46 results about "False positivity" patented technology

The invention provides a pathogen detection method based on targeted high-throughput sequencing. The pathogen detection method comprises the following steps: S1, acquiring NGS original sequencing data of a pathogen sample to be detected and a negative control sample; s2, removing low-quality data from the obtained original sequencing data, then performing host sequence filtering, and finally performing plasmid sequence filtering to obtain high-quality sequencing Read; s3, comparing the pathogen reference genomes to obtain specific Read data; s4, performing classification according to a comparison result, and calculating the specific Read number and the RPM value of each pathogen; s5, constructing a microbial reference genome database, and verifying the species specificity of the specific Read in the NT database; and S6, grading pathogen detection signals in the to-be-detected sample according to the RPM value and the NTRPM value to obtain a result. The method provided by the invention can effectively reduce false positive of a detection result and interference of background signals, the detection result is accurate, and the method can be well applied to the fields of disease diagnosis, epidemiological investigation, public health prevention and control and the like.

The invention provides a Chinese large language model safety evaluation system and method based on dynamic extension and a bottom-taking mechanism, and belongs to the field of natural language processing and artificial intelligence. On the basis of an existing benchmark test data set, an attack test data set is generated by combining a large language model with multiple attack methods, and attack test data is generated for a request rejected to respond by the model by utilizing a fine-tuned expert model through a bottom-out mechanism; constructing a corresponding false positive test data set, inputting the attack test data set and the false positive test data set into the Chinese large language model to be evaluated, and reasoning to obtain an output result; and finally, carrying out multi-dimensional evaluation on an output result and automatically generating a report. The method has good expandability and adaptability, a test data set can be dynamically expanded, emerging security threats can be followed up, and anaphylaxis and generation of harmful content can be effectively avoided; and a more flexible, comprehensive and efficient solution is provided for security assessment of the Chinese large language model.

The invention relates to the technical field of photon detection, in particular to an environment interference identification method and system for sample analysis and a sample analyzer. According to the scheme, firstly, the photometric curve data of the target object to be measured are obtained, then, the photometric curve data are input into the preset feature model for feature parameter extraction, at least one feature parameter is extracted, then, the extracted feature parameters are analyzed to obtain the analysis result, and finally, the analysis result is obtained. And determining whether the photometric curve data of the target to-be-measured object is interfered by the environment according to the analysis result. By adopting the above scheme, the photometric curve data abnormity caused by environmental interference in any link of sample analysis can be detected, and the accuracy of the detection result can be ensured while the abnormal result can be efficiently detected by using the feature model, so that the accuracy of the detection result is improved. And the risks of false negative and false positive in clinical diagnosis are effectively reduced.

The invention provides a helicobacter pylori fluorescence image fine tuning detection method and device and a readable storage medium thereof, and belongs to the technical field of medical image processing and computer-aided diagnosis. The method aims at solving the problems that in the prior art, the false positive is high due to the fact that only a fluorescence signal is relied on, and a model is poor in generalization for different data environments. The method comprises the following steps: outputting a detection frame of a suspected target from a fluorescence image block through a detection module; determining a target analysis area containing a detection frame and a surrounding tissue area, and inputting the analysis area into a classification module to judge true and false positive; and when the classification result is confirmed to be wrong, only performing fine tuning training on a classifier in the classification module by utilizing target analysis area data corresponding to the error and combining a group of preset representative sample features. According to the method, the false positive rate is remarkably reduced by using surrounding organization information, and the generalization ability and scene adaptability of the model are improved through an efficient fine adjustment mechanism.

The invention relates to the technical field of brain disease monitoring, in particular to a clinical research data safety equipment wearing state monitoring method and system based on artificial intelligence. Comprising the following steps: S1, acquiring body information and a brain disease type of a patient, and acquiring a wearable equipment type at the same time; s2, acquiring brain physiological data of the patient in real time through the wearable equipment, monitoring state data at the same time of data acquisition of the wearable equipment, and performing data anomaly threshold setting on the brain physiological data according to the state data in combination with a brain disease type; the abnormal threshold value is dynamically adjusted through the difference between the real-time state data and the standard state of the wearable equipment, the worse the equipment state is, the larger the allowed physiological data fluctuation range is, false alarms caused by non-pathological factors such as poor equipment contact and insufficient electric quantity are avoided, the abnormal threshold value is automatically adjusted, and false positive data are reduced.

The present invention discloses blood small extracellular vesicles hsa-let-7d-3p, hsa-let-7e-5p, hsa-let-7f-5p, hsa-miR-379-5p, hsa-miR-98-5p, hsa-miR-100-5p, hsa-miR-126-3p, hsa-miR-21-5p, hsa-miR-140-5p, hsa-miR-1908-5p, hsa-miR-328-3p, hsa-miR-486-5p and hsa-miR-99b-5p, which can be used as biomarkers for early diagnosis of lung cancer. The blood small extracellular vesicle miRNA of the present invention can distinguish early lung cancer patients from non-cancer subjects, especially people at high risk of lung cancer and patients with positive lung nodules. The blood small extracellular vesicle miRNA of the present invention has better sensitivity and specificity, higher diagnostic specificity and fewer false positives.

The utility model belongs to the technical field of reagent detection, and discloses a test paper clamping shell. The test paper clamping shell comprises a shell body, an observation window, a test tube fixing hole, a sealing piece and a tube breaking blade. According to the test paper clamping shell, in the sample adding link, a test tube cover does not need to be opened, a test tube only needs to be directly placed into the clamping shell through the test tube fixing hole, the sealing piece can seal a gap between the test tube and the test tube fixing hole, the tube breaking blade cuts the test tube in the test tube pressing process, and a reagent in the test tube flows into the reagent adding cavity; meanwhile, a transparent partition plate is arranged on the observation window in a sealed mode, it is guaranteed that the interior of the shell body of the test paper card shell is isolated from the outside in the sample adding operation process, and therefore it can be guaranteed that the whole chromatography process is not in contact with outside air, and the risk of outside aerosol pollution is greatly reduced through the design; the phenomenon that the detection result is false positive due to aerosol pollution is completely eradicated from the source, and the accuracy of the detection result is guaranteed.

A system and method are provided for evaluating a prediction model configured to predict occurrence of a clinical event in multiple patients during corresponding patient stays, where the prediction model outputs risk scores for the patients. The method includes identifying patient stays that include at least one risk score generated by the prediction model that crosses a predetermined risk threshold; providing notifications for risk scores that cross the risk threshold; determining a patient-level confusion matrix based on the notifications, where each patient stay contributes one patient data point to the patient-level confusion matrix; determining an event-level confusion matrix based on the notifications, where the event-level confusion matrix includes prediction lead time and notification limit, where each patient stay contributes one event data point to the event-level confusion matrix; calculating classification metrics for false positives and false negatives; and identifying errors in the predication model based on the classification metrics.

The application belongs to the technical field of molecular diagnosis, and particularly relates to a digital PCR kit and method for accurately detecting the copy number of a CAR gene of a CAR-T cell and a lentivirus vector. The application first realizes the absolute quantification of CAR, Rev and an internal reference gene RNase P in a single tube simultaneously, and has strong universality. Based on the principle of digital PCR, the absolute copy number is directly obtained without a standard curve, and the result is accurate and highly repeatable. The detection lower limit reaches 0.01%, and extremely low abundance CAR-T residues or microresidual diseases can be detected. Primers and probes are designed for the conservative regions of CD3zeta, Rev and RNase P, and no cross reaction is verified, so that false positives are effectively avoided. The sample only needs gDNA, and there is no requirement for cell activity, and the sample is compatible with freezing and transportation. The kit is premixed and optimized, the process is automated, and 4-5 hours are needed. The kit is suitable for the whole chain of CAR-T drug research and development, production process quality control, clinical patient monitoring and safety evaluation.

This invention provides a three-dimensional spatial organ image feature extraction system that reduces false positives. [Solution] A feature extraction method is developed to serve as the basis for classifying organ positives and false positives. Multiple organ cross-sectional layers of the same type of three-dimensional organ are extracted using MRI. An AI analysis system generates organ cross-sectional layers for each organ, and a specific organ marking image is generated. The organ cross-sectional layers from which the specific organ marking image was generated are imported into the image analysis unit. The centroid point of the specific organ marking image and the organ cross-sectional layer containing the specific organ marking image are imported into the feature extraction unit. The feature extraction unit calculates minimum distance data based on the centroid point and reference point, calculates area data based on the specific organ marking image, generates a layer number data set based on total layer number information, current layer number information, and image code information, and imports the distance data, area data, and layer number data set into the feature calculation unit to generate matching results. The post-processing unit selects whether to delete or retain the specific organ marking image based on the matching results.

The present invention discloses a method for implementing a multi-branch enhanced diagnostic network for locating coronary artery occlusion. The present invention adopts a multi-branch enhanced network, including a local feature extraction module and a global feature extraction module. The local feature extraction module uses a wavelet attention mechanism to enhance the local feature representation, and the global feature extraction module uses a real-virtual enhanced Fourier neural operator to extract global features in the frequency domain; the multi-branch enhanced network is divided into five branches, four of which are used for lesion detection in different coronary artery regions, and the fifth branch fuses 12-lead signals to extract global frequency domain features. The defined adaptive misclassification penalty loss function effectively addresses the problem of category imbalance by dynamically adjusting the penalty coefficients of false negative and false positive samples. The present invention significantly improves the accuracy and robustness of electrocardiogram signal analysis, and provides an efficient and non-invasive detection tool for the early diagnosis of coronary heart disease.

The utility model provides a cross contamination prevention metal bath which comprises a metal bath body and a heating template which is arranged in the metal bath body and is provided with a centrifugal tube hole site upwards, and a hemispherical cover plate is hinged to the upper portion of the metal bath body. The hemispherical cover plate is provided with a centrifugal tube plug pressure applying assembly which penetrates through and extends to the upper part of the hemispherical cover plate. According to the cross contamination prevention metal bath, the sample pressing plate is additionally arranged on the metal bath, the sample pressing plate can firmly press a centrifugal tube cover plug through adjustment of the lead screw on the nut, and the situation that the cover plug bounces off due to the fact that steam pressure in a tube is increased is effectively prevented. Therefore, small molecule DNA / RNA fragments in the positive sample cannot be splashed to form aerosol due to the fact that the cover plug is bounced off, and other negative samples cannot be polluted, so that false positive results of negative sample detection results are avoided, the accuracy and reliability of DNA / RNA detection are greatly improved, it is ensured that the experimental results truly reflect sample conditions, and the accuracy and reliability of DNA / RNA detection are improved. And an accurate basis is provided for medical diagnosis.

The invention discloses a monitoring evaluation method and system based on historical motion data, and relates to the technical field of intelligent monitoring and data analysis, and the method comprises the steps: S1, extracting a plurality of motion features from historical motion data, and building a feature library; the method comprises the steps of S1, building an anomaly detection model and training the model to adapt to different motion scenes, S3, counting the occurrence frequency of false positive and analyzing the cause of the false positive according to a detection result output by the trained anomaly detection model, and S4, optimally setting a detection threshold value of an abnormal motion mode based on an analysis result to reduce the probability of the false positive. According to the monitoring evaluation method and system based on the historical motion data, the detection threshold value of the abnormal motion mode is optimally set so as to solve the problem that the false positive is too high in early warning.

The application discloses a Mendelian randomization analysis method for correcting sample overlap and pleiotropic bias, and is based on a joint correction framework of IVW, HDL and MR-PRESSO, in which pleiotropic abnormal SNPs are removed first, and then sample overlap bias is quantified and corrected, so that the problem that existing methods can only correct single bias is solved, synchronous and effective correction of sample overlap and pleiotropic compound bias is realized, the estimation accuracy can be ensured in a complex scene with a high sample overlap ratio and pleiotropy, meanwhile, the dependence of MR-APSS and other methods on the InSIDE assumption is abandoned, the problem that the CAUSE method is too conservative is avoided, the false positive rate can be controlled while maintaining high causal discovery sensitivity, the method can be directly packaged into a computer program, and is applied to epidemiological causal inference research of large-scale GWAS summary data, and is efficient in calculation and wide in applicability.

This paper proposes an instance segmentation method, device, and readable storage medium for analyzing vascular invasion in early-stage colorectal cancer. The method constructs an end-to-end automated process: first, digital pathology images are cropped at multiple magnifications and overlapped, and highly sensitive instance segmentation is performed using a Mask2former instance segmentation network. An efficient union-find algorithm is then used to merge overlapping masks, and an ROI expansion algorithm is used to recursively complete incomplete instances. Full-slice alignment of HE, CD-31, and D2-40 staining is achieved through rigid-nonrigid micro-registration, and instance-level tiles of the three modalities are cropped based on the registration results. Finally, a hybrid expert network enhanced by a large pathology model is used to fuse multimodal features and dynamically screen expert pathways for true and false positive discrimination. This invention provides an efficient technical solution for the precise diagnosis of early-stage colorectal cancer, suitable for pathology big data analysis and clinical auxiliary diagnosis.

A method of using CST1 and GPX4 as a gastric cancer metastasis marker or prognostic marker. The method detects the protein expression of CST1 and GPX4, draws an ROC curve using SPSS software, obtains the sensitivity and specificity of the diagnosis, and calculates the area under the curve AUC value. Finally, the gastric cancer samples are classified according to the AUC value, sensitivity and specificity, and applied to the diagnosis of gastric cancer metastasis. CST1 and GPX4 are used as markers for joint detection of gastric cancer, and the detection has strong specificity and high sensitivity, which can effectively avoid false positive or false negative results that are prone to occur when CST1 or GPX4 is used alone as a diagnostic marker, and provide reliable information for the early diagnosis of gastric cancer metastasis. The method draws a survival curve through follow-up and survival analysis, determines that patients with double positive results of CST1 and GPX4 have a worse prognosis, and provides a biomarker for predicting the prognosis of gastric cancer. The present invention provides a combined biomarker for early diagnosis and prognosis of gastric cancer metastasis with strong specificity and high sensitivity, and its application.

This application discloses a method, apparatus, and storage medium for detecting fetal chromosomal aneuploidy. The method for detecting fetal chromosomal aneuploidy includes calculating a new Z-value for the sample based on the fetal DNA concentration, Z-value, and chimerism of the cell-free DNA in the pregnant woman's blood; the new Z-value is used to determine whether fetal chromosomal aneuploidy has occurred. Chimerism is the ratio of abnormal fetal cells to all fetal cells. This application is the first to incorporate chimerism into the detection of fetal chromosomal aneuploidy, comprehensively considering three variables—fetal DNA concentration, chimerism, and Z-value—to calculate a new Z-value, which improves the accuracy of NIPT detection, provides excellent differentiation between true positive and false positive samples, and reduces false positives. The new Z-value conforms to a normal distribution, meeting current regulatory and clinical requirements, reducing data distribution volatility, thereby reducing the gray area rate, reducing the retest rate, and improving the stability of the test results.

The application relates to the technical field of metabolomics analysis, and discloses an AI-assisted metabolomics difference analysis method, which comprises the following steps: performing data preprocessing on biological groups to obtain preprocessed data; identifying user input instructions, obtaining corresponding biological groups and output content categories based on the user input instructions; performing difference analysis on the preprocessed data corresponding to the biological groups through an AI analysis model to obtain difference analysis results and output content categories; and generating a complete analysis report according to the difference analysis results and the output content categories. Through the data preprocessing process, the application can separate and remove system variation (interfered by non-biological factors such as batch effect, instrument drift and sample collection time difference) from the data, highlight the real group difference, avoid false positive or false negative results, and ensure the accuracy of the analysis results when the AI performs difference analysis.

The utility model provides a sample adding and smearing device for a syphilis non-specific antibody detection test, relates to the technical field of medical instruments, and aims to solve the problems that the device is inconvenient to smear and completely spread samples on different reaction cards at the same time, and when the sample size is large, a long time difference exists between early-stage sample adding and later-stage sample adding, so that the time difference is large. The device comprises a suction tube and a sample adding tube, wherein the suction tube is arranged on the suction tube; a group of fixing blocks are slidably mounted at the outer end of the suction tube, a group of pistons are slidably mounted in the suction tube, and the fixing blocks at the outer end of the suction tube are inserted into limiting grooves in the top of the movable frame; according to the reaction card smearing device, samples on different reaction cards can be conveniently smeared at the same time and completely spread, the use efficiency of the device is effectively improved, the smearing plate can be conveniently and automatically fixed at the horizontal position, and the use effect of the device is further improved.

The present invention discloses the use of KAT6A in the preparation of a diagnostic kit for colorectal cancer liver metastasis. This invention, for the first time, discovers the use of the KAT6A gene as a diagnostic marker for CRC liver metastasis in the early diagnosis or prognosis of CRC liver metastasis. Furthermore, the inventors have verified through experiments that, under the same conditions, KAT6A offers superior diagnostic advantages over CEA and AFP, currently discovered potential diagnostic markers for colorectal cancer, avoiding the occurrence of false positives. Therefore, it can be used as an early predictive indicator for colorectal cancer, for early screening and diagnosis of the disease, filling a gap in the early diagnosis of CRC liver metastasis.

This invention discloses a method and system for constructing an AI-assisted diagnostic agent for pulmonary embolism based on ontology and knowledge graph, belonging to the fields of medical information systems and medical artificial intelligence technology. By integrating a pulmonary embolism-specific medical ontology, a clinically aligned knowledge graph, and thought chain reasoning, this invention leverages the hard constraints of ontology knowledge to achieve interpretability analysis of the entire process of AI-assisted diagnosis for pulmonary embolism, precise suppression of AI illusions specific to pulmonary embolism, end-to-end quality control, and closed-loop optimization. It is particularly suitable for rapid diagnosis of acute and critical pulmonary embolism, AI-assisted identification of pulmonary artery filling defects in CTPA images, thrombosis severity stratification, and emergency treatment decision tracing and regulatory compliance quality control scenarios. It can be adapted to the implementation, regulatory verification, and clinical optimization of various pulmonary embolism AI imaging diagnostic and emergency auxiliary diagnosis and treatment systems, specifically addressing core illusion problems in pulmonary embolism AI diagnosis such as false positive embolus identification and disordered risk stratification.

The invention belongs to the technical field of medical examination, and provides a DNA nanoflower system based on RCA for detecting miR-21 and APE1. The DNA nanoflower provided by the invention can generate fluorescence only under the condition that APE1 and miR-21 exist at the same time, so that the generation of false positive is avoided. The sensor disclosed by the invention has the advantages of good detection performance, high sensitivity and convenience in operation, and has a potential application prospect in cancer monitoring.

The present invention discloses a method for identifying false positives in polyp detection based on a cross-level incomplete connection mechanism, belonging to the technical field of colonoscopy polyp detection. It includes constructing a spatial multi-path mechanism to locate the characteristic regions of polyps shown on the feature map. The feature fusion main body fuses lesion features of different sizes using feature maps of three scales to form sets of positive sample data and negative sample data. After training the positive samples of the same batch and the same scale, the cosine similarity loss is calculated. The present invention locates the characteristic regions of polyps on the feature map through the spatial multi-path mechanism, forms sets of positive sample data and negative sample data, calculates the cosine similarity loss after training the positive samples of the same batch, and at the same time combines a single-stage model to perform multi-scale sampling on the negative sample data, and combines the maximum likelihood estimation to select the negative sample with the highest confidence score, improving the problem of high false alarm rates in detecting colon polyps using existing R-CNN methods and active learning-based methods.

Disclosed herein are methods for detecting a false positive initial sample result in a subject initially identified as having, or at risk for, cancer. Such methods can be performed on a sample obtained from the subject while undergoing a colonoscopy. Thus, methods can be useful for confirming or contradicting the initial identification that the high risk subject is at risk for cancer. Altogether, such methods are valuable for improving precision of a cancer test.

This invention discloses a hairpin-mediated isothermal amplification (HAMP) assay for nucleic acid detection. The method provides a rapid, efficient, and highly specific alternative to conventional PCR, demonstrating accurate detection without false positives or false negatives. It is optimized for resource-limited settings and applicable to a broad range of diagnostic targets.

The invention relates to the technical field of in-vitro diagnosis, in particular to a kit for detecting an allergen-specific IgE (Immunoglobulin E) antibody. The kit comprises an anti-interference agent used for being mixed with a to-be-detected sample, an immunolabelled allergen, a peroxidase compound capable of being combined with an immunolabel and catalyzing color development of a substrate, and the substrate capable of being catalyzed by peroxidase to develop color, the anti-interference agent is prepared from the following components in parts by weight: 3 to 6 parts of polystyrene microspheres, 12 to 18 parts of L-glucoside and 6 to 10 parts of casein. The kit provided by the invention can effectively reduce false positive and false positive results, so that the accuracy of the detection result is improved.

The present application addresses the technical problem of reducing false negatives and false positives in nuclear medicine image diagnosis. The diagnostic assistance device of the embodiments has a collating section and a presuming section. The collating section collates information on the accumulation site of a diagnostic drug, i.e., first site information, obtained from nuclear medicine imaging of a subject, and information on the source site of exosomes including the diagnostic drug, i.e., second site information, collected from the subject. The presuming section presumes that a tumor exists at a site where the collated results are consistent between the first site information and the second site information.

The invention discloses a physical examination data analysis system based on artificial intelligence, which relates to the technical field of data analysis, comprises a classification module, an analysis module and a marking module, and is characterized in that classification rules are set, attribute features are obtained through first analysis, time features are obtained through second analysis, and first marking is carried out. Multi-dimensional heterogeneous physical examination data is mapped to a unified semantic space by using a classification rule, false negative and false positive are avoided, cross section and longitudinal fusion analysis is realized through a two-stage model of attribute features of the first analysis and time features of the second analysis, and the analysis accuracy is improved. The classification rule and the time sequence model can be updated in a closed-loop mode every time new physical examination data is received, manual retraining is not needed, the maintenance cost is close to zero, original fragmented physical examination data are converted into explainable, intervened and traceable health management decisions through classification, attribute, time and marking four-level assembly lines, and the management efficiency is improved. Five values of technology precision, clinical early screening, economy, efficient management, compliance and safety are realized.

The bronchoalveolar lavage fluid collecting device comprises a bottle body, three independent cavities are formed in the bottle body, each cavity is provided with an outlet, the bottle body is provided with a fluid inlet pipe and a negative pressure pipe, the fluid inlet pipe and the negative pressure pipe are communicated with the three cavities through connecting pipes respectively, and the connecting pipes are communicated with the three cavities. A switching mechanism for independently controlling the opening or closing of each chamber connecting pipe is arranged; the bronchoalveolar lavage device is simple in structure and reasonable in design, samples of different parts can be collected in the bronchoalveolar lavage process through the three independent cavities, collection bottles do not need to be frequently replaced, the risks of sample loss or mixing and pollution are avoided, and the false positive occurrence rate is reduced; the switching mechanism can realize rapid switching of the collection chamber, reduce the steps of disconnecting and reconnecting the pipeline, save the operation time, reduce the risk of complications caused by too long operation time of a patient, and improve the diagnosis accuracy and the overall safety.