Raman spectrum analysis device for quantitative detection of blood tumor markers

Abstract

The invention belongs to the technical field of biomedical detection, and discloses a Raman spectrum analysis device for quantitative detection of hematologic tumor markers, which comprises a blood collection centrifugal unit with a self-adaptive load regulation mechanism for monitoring blood density and components in real time, centrifugal force and time parameters are automatically adjusted according to the blood density and components; the optical fiber Raman spectrometer is provided with an automatic wavelength calibration system, and an ultrafast laser pulse transmitter and a high-sensitivity photon counting detector are integrated on the optical fiber Raman spectrometer. Raman scattering signals are enhanced by adopting a quantum entanglement light source and a nonlinear optical crystal; according to the invention, the detection capability on low-concentration tumor markers can be obviously improved. Due to the improvement, the device can accurately detect the existence of the tumor marker at the early stage of cancer, and powerful support is provided for early treatment of a patient.

Description

Technical Field

[0001] The present invention belongs to the technical field of biomedical detection, and in particular relates to a Raman spectroscopy analysis device for quantitative detection of blood tumor markers. Background Art

[0002] In the field of medical diagnosis, 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 extremely susceptible to various interfering factors, such as biological background noise and degradation during sample processing, resulting in insufficient detection sensitivity. Second, the blood composition varies significantly between individuals, which limits the accuracy and reliability of traditional detection methods. Furthermore, existing detection technologies often only provide qualitative test results and cannot accurately quantify tumor markers, which to some extent limits 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-mentioned issues. Summary of the Invention

[0003] The object of the present invention is to provide a Raman spectroscopy analysis device for quantitative detection of blood tumor markers to solve the problems raised in the above background technology.

[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 centrifuge unit with an adaptive load regulation mechanism that monitors blood density and composition in real time and automatically adjusts centrifugal force and time parameters based on blood density and composition;

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

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

[0008] A machine learning and diagnostic unit, based on an advanced deep learning framework that combines deep reinforcement learning algorithms with multimodal learning mechanisms, includes an STCN network for feature learning and pattern recognition, and also includes an adaptive decision support module for real-time updating and optimization of detection models based on clinical data and scientific research progress;

[0009] Cancer cell identification and quantification unit, based on multidimensional Raman spectroscopy feature analysis calculation model, combined with machine learning algorithm and spectral data processing algorithm, defines the spectral data matrix as ,in represents the spectrum sampling point, Represents the number of wavelength channels, normalizes the spectral data, and obtains the normalized matrix , calculate its mean square error weighted matrix , using the formula:

[0010] ;

[0011] Calculate spectral feature weighted index , construct a nonlinear mapping relationship between the number of cancer cells and spectral intensity;

[0012] in, Represents the first The sampling point is The spectral intensity of the wavelength channel, Represents the first in the normalized spectral data matrix The sampling point is The spectral intensity of the wavelength channel, Representative The mean of the normalized spectral data of the wavelength channels, Represents the total number of spectral sampling points, represents the total number of wavelength channels, Represents the normalized The sampling point is The weighted mean square error of the wavelength channels.

[0013] Preferably, the adaptive load regulation 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 an adaptive regulation function.

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

[0015] Preferably, the adaptive noise suppression algorithm 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.

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

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

[0018] ;

[0019] Among them, L represents the loss function, Y true represents the true label, Y pred Represents the predicted label, θ represents the network parameter, and λ represents the regularization coefficient, which is 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 the formula:

[0021] ;

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

[0023] in, represents the corrected spectral intensity, Representative The original spectral intensity of the sampling points, Represents the mean of the original spectral intensity of all sampling points, Represents the standard deviation of the original spectral intensity of all sampling points, Representative The weight coefficient of the sampling points, Represents the total number of sampling points, An index representing a single sample point.

[0024] Preferably, the Raman spectroscopy analysis 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 a substance with known Raman spectral characteristics for calibrating the Raman spectrometer before detection.

[0025] Preferably, the Raman spectroscopy analysis device further comprises a remote monitoring and diagnosis 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 comprises a user interface for displaying the test results, providing operation instructions and receiving user input.

[0027] Compared with the prior art, the present invention has the following beneficial effects:

[0028] (1) This invention uses a quantum entangled light source and nonlinear optical crystals to enhance Raman scattering signals. This invention can significantly improve the ability to detect 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) The present invention achieves efficient processing and accurate analysis of blood samples by integrating an adaptive blood collection centrifugal unit, an image optimization unit, and a machine learning and diagnosis unit. The collaborative work of multiple units can minimize the interference of biological background noise and improve the accuracy and reliability of detection.

[0030] (3) By employing a multidimensional Raman spectral feature analysis and computational model, the present invention is able to construct a complex nonlinear mapping relationship between the number of cancer cells and their spectral intensity, thereby enabling accurate quantitative detection of tumor markers. This improvement provides more accurate data support for clinical diagnosis and treatment. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 It is a structural block diagram of the present invention. DETAILED DESCRIPTION

[0032] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0033] See also Figure 1 As shown, the present invention provides the following technical solutions:

[0034] Implementation Method 1

[0035] This embodiment provides a specific implementation of a Raman spectroscopy analysis device for quantitative detection of blood tumor markers. First, the blood collection centrifugal unit in the device uses an adaptive load regulation mechanism to automatically adjust the centrifugal force F and time parameters based on the real-time monitored blood density ρ and component concentration C through the algorithm F=f(ρ,C) to ensure efficient separation of plasma and solid components. Then, the fiber Raman spectrometer uses a quantum entangled light source and nonlinear optical crystals to automatically adjust the centrifugal force F and time parameters through the 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 represents the Raman scattering signal intensity, ϕ represents the phase of the quantum entangled light source, χ represents the conversion efficiency of the nonlinear optical crystal, P represents the laser pulse power, g represents the signal enhancement function, and the image optimization unit uses 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 the image quality, 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, where I fused Represents the fused image, I i (x,y) represents the image details of layer i, w i The machine learning and diagnosis unit, 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 multidimensional Raman spectral feature analysis computational model, combined with machine learning and spectral data processing algorithms, to construct a nonlinear mapping relationship between cancer cell number and spectral intensity, enabling accurate quantitative detection of tumor markers.

[0036] Implementation Method 2

[0037] In this embodiment, we further describe the data storage and automatic calibration functions of the device. The data storage unit is used to store all data generated during the detection process, including raw spectral data, processed image data, diagnostic results, and user information, which 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 a substance with known Raman spectral characteristics to ensure the accuracy and consistency of the Raman spectrometer. In addition, the remote monitoring and diagnosis unit is connected to the device through a network, which can remotely monitor the operating status of the device, receive detection data, and perform remote fault diagnosis and updates, thereby improving the reliability and maintainability of the device.

[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 operation and display interface for displaying test results, providing operational guidance, and accepting user input, enabling users to conveniently 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 diagnosis of the device. Experts can monitor and diagnose the device in real time from a remote location, ensuring stable operation and data accuracy. Furthermore, the remote monitoring and diagnostic unit can be remotely updated and optimized based on actual needs, enhancing the device's intelligence and adaptability.

[0040] Implementation Method 4

[0041] In this embodiment, we provide a specific application scenario. A hospital uses the Raman spectroscopy analysis device provided by the present invention to perform quantitative detection of blood tumor markers. First, a blood sample from a patient is collected using a blood collection centrifugal unit, and the centrifugal force and time parameters are automatically adjusted for separation. Next, a fiber optic Raman spectrometer is used to perform Raman spectroscopy on the separated plasma, and spectral data at multiple wavelengths is simultaneously captured and analyzed. Then, the spectral image is preprocessed using an image optimization unit to improve image quality. Finally, a machine learning and diagnosis unit and a cancer cell identification and quantification unit are used to perform feature learning and pattern recognition on the processed spectral data, construct a nonlinear mapping relationship between the number of cancer cells and spectral intensity, and achieve accurate quantitative detection of tumor markers. The test results show that the device has high sensitivity and accuracy, and can provide strong support for clinical diagnosis and treatment.

[0042] Implementation Method Five

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

[0044] Blood Collection and Centrifugation: Blood samples are collected from patients using a blood collection centrifuge unit with an adaptive load adjustment mechanism. This unit monitors blood density and composition in real time and automatically adjusts centrifugal 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 nonlinear optical crystal-enhanced Raman scattering signals to simultaneously capture and analyze spectral data at multiple wavelengths.

[0046] Image preprocessing: Using the image optimization unit, the Raman spectrum image is preprocessed using the adaptive noise suppression algorithm and the multi-scale image fusion algorithm to separate the target signal from the complex background noise, and the image quality is enhanced through the 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 diagnosis 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 a machine learning algorithm and a spectral data processing algorithm, to calculate the nonlinear mapping relationship between the number of cancer cells and the spectral intensity, thereby deriving the specific number of cancer cells.

[0049] Implementation Method 6

[0050] This embodiment provides a Raman spectroscopy analysis device for quantitative detection of blood tumor markers, which is characterized by further comprising a data storage unit and an automatic calibration unit:

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

[0052] Automatic Calibration: Before each measurement, the Raman spectrometer is calibrated using an automatic calibration unit. This unit includes a calibration light source that provides light with known spectral characteristics, and a calibration sample that provides a substance with known Raman spectral characteristics, ensuring the accuracy and consistency of spectral data.

[0053] Detection process: Blood collection, centrifugation, Raman spectrum acquisition, image preprocessing, machine learning and diagnosis, and cancer cell identification and quantification are performed according to the steps of specific implementation method one.

[0054] Implementation Method Seven

[0055] This embodiment provides a Raman spectroscopy analysis device for quantitative detection of blood tumor markers, which is characterized by also including a remote monitoring and diagnosis unit and a user interaction interface:

[0056] Remote Monitoring and Diagnosis: The remote monitoring and diagnosis unit connects to the device via the 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 instructions, and accepting user input. Users can easily understand test results and operation steps through this interface.

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

[0059] Implementation Method Eight

[0060] This embodiment provides a Raman spectroscopy analysis device for quantitative detection of blood tumor markers. Its features include the specific application of an adaptive load regulation mechanism, a quantum entangled Raman signal enhancement algorithm, an adaptive noise suppression algorithm, and a multi-scale image fusion algorithm.

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

[0062] Quantum entangled Raman signal enhancement algorithm: Using 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 through the signal enhancement function g(φ,χ,P) Raman , enhancing the intensity of Raman spectroscopy signals.

[0063] Adaptive noise suppression algorithm: Perform noise suppression on the original image I(x,y), and use the noise suppression function h(N(x,y)) to calculate the denoised image (x,y), effectively removing background noise.

[0064] Multi-scale image fusion algorithm: multi-layer image details Ii (x, y) is fused and the fused image Ifused is calculated using the fusion weight wi to improve the image resolution and clarity.

[0065] Detection process: Blood collection, centrifugation, Raman spectrum 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 regulation, Raman signal enhancement, noise suppression, and image fusion.

[0066] Implementation Method Nine

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

[0068] Patient information entry: First, enter the patient's basic information through the user interface, including but not limited to age, gender, medical history, etc. 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 centrifuge unit with an adaptive load regulation mechanism adjusts to the centrifugation parameters that best suit the patient's blood characteristics to ensure sample quality.

[0070] Spectral acquisition and analysis: After automatic wavelength calibration, the fiber Raman spectrometer performs targeted spectrum acquisition for the patient's specific tumor markers, using quantum entangled light sources and nonlinear optical crystals to optimize signal intensity.

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

[0072] Result output and interpretation: The diagnostic results are displayed through the user interface, and personalized explanations and suggestions for the patient are provided to help doctors develop more accurate treatment plans.

[0073] Implementation Method Eleven

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

[0075] Patient information entry: First, enter the patient's basic information through the user interface, including but not limited to age, gender, medical history, etc. 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 centrifuge unit with an adaptive load regulation mechanism adjusts to the centrifugation parameters that best suit the patient's blood characteristics to ensure sample quality.

[0077] Spectral acquisition and analysis: After automatic wavelength calibration, the fiber Raman spectrometer performs targeted spectrum acquisition for the patient's specific tumor markers, using quantum entangled light sources and nonlinear optical crystals to optimize signal intensity.

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

[0079] Result output and interpretation: The diagnostic results are displayed through the user interface, and personalized explanations and suggestions for the patient are provided to help doctors develop more accurate treatment plans.

[0080] Implementation Method Twelve

[0081] This implementation focuses on the continuous optimization and upgrade capabilities of the device:

[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 machine learning and model updates of the diagnostic unit.

[0083] Remote software upgrade: The remote monitoring and diagnostic unit is not only used to monitor the status of the equipment, but is also responsible for receiving and installing 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 test process, result interpretation, etc. through the user interaction interface to form a closed-loop feedback mechanism to continuously optimize user experience and test accuracy.

[0085] Implementation Method Thirteen

[0086] This embodiment highlights the device's multi-site deployment and collaborative working capabilities in large medical institutions or research centers:

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

[0088] Centralized management and analysis: The remote monitoring and diagnostic unit is responsible for centrally managing the equipment status and data flow of all sites, while providing advanced analytical 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 cooperation projects, and accelerates the discovery of new tumor markers and innovation of diagnostic methods.

[0090] Implementation Method 14

[0091] This embodiment emphasizes the device's ability to respond quickly in emergency situations:

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

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

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

[0095] Implementation Method 15

[0096] This embodiment 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 and evaluate drug efficacy and safety by accurately detecting changes in tumor markers in blood samples.

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

[0099] R&D data sharing: The device can share test data with drug R&D institutions, promote data exchange and analysis during the new drug R&D process, and accelerate the launch of new drugs.

[0100] Implementation Method 16

[0101] This implementation focuses on improving 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, which automatically triggers the alarm when an abnormality or potential danger is detected and notifies relevant personnel to handle it.

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

[0105] Implementation Method 17

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

[0107] Large-scale screening: The device can be used for large-scale population screening. By detecting tumor markers in blood samples, potential cancer patients can be detected early, thereby improving cancer prevention and treatment effectiveness.

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

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

[0110] Implementation Method 18

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

[0112] Patient education platform: Through the 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 plan: Based on the patient's test results and personal information, the device can develop a personalized health plan, including recommendations on diet, exercise, follow-up examinations, etc.

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

[0115] Implementation Method 19

[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 technology 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: Using Raman spectroscopy to detect protein characteristics in blood samples, combined with proteomics data, can further reveal the function and mechanism of action of tumor markers.

[0119] Metabolomics and spectroscopy cross-validation: The device can be combined with metabolomics technology to detect metabolite changes in blood samples and cross-validate with spectral data to improve the sensitivity and specificity of cancer detection.

[0120] Implementation Method 20

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

[0122] A blood collection centrifuge unit with an adaptive load regulation mechanism that monitors blood density and composition in real time and automatically adjusts centrifugal force and time parameters based on blood density and composition;

[0123] A fiber-optic Raman spectrometer equipped with an automatic wavelength calibration system, which integrates an ultrafast laser pulse transmitter and a highly sensitive photon counting detector. The fiber-optic Raman spectrometer is based on a quantum entangled light source and nonlinear optical crystal-enhanced Raman scattering signals, and is used to simultaneously capture and analyze spectral data at multiple wavelengths.

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

[0125] The Machine Learning and Diagnosis Unit is based on an advanced deep learning framework that combines deep reinforcement learning algorithms with multimodal learning mechanisms. It includes an STCN network for feature learning and pattern recognition. The Machine Learning and Diagnosis Unit also includes an adaptive decision support module for real-time updating and optimization of detection models based on clinical data and scientific research progress.

[0126] Cancer cell identification and quantification unit, based on multidimensional Raman spectroscopy feature analysis calculation model, combined with machine learning algorithm and spectral data processing algorithm, defines the spectral data matrix as ,in represents the spectrum sampling point, Represents the number of wavelength channels, normalizes the spectral data, and obtains the normalized matrix , calculate its mean square error weighted matrix , using the formula:

[0127] ;

[0128] Calculate spectral feature weighted index , construct a nonlinear mapping relationship between the number of cancer cells and spectral intensity;

[0129] in, Represents the first The sampling point is The spectral intensity of the wavelength channel, Represents the first in the normalized spectral data matrix The sampling point is The spectral intensity of the wavelength channel, Representative The mean of the normalized spectral data of the wavelength channels, Represents the total number of spectral sampling points, represents the total number of wavelength channels, Represents the normalized The sampling point is The weighted mean square error of the wavelength channels.

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

[0131] ;

[0132] in, and Respectively represent The minimum and maximum values ​​of the sampling points in all wavelength channels, the normalized data Between [0,1], ensure that different sample data are calculated in the same numerical range, and calculate the mean of the normalized data after normalization To measure the central tendency of each wavelength channel, the calculation formula is as follows:

[0133] ;

[0134] Perform mean square error weighting on the normalized data, calculate the degree of deviation of each spectral point relative to the mean, and use this to measure the dispersion of the data, and calculate the mean square error weighting matrix , using the formula:

[0135] ;

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

[0137] ;

[0138] By calculating the weighted deviation value of each wavelength channel , measure its fluctuation among samples and calculate the normalized weight distribution using the formula:

[0139] ;

[0140] Select wavelength channels with higher weights for feature extraction and set thresholds , filter to meet wavelength channels and establish the eigenvector matrix ,in It represents the filtered wavelength channel index, and finally a nonlinear mapping relationship between the number of cancer cells and spectral intensity is established based on the filtered eigenvector 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 the present invention, the adaptive load regulation 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 an adaptive regulation function.

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

[0146] In addition, in the present invention, regarding the above-mentioned adaptive noise suppression algorithm, the adaptive noise suppression algorithm 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] In addition, in the present invention, regarding the above-mentioned multi-scale image fusion algorithm, the multi-scale image fusion algorithm adopts the following formula: fused = w i ⋅I i (x,y), where I fused Represents the fused image, I i (x,y) represents the image details of layer i, w i represents the fusion weight.

[0148] In addition, in the present invention, regarding the above-mentioned STCN network: the loss function formula of the STCN network is as follows:

[0149] ;

[0150] Among them, L represents the loss function, Y true represents the true label, Y pred Represents the predicted label, θ represents the network parameter, and λ represents the regularization coefficient, which is used to prevent overfitting.

[0151] In addition, the device also includes a data storage unit for storing all data generated during the detection process, including original spectral data, processed image data, and the formula:

[0152] ;

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

[0154] in, represents the corrected spectral intensity, Representative The original spectral intensity of the sampling points, Represents the mean of the original spectral intensity of all sampling points, Represents the standard deviation of the original spectral intensity of all sampling points, Representative The weight coefficient of the sampling points, Represents the total number of sampling points, An index representing a single sampling point.

[0155] The data storage unit stores all the data generated during the detection process. First, the original spectral data is collected. The spectrometer is used to collect light intensity data at fixed time intervals. For example, within the wavelength range of 400nm to 700nm, sampling is performed at intervals of 10nm to obtain the original spectral data array. For this data, the spectrum mean and standard deviation need to be calculated to correct the noise fluctuation in the data. The mean is calculated first during the calculation process, that is,

[0156] ;

[0157] Then calculate the standard deviation

[0158] ;

[0159] Assumed number of sampling points , a set of data The mean of , the standard deviation is calculated , followed by normalization processing, the normalized spectral data is calculated as follows:

[0160] ;

[0161] At a certain sampling point Data For example, the normalized result is

[0162] ;

[0163] Then, the weight coefficient needs 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 the wavelength, the weight array is set using empirical values. , based on the normalized data and weights, the corrected spectral intensity is finally calculated:

[0164] ;

[0165] Assume that the calculation , which represents the corrected spectral characteristic intensity. The data is subsequently stored for further analysis. After the spectral data processing is completed, the system stores the data and combines it with the processed image data to record the pixel matrix with a resolution of 256x256. The recording method adopts matrix storage, that is,

[0166] ;

[0167] in Represents the image Row, No. Column pixel value, assuming a pixel data , then the point occupies the position in the stored data ,The processed spectrum and image data are stored in the storage unit for ,subsequent analysis., ,Finally, the diagnosis results and user information are ,synchronized 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 some of the spectral data calculation results. 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, Representative The original spectral intensity of the sampling points, Represents the mean of the original spectral intensity of all sampling points, Represents the standard deviation of the original spectral intensity of all sampling points, Representative The weight coefficient of the sampling points, Represents the total number of sampling points, Represents the index of a single sampling point, represents the normalized spectral intensity, Represents the pixel value of the processed image, Represents the stored image matrix.

[0172] In addition, 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 a substance with known Raman spectral characteristics for calibrating the Raman spectrometer before detection.

[0173] In addition, the device also includes a remote monitoring and diagnosis unit, which is connected to the device through a network and is used to remotely monitor the operating status of the device, receive detection data, and perform remote fault diagnosis and updates.

[0174] In addition, the device also includes a user interaction interface for displaying detection results, providing operation instructions, and receiving user input.

[0175] In addition, to achieve the above-mentioned objectives, an embodiment of the present invention further provides 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 an embodiment of the present invention.

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

[0177] The embodiments of the present invention are described with reference to flowcharts and / or block diagrams of methods, terminal devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts 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, a special-purpose computer, an embedded processor, or other programmable data processing terminal device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A system that specifies the functions of a box or boxes.

[0178] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing terminal device to operate in a specific manner, so that the instructions stored in the computer readable memory produce a product including an instruction system, which is implemented in the process Figure 1 a process or multiple 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 device so that a series of operating steps are executed on the computer or other programmable terminal device to produce a computer-implemented process, thereby providing instructions for executing on the computer or other programmable terminal device to implement the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0180] Finally, it should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply that there is any such actual relationship or order between these entities or operations. "" and / or "" indicate that either one or both of the two can be selected. Moreover, the terms "" include", "" contain", "" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or terminal device that includes a series of elements includes not only those elements, but also other elements that are not explicitly listed, or also includes elements that are inherent to such process, method, article or terminal device. In the absence of further restrictions, the elements defined by the sentence "" include a..."" does not exclude the presence of other identical elements in the process, method, article or terminal device that includes the elements.

[0181] Finally, it should be noted that the above embodiments are merely illustrative of 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 aforementioned embodiments, those skilled in the art should understand that they may still modify the technical solutions described in the aforementioned embodiments, or replace some of the technical features therein with equivalents; and such modifications or replacements do not deviate from the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention. Any changes or replacements that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be covered by the scope of protection of the present invention.

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

Claims

1. A Raman spectroscopy analysis device for quantitative detection of blood tumor markers, characterized by: include: A blood collection centrifuge unit with an adaptive load regulation mechanism that monitors blood density and composition in real time and automatically adjusts centrifugal force and time parameters based on blood density and composition; A fiber-optic Raman spectrometer equipped with an automatic wavelength calibration system, which integrates an ultrafast laser pulse transmitter and a high-sensitivity photon counting detector. The fiber-optic Raman spectrometer is based on a quantum entangled light source and nonlinear optical crystal-enhanced Raman scattering signals, and is used to synchronously capture and analyze spectral data at multiple wavelengths; The image optimization unit pre-processes the Raman spectrum image based on an adaptive noise suppression algorithm and a multi-scale image fusion algorithm to separate the target signal from the complex background noise, and enhances the image through a high-resolution image reconstruction algorithm; A machine learning and diagnostic unit, based on an advanced deep learning framework that combines deep reinforcement learning algorithms with multimodal learning mechanisms, includes an STCN network for feature learning and pattern recognition, and also includes an adaptive decision support module for real-time updating and optimization of detection models based on clinical data and scientific research progress; Cancer cell identification and quantification unit, based on multidimensional Raman spectroscopy feature analysis calculation model, combined with machine learning algorithm and spectral data processing algorithm, defines the spectral data matrix as ,in represents the spectrum sampling point, Represents the number of wavelength channels, normalizes the spectral data, and obtains the normalized matrix , calculate its mean square error weighted matrix , using the formula: ； Calculate spectral feature weighted index , construct a nonlinear mapping relationship between the number of cancer cells and spectral intensity; in, Represents the first The sampling point is The spectral intensity of the wavelength channel, Represents the first in the normalized spectral data matrix The sampling point is The spectral intensity of the wavelength channel, Representative The mean of the normalized spectral data of the wavelength channels, Represents the total number of spectral sampling points, represents the total number of wavelength channels, Represents the normalized The sampling point is The weighted mean square error of the wavelength channels.

2. The Raman spectroscopy analysis device for quantitative detection of blood tumor markers according to claim 1, characterized in that: The adaptive load regulation 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 an adaptive regulation function.

3. The Raman spectroscopy analysis device for quantitative detection of blood tumor markers according to claim 2, characterized in that: The quantum entangled Raman signal enhancement algorithm adopts the following formula: Raman =g(ϕ,χ,P), where I Raman represents the Raman scattering signal intensity, ϕ represents the phase of the quantum entangled light source, χ represents the conversion efficiency of the nonlinear optical crystal, P 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 adopts the following formula: fused = w i ⋅I i (x,y), where I fused represents the fused image, I i (x,y) represents the image details of layer i, w i represents 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 of the STCN network is as follows: ； Among them, L represents the loss function, Y true represents the true label, Y pred Represents the predicted label, θ represents the network parameter, and λ represents the regularization coefficient, which is used to prevent overfitting.

7. The 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 the formula: ； Calculate the corrected spectral intensity , diagnostic results and user information, etc., for subsequent analysis and tracing; in, represents the corrected spectral intensity, Representative The original spectral intensity of the sampling points, Represents the mean of the original spectral intensity of all sampling points, Represents the standard deviation of the original spectral intensity of all sampling points, Representative The weight coefficient of the sampling points, Represents the total number of sampling points, An index representing a single sample 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 a substance with known Raman spectral characteristics for calibrating the Raman spectrometer before detection.

9. The 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 diagnosis unit, which is connected to the device through a network and is used to remotely monitor the operating status of the device, receive detection data, and perform remote fault diagnosis and updates.

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

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