target image have the same fault information and position information, and the reference image does not have image quality defects to be evaluated corresponding to the image quality evaluation machine learning model; and inputting thereference image and the target image into an image quality evaluation machine learning model to obtain an evaluation result of the image quality of the target image. According to the medical image quality evaluation method, by taking the reference image without the to-be-evaluated image quality defect as a gold standard, whether the target image has the image quality defect is evaluated, so that adoctor can make a scanning decision according to a quality evaluation result, the workload of the doctor can be effectively reduced, and subsequent diagnosis is prevented from being influenced by a medical image with poor quality.

A new lipoprotein analysis procedure based on the CDC gold standard, analytical ultra centrifugation, having dramatically reduced the time and cost to obtain a result with a self generating continuous gradient. The method allows quantification of the risk factors of cardiovascular disease based on particle numbers of the particles of various groups and subgroups of lipoproteins.

The invention relates to a cultivating method of Hosta plantaginea Gold Standard. The method comprises the following steps of: first carrying out sterile treatment on Hosta plantaginea Gold Standard buds excavated in the early spring; and then inoculating the buds on a bud inducing culture medium to differentiate the buds; culturing the differentiated buds for 1 month; and then cutting off a budding callus and putting the budding callus into a proliferation medium to be proliferated so as to obtain cluster buds; after dividing the cluster buds into shoots or single buds, putting the shoots orthe single buds into a seedling culture medium to culture the seedling with adventitious buds; obtaining 2-3 cm small plants after 20 days; transferring the small plants into a rooting culture mediumto be induced for rooting; selecting strongly growing sterile seedlings with developed roots; carrying out seedling adaptation indoors in an open bottle for three days; washing off agars at the rootsof the seedlings after taking out the seedlings; transplanting the seedlings outdoors after naturalizing the seedlings in a greenhouse for 40 days; and carrying out fertilizer and water management onthe seedlings. Compared with the prior art, the cultivating method of the Hosta plantaginea Gold Standard provided by the invention has the following advantages that: the reproductive speed and the evenness of the nursery-grown plants can be largely increased; the original maternal character is preferably kept; and the survival rate can be increased so that the yield is increased and the transplanting survival rate can reach 95%.

The invention belongs to the technical field of veterinary drug residue analysis and particularly relates to a colloidal gold immunochromatographic assay test strip for detecting dexamethasone residues in milk. The test strip is formed by sequentially stacking and adhering an absorbing cushion, a nitrocellulose membrane, a gold standard cushion and a sample cushion to a plastic liner plate. A detection line and a quality control line are arranged on the nitrocellulose membrane. The gold standard cushion is coated with dexamethasone monoclonal antibodies which are marked with colloidal gold and can be recognized. The detection line is coated with dexamethasone coating antigens. The quality control line is coated with goat-ant-mouse IgG. The invention further discloses a preparation method and application of the colloidal goldy test strip for detecting the dexamethasone residues in milk. The test strip can be used for analyzing the dexamethasone residues in milk, and the detection limit is 0.2 microgram per kilogram. The test strip has the advantages of being high in specificity, good in stability and the like.

The invention discloses a histamine immunoassay method based on the characteristics of platinum-gold bimetallic nano-particle peroxidase. The method comprises the following steps: S1, labeling a histamine monoclonal antibody with a platinum-gold nano-particle to prepare a platinum-gold standard antibody nano-probe; S2, adding a sample to be tested and the nano-probe obtained in step S1 to an ELISAplate coated with a histamine antigen, and forming an antigen and platinum-gold standard antibody immune compound in a competitive immunoassay mode; S3, adding a coloring substrate solution containing 3,3',5,5'-tetramethylbenzidine and hydrogen peroxide to the ELISA plate, performing color development, and terminating the color development with sulfuric acid; and S4, detecting the absorbance at 450 nm by a the absorption of 450 nm was measured by an enzyme-linked immunometric meter to achieve the quantitative detection of histamine in the sample. The method has the advantages of easiness in operation, high sensitivity, good stability and high sample throughput, is suitable for rapid high-throughput screening of the histamine in foods, and has a great application prospect.

The invention relates to a test strip and a kit for premature rupture of membrane. The test strip comprises, from top to bottom of the horizontal plane of the test strip, a water absorbent pad, a cellulose nitrate membrane, a gold standard pad and a sample pad, wherein a detection line and a quality control line are coated on the cellulose nitrate membrane; the detection line is a monoclonal antibody 11B6 and the quality control line is rabbit anti-mouse IgG antibody; a monoclonal antibody 14A3 marked by colloidal gold is coated on the gold standard pad; and the absorbent pad, the cellulose nitrate membrane coated with the detection line and the quality control line, the gold standard pad coated with the monoclonal antibody 14A3 marked by the colloidal gold and the sample pad are sequentially adhered on a water-nonabsorbent support thin sheet from top to bottom; and a sample injecting hole is provided on the sample pad. The test strip in the kit has the advantages of high specificity and high sensitivity, and can be used for clinic diagnosis of premature rupture of membrane. The kit has simple operation and can determine the result in ten minutes.

The invention provides an analyzing and processing method for analyzing and processing the magnetic resonance image of the acute ischemic stroke the method specifically comprises the steps of according to the data of a magnetic resonance image, acquiring the data of the magnetic resonance image, conducting the gold standard treatment on the data of the magnetic resonance image, setting the data of the image after the gold standard treatment as a positive sample set, obtaining a negative sample based on the data of the image after the gold standard treatment, obtaining a negative sample based on the data of the image in an initial ischemic region, obtaining a negative sample set, extracting the feature vectors of the positive sample set and the negative sample set, combining the extracted feature vectors as a training dictionary, obtaining to-be-segmented voxels based on the data of the image in an initial ischemic candidate region, and judging whether to-be-segmented voxels are ischemic voxels or not according to the training dictionary. According to the technical scheme of the invention, based on the analyzing and processing method for analyzing and processing the magnetic resonance image of the acute ischemic stroke, an ischemic region can be segmented more accurately. Meanwhile, the segmentation accuracy is high and the influence of noises or artifacts is small.

The invention discloses an interventional medical device and medical material testing system, and a corresponding experimental method. The system includes an experimental operation module and a detection device module, wherein the experimental operation module includes a test loop and a controller, the test loop is provided with a power device, a pulsating blood flow simulation device, an interventional medical device fixing device and a to-be-tested sample fixing device which are sequentially connected, the controller is used for controlling the power device, the pulsating blood flow simulation device and the to-be-tested sample fixing device according to control parameters, the detection device module includes a data collecting device, a parameter input device and a detection device, theparameter input device is used for transmitting the control parameters to the controller, the data collecting device is used for collecting test data of the test loop, and the detection device is used for detecting biochemical indicator data in the test loop. The system is advantaged in that the system provides a standardized test environment, adopts industry-recognized gold-standard testing equipment and provides uniform testing standards for testing of interventional medical devices and medical materials.