A raman spectrum analysis device for quantitative detection of blood tumor markers

Abstract

The application belongs to the technical field of biomedical detection, and discloses a Raman spectrum analysis device for quantitative detection of blood tumor markers, which comprises a blood collection and centrifugal unit with an adaptive load adjustment mechanism, which is used for real-time monitoring of blood density and composition, and automatic adjustment of centrifugal force and time parameters according to the blood density and composition; a fiber Raman spectrometer equipped with an automatic wavelength calibration system, wherein an ultrafast laser pulse emitter and a high-sensitivity photon counting detector are integrated on the fiber Raman spectrometer; the application enhances the Raman scattering signal by adopting a quantum entangled light source and a nonlinear optical crystal, and can significantly improve the detection capability of low-concentration tumor markers. This improvement enables the device to accurately detect the presence of tumor markers in the early stage of cancer, providing strong support for early treatment of patients.

Description

Technical Field

[0001] This invention belongs to the field of biomedical detection technology, specifically relating to a Raman spectroscopy analysis device for quantitative detection of blood tumor markers. Background Technology

[0002] In the field of medical diagnostics, the detection of blood tumor markers plays a crucial role in the early detection, diagnosis, and treatment of cancer. However, existing blood tumor marker detection technologies face numerous challenges. First, because the concentration of tumor markers in the blood is often very low, especially in the early stages of cancer, the detection process is highly susceptible to various interfering factors, such as biological background noise and degradation during sample processing, leading to insufficient detection sensitivity. Second, the significant differences in blood composition between individuals limit the accuracy and reliability of traditional detection methods. Furthermore, existing detection technologies often only provide qualitative results and cannot achieve precise quantification of tumor markers, which to some extent restricts their application in clinical diagnosis and treatment. Therefore, a Raman spectroscopy analysis device for the quantitative detection of blood tumor markers is proposed to address the above problems. Summary of the Invention

[0003] The purpose of this invention is to provide a Raman spectroscopy analysis device for quantitative detection of blood tumor markers, so as to solve the problems mentioned in the background art.

[0004] To achieve the above objectives, the present invention provides the following technical solution: a Raman spectroscopy analysis device for quantitative detection of blood tumor markers, comprising:

[0005] A blood collection centrifugation unit with an adaptive load adjustment mechanism is used to monitor blood density and composition in real time, and automatically adjust centrifugation force and time parameters based on blood density and composition.

[0006] A fiber Raman spectrometer equipped with an automatic wavelength calibration system integrates an ultrafast laser pulse emitter and a high-sensitivity photon counting detector. The fiber Raman spectrometer is based on a quantum entangled light source and nonlinear optical crystal to enhance Raman scattering signals, and is used to simultaneously capture and analyze spectral data of multiple wavelengths.

[0007] The image optimization unit, based on an adaptive noise suppression algorithm and a multi-scale image fusion algorithm, preprocesses the Raman spectrum image to separate the target signal from complex background noise and enhances the image through a high-resolution image reconstruction algorithm.

[0008] The machine learning and diagnostic unit is based on an advanced deep learning framework that combines deep reinforcement learning algorithms and multimodal learning mechanisms. It includes an STCN network for feature learning and pattern recognition. The machine learning and diagnostic unit also includes an adaptive decision support module for updating and optimizing the detection model in real time based on clinical data and research progress.

[0009] The cancer cell identification and quantification unit, based on a multidimensional Raman spectral feature analysis calculation model, combines machine learning algorithms and spectral data processing algorithms, defining the spectral data matrix as follows: ,in Represents spectral sampling points, The normalized matrix is ​​obtained by normalizing the spectral data to represent the number of wavelength channels. Calculate its mean squared error weighted matrix. The formula used is:

[0010] ;

[0011] Calculate the weighted index of spectral features A nonlinear mapping relationship between the number of cancer cells and spectral intensity was constructed;

[0012] in, Represents the first element in the original spectral data matrix. The sampling point at the th sampling point Spectral intensity of each wavelength channel Represents the first normalized spectral data matrix. The sampling point at the th sampling point Spectral intensity of each wavelength channel Representing the The mean of the normalized spectral data for each wavelength channel Represents the total number of spectral sampling points. Represents the total number of wavelength channels. Represents the normalized first The sampling point at the th sampling point The weighted average deviation of each wavelength channel.

[0013] Preferably, the adaptive load adjustment mechanism adopts the following algorithm formula: F=f(ρ,C), where F represents centrifugal force, ρ represents blood density, C represents blood component concentration, and f represents adaptive adjustment function.

[0014] Preferably, the Raman signal enhancement algorithm for quantum entanglement adopts the following formula: I Raman =g(ϕ,χ,P), where I Ramanϕ represents the Raman scattering signal intensity, χ represents the phase of the quantum entangled source, P represents the conversion efficiency of the nonlinear optical crystal, g represents the laser pulse power, and g represents the signal enhancement function.

[0015] Preferably, the adaptive noise suppression algorithm uses the following formula: (x,y)=I(x,y)−h(N(x,y)), where (x,y) represents the denoised image, I(x,y) represents the original image, N(x,y) represents the noise, and h represents the noise suppression function.

[0016] Preferably, the multi-scale image fusion algorithm adopts the following formula: I fused = w i ⋅I i (x,y), where I fused I represents the fused image. i (x,y) represents the detail of the i-th layer of the image, w i This indicates the fusion weight.

[0017] Preferably, the loss function formula of the STCN network is as follows:

[0018] ;

[0019] Where L represents the loss function, Y true Y represents the true label. pred θ represents the predicted label, θ represents the network parameters, and λ represents the regularization coefficient, used to prevent overfitting.

[0020] Preferably, the Raman spectroscopy analysis device further includes a data storage unit for storing all data generated during the detection process, including raw spectral data, processed image data, and data obtained using the formula:

[0021] ;

[0022] Calculate the corrected spectral intensity Diagnostic results and user information, etc., are used for subsequent analysis and tracing;

[0023] in, Represents the corrected spectral intensity. Representing the The original spectral intensity of each sampling point The mean of the original spectral intensities of all sampling points. The standard deviation of the original spectral intensity of all sampling points Representing the The weighting coefficients for each sampling point Represents the total number of sampling points. An index representing a single sampling point.

[0024] Preferably, the Raman spectroscopy analysis device further includes an automatic calibration unit for calibrating the Raman spectrometer before each detection to ensure the accuracy and consistency of the spectral data. The automatic calibration unit includes a calibration light source and a calibration sample. The calibration light source is used to provide light with known spectral characteristics, and the calibration sample is used to provide substances with known Raman spectral characteristics for calibrating the Raman spectrometer before detection.

[0025] Preferably, the Raman spectroscopy analysis device further includes a remote monitoring and diagnostic unit, which is connected to the device via a network and is used to remotely monitor the operating status of the device, receive detection data, and perform remote fault diagnosis and updates.

[0026] Preferably, the Raman spectroscopy analysis device further includes a user interface for displaying detection results, providing operating instructions, and receiving user input.

[0027] Compared with the prior art, the beneficial effects of the present invention are:

[0028] (1) This invention enhances the Raman scattering signal by employing a quantum entangled light source and a nonlinear optical crystal, thereby significantly improving the detection capability for low-concentration tumor markers. This improvement enables the device to accurately detect the presence of tumor markers in the early stages of cancer, providing strong support for early treatment of patients.

[0029] (2) By integrating an adaptive blood collection centrifugation unit, an image optimization unit, and a machine learning and diagnostic unit, this invention achieves efficient processing and accurate analysis of blood samples. Through the collaborative work of multiple units, the interference of biological background noise can be minimized, thereby improving the accuracy and reliability of detection.

[0030] (3) By employing a multidimensional Raman spectral feature analysis calculation model, this invention can construct a complex nonlinear mapping relationship between the number of cancer cells and their spectral intensity, thereby achieving accurate quantitative detection of tumor markers. This improvement provides more accurate data support for clinical diagnosis and treatment. Attached Figure Description

[0031] Figure 1 This is a structural block diagram of the present invention. Detailed Implementation

[0032] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0033] Please see Figure 1 As shown, the present invention provides the following technical solution:

[0034] Implementation Method 1

[0035] This embodiment provides a specific implementation of a Raman spectroscopy analysis device for the quantitative detection of blood tumor markers. First, the blood collection centrifugation unit in the device utilizes an adaptive load adjustment mechanism to automatically adjust the centrifugal force F and time parameters based on the real-time monitored blood density ρ and component concentration C, using the algorithm F=f(ρ,C), to ensure efficient separation of plasma from solid components. Next, the fiber optic Raman spectrometer utilizes a quantum entangled light source and a nonlinear optical crystal, using algorithm I... Raman =g(ϕ,χ,P) enhances the Raman scattering signal and simultaneously captures and analyzes spectral data at multiple wavelengths, where I Raman Let ε represent the Raman scattering signal intensity, ϕ represent the phase of the quantum entangled source, χ represent the conversion efficiency of the nonlinear optical crystal, P represent the laser pulse power, and g represent the signal enhancement function. The image optimization unit employs an adaptive noise suppression algorithm. (x,y)=I(x,y)−h(N(x,y)) and multi-scale image fusion algorithm I fused = w i ⋅I i (x,y) preprocesses the spectral image to improve image quality, where Let (x,y) represent the denoised image, I(x,y) represent the original image, N(x,y) represent the noise, and h represent the noise suppression function, where I... fused I represents the fused image. i (x,y) represents the detail of the i-th layer of the image, w i The fusion weights are represented by the machine learning and diagnostic unit, which, based on the STCN network and adaptive decision support module, performs feature learning and pattern recognition on the processed spectral data and updates and optimizes the detection model in real time. Finally, the cancer cell identification and quantification unit utilizes a multi-dimensional Raman spectral feature analysis calculation model, combined with machine learning algorithms and spectral data processing algorithms, to construct a nonlinear mapping relationship between the number of cancer cells and spectral intensity, achieving accurate quantitative detection of tumor markers.

[0036] Implementation Method 2

[0037] In this embodiment, we further describe the device's data storage and automatic calibration functions. The data storage unit stores all data generated during the detection process, including raw spectral data, processed image data, diagnostic results, and user information. This data can be used for subsequent analysis and traceability. The automatic calibration unit includes a calibration light source and a calibration sample. Before each detection, the calibration light source provides light with known spectral characteristics, and the calibration sample provides substances with known Raman spectral characteristics to ensure the accuracy and consistency of the Raman spectrometer. Furthermore, the remote monitoring and diagnostic unit is connected to the device via a network, enabling remote monitoring of the device's operating status, receiving detection data, and performing remote fault diagnosis and updates, thus improving the device's reliability and maintainability.

[0038] Implementation Method 3

[0039] This embodiment focuses on the device's user interface and remote monitoring and diagnostic functions. The user interface provides an intuitive operating and display interface for showing test results, providing operation guides, and receiving user input, enabling users to easily operate the device and obtain test results. The remote monitoring and diagnostic unit, connected via a high-speed network, enables remote real-time monitoring and diagnostics of the device. Experts can monitor and diagnose the device remotely, ensuring stable operation and data accuracy. Furthermore, the remote monitoring and diagnostic unit can be remotely updated and optimized according to actual needs, improving the device's intelligence and adaptability.

[0040] Implementation Method 4

[0041] In this embodiment, we present a specific application scenario. A hospital uses the Raman spectroscopy analysis device provided by this invention to quantitatively detect blood tumor markers. First, blood samples from patients are collected through a blood collection centrifugation unit, and the centrifugation force and time parameters are automatically adjusted for separation. Next, a fiber optic Raman spectrometer is used to perform Raman spectroscopy detection on the separated plasma, simultaneously capturing and analyzing spectral data at multiple wavelengths. Then, an image optimization unit preprocesses the spectral images to improve image quality. Finally, a machine learning and diagnostic unit and a cancer cell identification and quantification unit are used to perform feature learning and pattern recognition on the processed spectral data, constructing a nonlinear mapping relationship between the number of cancer cells and spectral intensity, thereby achieving accurate quantitative detection of tumor markers. The detection results show that the device has high sensitivity and accuracy, providing strong support for clinical diagnosis and treatment.

[0042] Implementation Method 5

[0043] This embodiment provides a Raman spectroscopy analysis device for quantitative detection of blood tumor markers, and its operation steps are as follows:

[0044] Blood Collection and Centrifugation: Blood samples are collected from patients using the blood collection centrifugation unit with an adaptive load adjustment mechanism in the device. This unit monitors blood density and composition in real time and automatically adjusts centrifugation force and time parameters based on this information to ensure optimal centrifugation results.

[0045] Raman spectroscopy acquisition: The centrifuged blood sample is placed in a fiber optic Raman spectrometer equipped with an automatic wavelength calibration system. This spectrometer uses a quantum entangled light source and a nonlinear optical crystal to enhance the Raman scattering signal, simultaneously capturing and analyzing spectral data at multiple wavelengths.

[0046] Image preprocessing: Using the image optimization unit, the Raman spectrum image is preprocessed with an adaptive noise suppression algorithm and a multi-scale image fusion algorithm to separate the target signal from the complex background noise, and the image quality is enhanced by a high-resolution image reconstruction algorithm.

[0047] Machine Learning and Diagnosis: The preprocessed image data is input into the machine learning and diagnosis unit, which performs feature learning and pattern recognition based on deep reinforcement learning algorithms and multimodal learning mechanisms, and outputs diagnostic results.

[0048] Cancer cell identification and quantification: Based on the diagnostic results, the cancer cell identification and quantification unit uses a multidimensional Raman spectral feature analysis calculation model, combined with machine learning algorithms and spectral data processing algorithms, to calculate the nonlinear mapping relationship between the number of cancer cells and spectral intensity, thereby obtaining the specific number of cancer cells.

[0049] Implementation Method Six

[0050] This embodiment provides a Raman spectroscopy analysis device for quantitative detection of blood tumor markers, characterized in that it further includes a data storage unit and an automatic calibration unit:

[0051] Data storage: During the detection process, all generated data, including raw spectral data, processed image data, diagnostic results, and user information, are stored in the data storage unit for subsequent analysis and traceability.

[0052] Automatic calibration: Before each test, the automatic calibration unit calibrates the Raman spectrometer. This unit includes a calibration light source and a calibration sample. The calibration light source provides light with known spectral characteristics, and the calibration sample provides substances with known Raman spectral characteristics, ensuring the accuracy and consistency of the spectral data.

[0053] Detection process: Follow the steps in Specific Implementation Method 1 to perform blood collection, centrifugation, Raman spectroscopy acquisition, image preprocessing, machine learning and diagnosis, and cancer cell identification and quantification.

[0054] Implementation Method Seven

[0055] This embodiment provides a Raman spectroscopy analysis device for quantitative detection of blood tumor markers, characterized in that it further includes a remote monitoring and diagnostic unit and a user interface:

[0056] Remote monitoring and diagnostics: The remote monitoring and diagnostics unit connects to the device via a network, monitors the device's operating status in real time, receives test data, and performs remote fault diagnosis and updates. This ensures efficient device operation and accurate data transmission.

[0057] User Interface: The user interface provides a user-friendly interface for displaying test results, providing operation guides, and receiving user input. Users can easily understand the test results and operation procedures through this interface.

[0058] Detection process: Blood collection, centrifugation, Raman spectroscopy acquisition, image preprocessing, machine learning and diagnosis, and cancer cell identification and quantification are performed according to the steps of Specific Implementation Method 1. Simultaneously, remote monitoring and user interaction are achieved through a remote monitoring and diagnostic unit and a user interface.

[0059] Implementation Method Eight

[0060] This embodiment provides a Raman spectroscopy analysis device for quantitative detection of blood tumor markers, characterized by the specific application of adaptive load adjustment mechanism, quantum entanglement Raman signal enhancement algorithm, adaptive noise suppression algorithm, and multi-scale image fusion algorithm:

[0061] Adaptive load adjustment mechanism: Based on blood density and component concentration, the adaptive adjustment function f(ρ,C) calculates the optimal centrifugal force F to achieve the best centrifugation effect for blood samples.

[0062] A Raman signal enhancement algorithm based on quantum entanglement: Utilizing the phase φ of the quantum entangled light source, the conversion efficiency χ of the nonlinear optical crystal, and the laser pulse power P, the Raman scattering signal intensity I is calculated using the signal enhancement function g(φ,χ,P). Raman This enhances the intensity of the Raman spectral signal.

[0063] Adaptive noise suppression algorithm: The original image I(x,y) is subjected to noise suppression processing. The noise suppression function h(N(x,y)) is used to calculate the denoised image (x,y), which effectively removes background noise.

[0064] Multi-scale image fusion algorithm: for multi-layer image detail Ii The image (x, y) is fused, and the fused image Ifused is calculated using the fusion weight wi, thereby improving the image resolution and clarity.

[0065] Detection process: Blood collection, centrifugation, Raman spectroscopy acquisition, image preprocessing, machine learning and diagnosis, and cancer cell identification and quantification are performed according to the steps of Specific Implementation Method 1. Simultaneously, the aforementioned algorithms are applied to achieve adaptive load adjustment, Raman signal enhancement, noise suppression, and image fusion.

[0066] Implementation Method Nine

[0067] This implementation emphasizes the application of the device in personalized medicine, particularly customized testing procedures for specific patient groups:

[0068] Patient information entry: First, the patient's basic information, including but not limited to age, gender, and medical history, is entered through the user interface. This information will be used for personalized adjustments in subsequent analysis.

[0069] Blood sample pretreatment: Based on the patient's specific information, the blood collection centrifugation unit with adaptive load adjustment mechanism is adjusted to the centrifugation parameters most suitable for the patient's blood characteristics to ensure sample quality.

[0070] Spectral Acquisition and Analysis: After automatic wavelength calibration, the fiber optic Raman spectrometer performs targeted spectral acquisition for specific tumor markers in patients, and optimizes signal intensity using quantum entangled light sources and nonlinear optical crystals.

[0071] Machine learning model tuning: The STCN network in the machine learning and diagnostic unit dynamically adjusts the model parameters based on the patient's personal information and existing clinical data to more accurately identify the patient's specific tumor markers.

[0072] Results Output and Interpretation: Diagnostic results are displayed through the user interface, along with personalized explanations and suggestions for the patient, helping doctors develop more precise treatment plans.

[0073] Implementation Method Eleven

[0074] This implementation emphasizes the application of the device in personalized medicine, particularly customized testing procedures for specific patient groups:

[0075] Patient information entry: First, the patient's basic information, including but not limited to age, gender, and medical history, is entered through the user interface. This information will be used for personalized adjustments in subsequent analysis.

[0076] Blood sample pretreatment: Based on the patient's specific information, the blood collection centrifugation unit with adaptive load adjustment mechanism is adjusted to the centrifugation parameters most suitable for the patient's blood characteristics to ensure sample quality.

[0077] Spectral Acquisition and Analysis: After automatic wavelength calibration, the fiber optic Raman spectrometer performs targeted spectral acquisition for specific tumor markers in patients, and optimizes signal intensity using quantum entangled light sources and nonlinear optical crystals.

[0078] Machine learning model tuning: The STCN network in the machine learning and diagnostic unit dynamically adjusts the model parameters based on the patient's personal information and existing clinical data to more accurately identify the patient's specific tumor markers.

[0079] Results Output and Interpretation: Diagnostic results are displayed through the user interface, along with personalized explanations and suggestions for the patient, helping doctors develop more precise treatment plans.

[0080] Implementation Method Twelve

[0081] This implementation focuses on the device's ability to continuously optimize and upgrade:

[0082] Real-time data updates: The data storage unit not only stores test data, but also regularly receives the latest research results and clinical data from global scientific research databases for use in updating the models of the machine learning and diagnostic units.

[0083] Remote software upgrade: The remote monitoring and diagnostic unit is not only used to monitor the status of the equipment, but also to receive and install software updates released by the manufacturer, ensuring that the device always uses the latest technology and algorithms.

[0084] User feedback loop: Collect user feedback on the testing process, result interpretation, etc. through the user interface to form a closed-loop feedback mechanism and continuously optimize user experience and testing accuracy.

[0085] Implementation Method Thirteen

[0086] This implementation highlights the device's ability to be deployed and work collaboratively in multiple locations within large medical institutions or research centers:

[0087] Multi-site data integration: Multiple Raman spectroscopy analysis devices deployed in different locations are connected through a network to form a distributed detection system, which can share detection data and model updates.

[0088] Centralized Management and Analysis: The remote monitoring and diagnostic unit is responsible for the centralized management of the equipment status and data flow of all sites, while providing advanced analysis functions such as cross-site data comparison and trend analysis.

[0089] Collaborative Research Platform: Provides a collaborative research platform for researchers and clinicians, supports cross-regional and cross-institutional collaborative projects, and accelerates the discovery of new tumor markers and the innovation of diagnostic methods.

[0090] Implementation Method Fourteen

[0091] This implementation emphasizes the device's rapid response capability in emergency situations:

[0092] Emergency Detection Mode: In emergency situations, such as rapid screening for suspected tumors, the device can activate a simplified process, reducing unnecessary steps and speeding up the detection process.

[0093] Priority data processing: The data storage unit and machine learning and diagnostic unit have priority processing capabilities to ensure that detection data in emergency situations can be analyzed and reported first.

[0094] Telemedicine support: Through remote monitoring and diagnostic units, experts can remotely guide the operations of primary healthcare institutions and provide immediate medical consultation and support.

[0095] Implementation Method Fifteen

[0096] This implementation emphasizes the application of the device in clinical trials and drug development:

[0097] Clinical trial support: The device can be used to support clinical trials of new drugs by accurately detecting changes in tumor markers in blood samples to assess drug efficacy and safety.

[0098] Drug response prediction: Using machine learning algorithms, the device can predict a patient's response to a specific drug based on the patient's blood spectral characteristics, thereby personalizing the selection of the best treatment plan.

[0099] Research and development data sharing: The device can share testing data with drug research and development institutions, promote data exchange and analysis in the process of new drug development, and accelerate the market launch of new drugs.

[0100] Implementation Method Sixteen

[0101] This implementation focuses on enhancing the automation and intelligence of the device:

[0102] Fully automated process: By integrating an automated sample processing system, the device can achieve a fully automated process from blood collection to result output, reducing manual intervention and improving detection efficiency and accuracy.

[0103] Intelligent alarm system: The device has a built-in intelligent alarm system that automatically triggers an alarm and notifies relevant personnel to handle the situation when an anomaly or potential danger is detected.

[0104] AI-assisted diagnosis: Utilizing deep learning algorithms, the device can automatically identify and analyze spectral data, providing preliminary diagnostic suggestions and reducing the workload of doctors.

[0105] Implementation Method Seventeen

[0106] This embodiment emphasizes the role of the device in public health surveillance and disease prevention:

[0107] Large-scale screening: The device can be used for large-scale population screening, detecting potential cancer patients at an early stage by detecting tumor markers in blood samples, thereby improving the effectiveness of cancer prevention and control.

[0108] Epidemiological studies: The data collected by the device can be used for epidemiological studies to analyze differences in cancer incidence rates among different populations and regions, providing a basis for developing preventive measures.

[0109] Public health early warning: By combining other public health data, the device can monitor cancer incidence trends in real time, detect abnormalities in a timely manner, and provide early warning information to public health departments.

[0110] Implementation Method 18

[0111] This implementation highlights the application of the device in patient education and self-management:

[0112] Patient Education Platform: Through a user interface, the device can provide knowledge on cancer prevention, early detection, and self-management, improving patients' health awareness and self-management capabilities.

[0113] Personalized health plans: Based on the patient's test results and personal information, the device can create personalized health plans, including suggestions on diet, exercise, follow-up examinations, etc.

[0114] Community interaction: The device can connect patients to the patient community, allowing patients to share their experiences, insights and suggestions, forming a social network of mutual help and support.

[0115] Implementation Method Nineteen

[0116] This implementation emphasizes the integrated application of the device in interdisciplinary research:

[0117] Combining genomics and spectroscopy: The device can be combined with genomics technologies to provide more comprehensive cancer risk assessment and diagnostic information by simultaneously analyzing the spectral characteristics and genetic variations of blood samples.

[0118] Combined analysis of proteomics and spectroscopy: Raman spectroscopy is used to detect protein characteristics in blood samples, and combined with proteomics data, the functions and mechanisms of action of tumor markers can be further revealed.

[0119] Cross-validation of metabolomics and spectroscopy: The device can be combined with metabolomics technology to detect changes in metabolites in blood samples and cross-validate them with spectral data, thereby improving the sensitivity and specificity of cancer detection.

[0120] Implementation Method Twenty

[0121] In addition, this invention provides a Raman spectroscopy analysis device for quantitative detection of blood tumor markers, comprising:

[0122] A blood collection centrifugation unit with an adaptive load adjustment mechanism is used to monitor blood density and composition in real time, and automatically adjust centrifugation force and time parameters based on blood density and composition.

[0123] The fiber Raman spectrometer is equipped with an automatic wavelength calibration system. It integrates an ultrafast laser pulse emitter and a high-sensitivity photon counting detector. The fiber Raman spectrometer is based on a quantum entangled light source and nonlinear optical crystal to enhance Raman scattering signals, and is used to simultaneously capture and analyze spectral data of multiple wavelengths.

[0124] The image optimization unit, based on an adaptive noise suppression algorithm and a multi-scale image fusion algorithm, preprocesses the Raman spectrum image to separate the target signal from complex background noise and enhances the image through a high-resolution image reconstruction algorithm.

[0125] The machine learning and diagnostics unit is based on an advanced deep learning framework that combines deep reinforcement learning algorithms and multimodal learning mechanisms. It includes the STCN network for feature learning and pattern recognition. The machine learning and diagnostics unit also includes an adaptive decision support module for updating and optimizing the detection model in real time based on clinical data and research progress.

[0126] The cancer cell identification and quantification unit, based on a multidimensional Raman spectral feature analysis calculation model, combines machine learning algorithms and spectral data processing algorithms, defining the spectral data matrix as follows: ,in Represents spectral sampling points, The normalized matrix is ​​obtained by normalizing the spectral data to represent the number of wavelength channels. Calculate its mean squared error weighted matrix. The formula used is:

[0127] ;

[0128] Calculate the weighted index of spectral features A nonlinear mapping relationship between the number of cancer cells and spectral intensity was constructed;

[0129] in, Represents the first element in the original spectral data matrix. The sampling point at the th sampling point Spectral intensity of each wavelength channel Represents the first normalized spectral data matrix. The sampling point at the th sampling point Spectral intensity of each wavelength channel Representing the The mean of the normalized spectral data for each wavelength channel Represents the total number of spectral sampling points. Represents the total number of wavelength channels. Represents the normalized first The sampling point at the th sampling point The weighted average deviation of each wavelength channel.

[0130] Based on the multidimensional Raman spectral feature analysis calculation model, the spectral data matrix is ​​first obtained. ,in Represents spectral sampling points, The number of wavelength channels represents the spectral data for each sampling point, which is obtained from experimental data acquisition using a Raman spectrometer. The acquisition range is set between 400 nm and 1800 nm, with one spectral point acquired every 2 nm. The complete spectral data for each sample contains 700 wavelength channels, which are used to construct the initial spectral data matrix. The intensity value of each spectral point is digitized using a photodetector and recorded in the data storage unit. Each sample contains multiple spectral sampling points, and a dataset is established based on the spectral intensity distribution. The goal of normalization is to eliminate the light intensity differences between different samples. The minimum-maximum normalization method is used for normalization transformation, and the normalization formula is as follows:

[0131] ;

[0132] in, and They represent the first The minimum and maximum values ​​of each sampling point across all wavelength channels, after normalization. The values ​​are between [0,1], ensuring that different sample data are calculated within the same numerical range. After normalization, the mean of the normalized data is calculated. The formula for calculating the central tendency of each wavelength channel is as follows:

[0133] ;

[0134] The normalized data is weighted by mean square error, and the deviation of each spectral point from the mean is calculated to measure the dispersion of the data. The mean square error weighting matrix is ​​then calculated. The formula used is:

[0135] ;

[0136] Among them, the mean square deviation The calculation is as follows:

[0137] ;

[0138] By calculating the weighted deviation value for each wavelength channel This measures the fluctuation of the weights across samples and calculates the normalized weight distribution using the formula:

[0139] ;

[0140] Select wavelength channels with higher weights for feature extraction and set a threshold. Filter to meet The wavelength channels are determined, and the eigenvector matrix is ​​established. ,in This represents the index of the selected wavelength channels, and finally, a nonlinear mapping relationship between the number of cancer cells and spectral intensity is established based on the selected feature vector matrix.

[0141] Table 1. Example of Normalized Spectral Data

[0142]

[0143] As shown in Table 1, the spectral intensity data of different sampling points in each wavelength channel after normalization are between [0,1].

[0144] In addition, in this invention, the adaptive load adjustment mechanism adopts the following algorithm formula: F=f(ρ,C), where F represents centrifugal force, ρ represents blood density, C represents blood component concentration, and f represents adaptive adjustment function.

[0145] Furthermore, in this invention, the Raman signal enhancement algorithm for quantum entanglement adopts the following formula: I Raman =g(ϕ,χ,P), where I Raman ϕ represents the Raman scattering signal intensity, χ represents the phase of the quantum entangled source, P represents the conversion efficiency of the nonlinear optical crystal, g represents the laser pulse power, and g represents the signal enhancement function.

[0146] Furthermore, in this invention, the adaptive noise suppression algorithm described above adopts the following formula: (x,y)=I(x,y)−h(N(x,y)), where (x,y) represents the denoised image, I(x,y) represents the original image, N(x,y) represents the noise, and h represents the noise suppression function.

[0147] Furthermore, in this invention, regarding the aforementioned multi-scale image fusion algorithm, the multi-scale image fusion algorithm adopts the following formula: I fused = w i ⋅I i (x,y), where I fused I represents the fused image. i (x,y) represents the detail of the i-th layer of the image, w i This indicates the fusion weight.

[0148] Furthermore, in this invention, regarding the aforementioned STCN network: the loss function formula for the STCN network is as follows:

[0149] ;

[0150] Where L represents the loss function, Y true Y represents the true label. pred θ represents the predicted label, θ represents the network parameters, and λ represents the regularization coefficient, used to prevent overfitting.

[0151] Furthermore, the device also includes a data storage unit for storing all data generated during the detection process, including raw spectral data, processed image data, and data obtained using the formula:

[0152] ;

[0153] Calculate the corrected spectral intensity Diagnostic results and user information, etc., are used for subsequent analysis and tracing;

[0154] in, Represents the corrected spectral intensity. Representing the The original spectral intensity of each sampling point The mean of the original spectral intensities of all sampling points. The standard deviation of the original spectral intensity of all sampling points Representing the The weighting coefficients for each sampling point Represents the total number of sampling points. An index representing a single sampling point.

[0155] The data storage unit stores all data generated during the detection process. First, the raw spectral data is acquired by using a spectrometer to collect light intensity data at fixed time intervals. For example, in the wavelength range of 400nm to 700nm, sampling is performed at 10nm intervals to obtain a raw spectral data array. For this data, it is necessary to calculate the spectral mean and standard deviation to correct for noise fluctuations in the data. The calculation process begins with the mean calculation, i.e.

[0156] ;

[0157] Then calculate the standard deviation.

[0158] ;

[0159] Assuming the number of sampling points A set of data The mean was calculated to obtain The standard deviation is calculated as follows Next, normalization is performed, and the normalized spectral data is calculated as follows:

[0160] ;

[0161] With a certain sampling point Data For example, its normalization result is

[0162] ;

[0163] Next, the weighting coefficients need to be calculated. This coefficient depends on the background noise distribution of the measurement environment and the sensitivity of the equipment. Assuming that the noise level of the experimental environment varies with wavelength, empirical values ​​are used to set the weight array. Based on normalized data and weights, the corrected spectral intensity is finally calculated:

[0164] ;

[0165] Assuming the calculation yields This value represents the corrected spectral feature intensity. This data is stored for further analysis. After spectral data processing, the system stores this data and, in conjunction with the processed image data, records it as a pixel matrix at a resolution of 256x256. The recording method uses matrix storage.

[0166] ;

[0167] in The image represents the first Okay, number The pixel values ​​of a column, assuming a certain pixel data Then that point occupies a position in the stored data. The processed spectral and image data are stored in the storage unit for subsequent analysis. Finally, the diagnostic results and user information are stored synchronously with the above data to form a complete data chain for subsequent analysis and tracing.

[0168] Table 2 Spectral Data Storage Table

[0169]

[0170] Table 2 lists the calculation results for some spectral data. As shown in Table 2, after the calculation of the corrected spectral intensity contribution value is completed, it can be used for further calculations. .

[0171] in, Represents the corrected spectral intensity. Representing the The original spectral intensity of each sampling point The mean of the original spectral intensities of all sampling points. The standard deviation of the original spectral intensity of all sampling points Representing the The weighting coefficients for each sampling point Represents the total number of sampling points. An index representing a single sampling point. Represents the normalized spectral intensity. Represents the pixel values ​​of the processed image. This represents the stored image matrix.

[0172] Furthermore, the device also includes an automatic calibration unit for calibrating the Raman spectrometer before each detection to ensure the accuracy and consistency of the spectral data. The automatic calibration unit includes a calibration light source and a calibration sample. The calibration light source is used to provide light with known spectral characteristics, and the calibration sample is used to provide substances with known Raman spectral characteristics for calibrating the Raman spectrometer before detection.

[0173] Furthermore, the device also includes a remote monitoring and diagnostic unit, which is connected to the device via a network to remotely monitor the device's operating status, receive test data, and perform remote fault diagnosis and updates.

[0174] Furthermore, the device also includes a user interface for displaying test results, providing operating instructions, and receiving user input.

[0175] Furthermore, to achieve the above objectives, embodiments of the present invention also provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the Raman spectroscopy analysis device for quantitative detection of blood tumor markers according to embodiments of the present invention.

[0176] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, embodiments of the present invention can take the form of entirely hardware embodiments, entirely software embodiments, or embodiments combining software and hardware aspects. Furthermore, embodiments of the present invention can take the form of computer program products implemented on one or more computer-usable vehicles (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0177] Embodiments of the present invention are described with reference to flowchart illustrations and / or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A system that specifies functions in one or more boxes.

[0178] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing terminal device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including an instruction set implemented in a process. Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0179] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal equipment, causing a series of operational steps to be performed on the computer or other programmable terminal equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable terminal equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0180] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. "" and / or "" indicate that either one or both can be selected. Furthermore, the terms "includes," "contains," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that includes a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Without further limitations, an element defined by the statement "includes a..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes the element.

[0181] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the embodiments of the present invention, and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the protection scope of the present invention.

[0182] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A Raman spectroscopy analysis device for quantitative detection of blood tumor markers, characterized in that: include: A blood collection centrifugation unit with an adaptive load adjustment mechanism is used to monitor blood density and composition in real time, and automatically adjust centrifugation force and time parameters based on blood density and composition. A fiber Raman spectrometer equipped with an automatic wavelength calibration system integrates an ultrafast laser pulse emitter and a high-sensitivity photon counting detector. The fiber Raman spectrometer is based on a quantum entangled light source and nonlinear optical crystal to enhance Raman scattering signals, and is used to simultaneously capture and analyze spectral data of multiple wavelengths. The image optimization unit, based on an adaptive noise suppression algorithm and a multi-scale image fusion algorithm, preprocesses the Raman spectrum image to separate the target signal from complex background noise and enhances the image through a high-resolution image reconstruction algorithm. The machine learning and diagnostic unit is based on an advanced deep learning framework that combines deep reinforcement learning algorithms and multimodal learning mechanisms. It includes an STCN network for feature learning and pattern recognition. The machine learning and diagnostic unit also includes an adaptive decision support module for updating and optimizing the detection model in real time based on clinical data and research progress. The cancer cell identification and quantification unit, based on a multidimensional Raman spectral feature analysis calculation model, combines machine learning algorithms and spectral data processing algorithms, defining the spectral data matrix as follows: ,in Represents spectral sampling points, The normalized matrix is ​​obtained by normalizing the spectral data to represent the number of wavelength channels. Calculate its mean squared error weighted matrix. The formula used is: ； Calculate the weighted index of spectral features A nonlinear mapping relationship between the number of cancer cells and spectral intensity was constructed; in, Represents the first element in the original spectral data matrix. The sampling point at the th sampling point Spectral intensity of each wavelength channel Represents the first normalized spectral data matrix. The sampling point at the th sampling point Spectral intensity of each wavelength channel Representing the The mean of the normalized spectral data for each wavelength channel Represents the total number of spectral sampling points. Represents the total number of wavelength channels. Represents the normalized first The sampling point at the th sampling point The weighted average deviation of each wavelength channel.

2. The Raman spectroscopy analysis device for quantitative detection of blood tumor markers according to claim 1, characterized in that: The adaptive load adjustment mechanism adopts the following algorithm formula: F=f(ρ,C), where F represents centrifugal force, ρ represents blood density, C represents blood component concentration, and f represents adaptive adjustment function.

3. The Raman spectroscopy analysis device for quantitative detection of blood tumor markers according to claim 2, characterized in that: The Raman signal enhancement algorithm for quantum entanglement uses the following formula: I Raman =g(ϕ,χ,P), where I Raman ϕ represents the Raman scattering signal intensity, χ represents the phase of the quantum entangled source, P represents the conversion efficiency of the nonlinear optical crystal, g represents the laser pulse power, and g represents the signal enhancement function.

4. The Raman spectroscopy analysis device for quantitative detection of blood tumor markers according to claim 3, characterized in that: The adaptive noise suppression algorithm uses the following formula: (x,y)=I(x,y)−h(N(x,y)), where (x,y) represents the denoised image, I(x,y) represents the original image, N(x,y) represents the noise, and h represents the noise suppression function.

5. The Raman spectroscopy analysis device for quantitative detection of blood tumor markers according to claim 4, characterized in that: The multi-scale image fusion algorithm uses the following formula: I fused = w i ⋅I i (x,y), where I fused I represents the fused image. i (x,y) represents the detail of the i-th layer of the image, w i This indicates the fusion weight.

6. The Raman spectroscopy analysis device for quantitative detection of blood tumor markers according to claim 5, characterized in that: The loss function formula for the STCN network is as follows: ； Where L represents the loss function, Y true Y represents the true label. pred θ represents the predicted label, θ represents the network parameters, and λ represents the regularization coefficient, used to prevent overfitting.

7. A Raman spectroscopy analysis device for quantitative detection of blood tumor markers according to claim 6, characterized in that: It also includes a data storage unit for storing all data generated during the detection process, including raw spectral data, processed image data, and data obtained using the formula: ； Calculate the corrected spectral intensity Diagnostic results and user information, etc., are used for subsequent analysis and tracing; in, Represents the corrected spectral intensity. Representing the The original spectral intensity of each sampling point The mean of the original spectral intensities of all sampling points. The standard deviation of the original spectral intensity of all sampling points Representing the The weighting coefficients for each sampling point Represents the total number of sampling points. An index representing a single sampling point.

8. The Raman spectroscopy analysis device for quantitative detection of blood tumor markers according to claim 1, characterized in that: It also includes an automatic calibration unit for calibrating the Raman spectrometer before each detection to ensure the accuracy and consistency of the spectral data. The automatic calibration unit includes a calibration light source and a calibration sample. The calibration light source is used to provide light with known spectral characteristics, and the calibration sample is used to provide substances with known Raman spectral characteristics for calibrating the Raman spectrometer before detection.

9. A Raman spectroscopy analysis device for quantitative detection of blood tumor markers according to claim 1, characterized in that: It also includes a remote monitoring and diagnostic unit, which is connected to the device via a network and is used to remotely monitor the device's operating status, receive detection data, and perform remote fault diagnosis and updates.

10. A Raman spectroscopy analysis device for quantitative detection of blood tumor markers according to claim 9, characterized in that: It also includes a user interface for displaying test results, providing operation instructions, and receiving user input.

Images