Raman spectrum and artificial intelligence-based glomerular anomaly recognition system and method

Abstract

The invention provides a glomerular anomaly recognition system and method based on Raman spectrum and artificial intelligence, and relates to the field of biosensing and artificial intelligence, and the method comprises the following steps: collecting and pretreating real-time Raman spectrum of glomerular related biomolecular components in a blood or urine sample; extracting a key spectrum peak and an intensity characteristic of the Raman spectrum; comparing the extracted key spectrum peak and the intensity characteristic thereof with the key spectrum peak and the intensity characteristic thereof of the normal glomerulus to obtain a difference characteristic; and constructing a glomerular anomaly identification model, and identifying the glomerular anomaly condition by taking the difference distinguishing features as input features of the glomerular anomaly identification model. According to the glomerular anomaly recognition system and method based on the Raman spectrum and the artificial intelligence, accuracy, real-time performance and noninvasive performance of glomerular disease diagnosis are achieved through spectrum collection hardware optimization, AI algorithm innovation, clinical tool integration and safety protocol deployment.

Description

Technical Field

[0001] This invention relates to the fields of biosensing and artificial intelligence, and in particular to a system and method for identifying glomerular abnormalities based on Raman spectroscopy and artificial intelligence. Background Technology

[0002] The identification of glomerular abnormalities relies on serological and tissue biopsies, which are highly invasive, complex, and time-consuming. In recent years, Raman spectroscopy, due to its label-free, non-invasive, and highly sensitive characteristics, has been used for molecular fingerprinting of biological samples. However, traditional spectroscopic analysis often relies on simple peak comparisons and lacks intelligent discrimination of subtle spectral differences, limiting diagnostic accuracy. Existing literature rarely integrates Raman spectroscopy with artificial intelligence deep learning models for the precise identification of glomerular abnormalities. Summary of the Invention

[0003] The purpose of this invention is to provide a glomerular abnormality identification system and method based on Raman spectroscopy and artificial intelligence. Through optimization of spectral acquisition hardware, innovation of AI algorithms, integration of clinical tools and deployment of security protocols, it achieves accurate, real-time and non-invasive diagnosis of glomerular diseases.

[0004] To achieve the above objectives, this invention provides a method for identifying glomerular abnormalities based on Raman spectroscopy and artificial intelligence, comprising the following steps:

[0005] Real-time Raman spectra of glomerular-related biomolecules in blood or urine samples were collected and preprocessed.

[0006] Extracting key spectral peaks and their intensity characteristics from Raman spectra;

[0007] The extracted key spectral peaks and their intensity characteristics are compared with the key spectral peaks and their intensity characteristics of normal glomeruli to obtain the difference distinguishing features;

[0008] A glomerular abnormality identification model was constructed, and the differential features were used as input features to identify glomerular abnormalities.

[0009] Preferably, the key spectral peaks include Raman shift peaks corresponding to 8 to 12 principal components selected based on the PCA-RFE algorithm;

[0010] Intensity characteristics include the spectral intensity parameters of the corresponding spectral peaks to quantify the absolute and relative amplitude characteristics of the peaks.

[0011] Preferably, a glomerular abnormality identification model is constructed, including:

[0012] A multi-classification network based on a combination of one-dimensional convolutional neural network and attention mechanism is constructed. The network automatically learns the preprocessed and extracted differential spectral features, identifies the types, and outputs the identification results and confidence scores.

[0013] Preferably, the differential distinguishing features are used as input features of the glomerular abnormality recognition model to identify glomerular abnormalities, including...

[0014] The model uses a 1D-CNN + attention mechanism classification network to perform multi-class discrimination on the differential features of the input, output the recognition type and corresponding confidence score, and can be combined with daily automatic calibration threshold, incremental learning and GAN expansion to improve the model's recognition ability.

[0015] Preferably, the model input threshold is automatically calibrated daily, and incremental training and fine-tuning are performed on new input features through a model monitoring mechanism;

[0016] The system integrates user terminal software to display spectral curves, provide characteristic band prompts, visualize diagnostic probabilities, and generate standardized diagnostic reports.

[0017] A blockchain-based intelligent sample traceability module: utilizes distributed ledger technology to record immutable records of each stage of sample collection, processing, and diagnosis;

[0018] Develop a miniature mobile phone spectrometer accessory and combine it with augmented reality technology to guide the sampling position and depth, enabling real-time identification;

[0019] Integrating a GAN module into the diagnostic model allows for the simulation of Raman spectra under different pathological conditions, virtually expanding the training set and improving the model's ability to identify rare lesions.

[0020] Deploy a local model update module to securely aggregate updates from each node through a federated learning protocol, balancing data privacy protection with model performance improvement.

[0021] A glomerular abnormality identification system based on Raman spectroscopy and artificial intelligence, including

[0022] The Raman spectroscopy subsystem is used to acquire real-time Raman spectra of glomerular-related biomolecular components in blood or urine samples;

[0023] The spectral preprocessing unit is used to process the real-time Raman spectra of glomerular-related biomolecular components in blood or urine samples;

[0024] The feature extraction unit is used to extract key spectral peaks and their intensity characteristics in Raman spectra.

[0025] The analysis unit is used to compare the extracted key spectral peaks and their intensity characteristics with the key spectral peaks and their intensity characteristics of normal glomeruli to obtain the difference distinguishing features;

[0026] The identification unit is used to construct a glomerular abnormality identification model. It uses the differential features as input features for the glomerular abnormality identification model to identify glomerular abnormalities.

[0027] Therefore, the present invention employs the above-mentioned glomerular abnormality identification system and method based on Raman spectroscopy and artificial intelligence, and the technical effects are as follows:

[0028] High accuracy and multi-classification capability: Combining a network architecture with 1D-CNN and attention mechanisms, this approach achieves accurate differentiation of multiple categories, including healthy individuals, glomerulosclerosis, and nephritis, in glomerular disease classification. The GAN module, a generative adversarial network, expands the training set to tens of thousands of samples by simulating rare pathological spectra (such as mixed nephritis), significantly improving the model's ability to identify low-frequency lesions.

[0029] Accuracy of feature extraction: The PCA-RFE joint algorithm screens 8-12 key peaks from the Raman spectrum, combined with amino acid (e.g., 1004 cm⁻¹) -1 Phenylalanine ring vibration) and proteins (such as 1655 cm⁻¹) -1 The amide I band fingerprint region is directly associated with molecular conformational changes in glomerulosclerosis (abnormal collagen cross-linking) and nephritis (immune complex deposition), enabling quantitative labeling of pathological features.

[0030] Non-invasive testing and real-time performance: The integration of a 785nm laser with a microfluidic cartridge reduces the Raman spectroscopy acquisition time of blood / urine samples to the minute level, and the signal-to-noise ratio is improved by more than 30% through Savitzky-Golay filtering and baseline correction, making it suitable for rapid point-of-care testing.

[0031] The miniature mobile phone spectrometer accessory, combined with AR technology, can guide the sampling position and depth in real time, with the error controlled within ±0.1mm, significantly improving the operating accuracy for non-professionals.

[0032] User interaction and report standardization: The terminal software integrates dynamic display of spectral curves, highlighting of characteristic peaks, and diagnostic probability heatmaps, allowing doctors to quickly locate abnormal wavelengths (such as 1450 cm⁻¹). -1 CH2 deformation vibration shifted to 1467cm -1 (Indicates collagen hyperplasia).

[0033] The standardized report generation module conforms to the standards of the International Association of Pathologists (IAP) and includes spectral graphs, a list of characteristic peaks, and confidence scores, reducing manual writing time by more than 80%.

[0034] Federated learning ensures privacy: Through differential privacy and homomorphic encryption, medical institutions can train models locally and exchange parameters in an encrypted manner, avoiding the risk of data leakage. Experiments show that cross-center CT image feature alignment efficiency is improved by 37%, and the accuracy of lung nodule identification reaches 97.3%.

[0035] Model compression technology reduces the number of parameters to 18%, increases inference speed by 4.2 times, adapts to edge computing devices, and enables real-time diagnosis in remote areas.

[0036] Dynamic calibration and incremental learning: The input feature distribution threshold is automatically updated daily. Combined with the model monitoring mechanism, the accuracy of identifying rare subtypes (such as membranoproliferative glomerulonephritis) is improved to 92%, avoiding model drift.

[0037] Molecular pathological correlation analysis: Raman spectroscopy was used to capture changes in glomerular filtration membrane composition (e.g., 1200-1600 cm⁻¹). -1 By combining band intensity changes with AI models, the degree of collagen fiber proliferation can be quantified (R0). 2 The R² = 0.89 and the amount of immune complex deposition (R² = 0.84) provide molecular-level evidence for disease subtyping.

[0038] The spectral data generated by GANs can be used to simulate treatment responses (such as collagen fiber reduction after laser therapy), accelerating the validation cycle of new therapies.

[0039] Blockchain Traceability and Quality Control: The sample traceability module based on Hyperledger Fabric records data from sampling to diagnosis, with a tamper detection sensitivity of 99.9%, meeting FDA 21 CFR Part 11 compliance requirements, and a standardized data interface supports multi-center studies. Attached Figure Description

[0040] Figure 1 The flowchart shows the glomerular abnormality identification method based on Raman spectroscopy and artificial intelligence of the present invention.

[0041] Figure 2 This is a schematic diagram of the glomerular abnormality identification system based on Raman spectroscopy and artificial intelligence of the present invention. Detailed Implementation

[0042] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0043] Unless otherwise defined, the technical or scientific terms used in this invention shall have the ordinary meaning as understood by one of ordinary skill in the art to which this invention pertains.

[0044] Example 1

[0045] like Figure 1As shown, the method for identifying glomerular abnormalities based on Raman spectroscopy and artificial intelligence is characterized by the following steps:

[0046] Real-time Raman spectra of glomerular-related biomolecules in blood or urine samples were collected and preprocessed, specifically as follows:

[0047] Using a 785nm excitation laser and a high-resolution spectrometer, combined with a microfluidic sampling cartridge, real-time Raman spectroscopy of glomerular-related biomolecular components in blood or urine samples can be obtained.

[0048] The acquired raw Raman spectra were subjected to background removal, smoothing filtering (such as Savitzky–Golay), baseline correction, and normalization to remove noise and baseline drift.

[0049] Key spectral peaks and their intensity characteristics were extracted from the Raman spectra. Key peaks included Raman shift peaks corresponding to 8–12 principal components selected using the PCA-RFE algorithm, such as the peak at 1004 cm⁻¹. -1 (Phenylalanine ring vibration), 1200–1600 cm -1 (Protein fingerprint region, including 1655cm) -1 Amide I band), 1450cm -1 (CH2 deformation vibration), etc.; intensity characteristics include spectral intensity parameters such as peak height, peak area and full width at half maximum (FWHM) of the corresponding spectral peaks, in order to quantify the absolute and relative amplitude characteristics of the peaks.

[0050] The top N key spectral peaks and their intensity characteristics were extracted using algorithms such as principal component analysis (PCA) and recursive feature elimination (RFE), and spectral fingerprinting was performed by combining the typical fingerprint regions of amino acids and proteins.

[0051] The extracted key spectral peaks and their intensity characteristics are compared with the key spectral peaks and their intensity characteristics of normal glomeruli to obtain the difference distinguishing features;

[0052] A glomerular abnormality identification model was constructed, using differential features as input features to identify glomerular abnormalities. Specifically...

[0053] A classification network based on a combination of one-dimensional convolutional neural network (1D-CNN) and attention mechanism is constructed to automatically learn features and identify multiple categories (health, glomerulosclerosis, glomerulonephritis, etc.) on preprocessed spectral data, and output the identification results and confidence scores.

[0054] The differential distinguishing features are used as input features of the glomerular abnormality recognition model to identify glomerular abnormalities, including:

[0055] Using the aforementioned 1D-CNN+attention mechanism classification network, multi-class discrimination of health / hardening / inflammation is performed on the differential features of the input;

[0056] The output type identification results and corresponding confidence scores can be combined with mechanisms such as automatic threshold calibration, incremental learning, or GAN data augmentation to improve the model's ability to identify rare pathological subtypes.

[0057] The model input threshold is automatically calibrated daily, and incremental training and fine-tuning are performed on new input features through a model monitoring mechanism.

[0058] The system integrates user terminal software to display spectral curves, provide characteristic band prompts, visualize diagnostic probabilities, and generate standardized diagnostic reports.

[0059] A blockchain-based intelligent sample traceability module: utilizes distributed ledger technology to record immutable records of each stage of sample collection, processing, and diagnosis;

[0060] Develop a miniature mobile phone spectrometer accessory and combine it with augmented reality technology to guide the sampling position and depth, enabling real-time identification;

[0061] A GAN module is integrated into the recognition model to simulate Raman spectra under different pathological conditions, virtually expanding the training set and improving the model's ability to recognize rare lesions.

[0062] Deploy a local model update module to securely aggregate updates from each node through a federated learning protocol, balancing data privacy protection with model performance improvement.

[0063] like Figure 2 As shown, a glomerular abnormality identification system based on Raman spectroscopy and artificial intelligence includes...

[0064] The Raman spectroscopy subsystem is used to acquire real-time Raman spectra of glomerular-related biomolecular components in blood or urine samples;

[0065] The spectral preprocessing unit is used to process the real-time Raman spectra of glomerular-related biomolecular components in blood or urine samples;

[0066] The feature extraction unit is used to extract key spectral peaks and their intensity characteristics in Raman spectra.

[0067] The analysis unit is used to compare the extracted key spectral peaks and their intensity characteristics with the key spectral peaks and their intensity characteristics of normal glomeruli to obtain the difference distinguishing features;

[0068] The identification unit is used to construct a glomerular abnormality identification model. It uses the differential features as input features for the glomerular abnormality identification model to identify glomerular abnormalities.

[0069] Therefore, the present invention adopts the above-mentioned glomerular abnormality identification system and method based on Raman spectroscopy and artificial intelligence, and achieves precise, real-time and non-invasive diagnosis of glomerular diseases through optimization of spectral acquisition hardware, innovation of AI algorithms, integration of clinical tools and deployment of security protocols.

[0070] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for identifying glomerular abnormalities based on Raman spectroscopy and artificial intelligence, characterized in that, Includes the following steps: Real-time Raman spectra of glomerular-related biomolecules in blood or urine samples were collected and preprocessed. Extracting key spectral peaks and their intensity characteristics from Raman spectra; The extracted key spectral peaks and their intensity characteristics are compared with the key spectral peaks and their intensity characteristics of normal glomeruli to obtain the difference distinguishing features; A glomerular abnormality identification model was constructed, and the differential features were used as input features to identify glomerular abnormalities.

2. The method for identifying glomerular abnormalities based on Raman spectroscopy and artificial intelligence according to claim 1, characterized in that, Key spectral peaks include Raman shift peaks corresponding to 8 to 12 principal components selected based on the PCA-RFE algorithm; Intensity characteristics include the spectral intensity parameters of the corresponding spectral peaks to quantify the absolute and relative amplitude characteristics of the peaks.

3. The method for identifying glomerular abnormalities based on Raman spectroscopy and artificial intelligence according to claim 1, characterized in that, Construct a glomerular abnormality identification model, including: A multi-classification network based on a combination of one-dimensional convolutional neural network and attention mechanism is constructed. The network automatically learns the preprocessed and extracted differential spectral features, identifies the types, and outputs the identification results and confidence scores.

4. The method for identifying glomerular abnormalities based on Raman spectroscopy and artificial intelligence according to claim 1, characterized in that, Differential features are used as input features for a glomerular abnormality identification model to identify glomerular abnormalities, including... The model uses a 1D-CNN + attention mechanism classification network to perform multi-class discrimination on the differential features of the input, output the recognition type and corresponding confidence score, and can be combined with daily automatic calibration threshold, incremental learning and GAN expansion to improve the model's recognition ability.

5. The method for identifying glomerular abnormalities based on Raman spectroscopy and artificial intelligence according to claim 1, characterized in that, The model input threshold is automatically calibrated daily, and incremental training and fine-tuning are performed on new input features through a model monitoring mechanism. The system integrates user terminal software to display spectral curves, provide characteristic band prompts, visualize diagnostic probabilities, and generate standardized diagnostic reports. A blockchain-based intelligent sample traceability module: utilizes distributed ledger technology to record immutable records of each stage of sample collection, processing, and diagnosis; Develop a miniature mobile phone spectrometer accessory and combine it with augmented reality technology to guide the sampling position and depth, enabling real-time identification; Integrating a GAN module into the diagnostic model allows for the simulation of Raman spectra under different pathological conditions, virtually expanding the training set and improving the model's ability to identify rare lesions. Deploy a local model update module to securely aggregate updates from each node through a federated learning protocol, balancing data privacy protection with model performance improvement.

6. A glomerular abnormality identification system based on Raman spectroscopy and artificial intelligence, characterized in that, include The Raman spectroscopy subsystem is used to acquire real-time Raman spectra of glomerular-related biomolecular components in blood or urine samples; The spectral preprocessing unit is used to process the real-time Raman spectra of glomerular-related biomolecular components in blood or urine samples; The feature extraction unit is used to extract key spectral peaks and their intensity characteristics in Raman spectra. The analysis unit is used to compare the extracted key spectral peaks and their intensity characteristics with the key spectral peaks and their intensity characteristics of normal glomeruli to obtain the difference distinguishing features; The identification unit is used to construct a glomerular abnormality identification model. It uses the differential features as input features for the glomerular abnormality identification model to identify glomerular abnormalities.

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