Chronic renal function non-invasive evaluation method and device based on near infrared spectrum technology

Abstract

The invention discloses a chronic renal function non-invasive assessment method and device based on a near infrared spectrum technology, and the method comprises the steps: obtaining a to-be-assessed human face near infrared image, and carrying out the preprocessing of the human face near infrared image, and obtaining a blood component spectrogram; inputting the blood component spectrogram into a discrimination model to output a specified biomarker content; determining glomerular filtering numerical value information based on the preprocessed information and the corresponding attribute information of the user; and inputting the glomerular filtering numerical value information into an evaluation model to output chronic renal insufficiency staging information. The purpose of evaluating the renal function in a convenient, sensitive, accurate, non-contact and non-invasive mode is achieved through the near infrared spectrum technology.

Description

Technical Field

[0001] This invention relates to the field of information processing technology, specifically to a non-invasive assessment method for chronic renal insufficiency based on near-infrared spectroscopy. Background Technology

[0002] In related technologies, chronic renal insufficiency refers to the impairment of glomerular filtration function due to various etiologies, also known as chronic renal failure, and is a common clinical syndrome of kidney disease. Causes of chronic renal insufficiency include chronic nephritis, hypertensive nephropathy, polycystic kidney disease, hyperuricemic nephropathy, etc.

[0003] Currently, chronic renal insufficiency is generally assessed and its stage is determined by blood tests. However, these methods are not suitable for long-term, regular self-monitoring, have limitations, and are inefficient. Therefore, there is an urgent need for a simple, sensitive, accurate, non-contact, and non-invasive testing method to overcome these problems. Summary of the Invention

[0004] The main objective of this invention is to provide a non-invasive assessment method and device for chronic kidney function based on near-infrared spectroscopy technology, in order to address the shortcomings of related technologies.

[0005] To achieve the above objectives, according to a first aspect of the present invention, a non-invasive assessment method for chronic renal function based on near-infrared spectroscopy is provided, characterized by comprising: acquiring a near-infrared image of a face to be assessed, and preprocessing the near-infrared image of the face to obtain a blood component spectrum; inputting the blood component spectrum into a discrimination model to output the content of a specified biomarker; determining glomerular filtration rate information based on the preprocessed information and the corresponding user's own attribute information; and inputting the glomerular filtration rate information into an assessment model to output chronic renal insufficiency stage information.

[0006] Optionally, acquiring a near-infrared image of the face to be evaluated and preprocessing the near-infrared image to obtain a blood component spectrum includes: illuminating the face with a near-infrared camera, and then using a beam splitter to receive photoelectric pulse waves corresponding to the emitted light of each wavelength; a photoelectric converter and an analog-to-digital converter converting the photoelectric pulse waves to obtain data on the varying emitted light intensity; calculating the absorbance data of each component in the blood based on the varying emitted light intensity data; obtaining spectral samples of different blood components based on the absorbance data; and processing the spectral samples to obtain a blood component spectrum.

[0007] Optionally, processing the spectral samples to obtain a blood component spectrum includes: determining the discrete points of the spectral samples using Euclidean distance and removing invalid spectral samples; removing interference noise from the spectral samples using wavelet transform denoising; removing the spectral baseline of the spectral samples using one or more of the following methods: peak-valley leveling, offset subtraction, differential processing, and baseline tilting; and normalizing the preprocessed spectral samples to obtain a preprocessed blood component spectrum.

[0008] Optionally, inputting the glomerular filtration rate information into the evaluation model to output chronic renal insufficiency staging information includes: the evaluation model matching similar glomerular filtration rate values ​​from the database based on the glomerular filtration rate information; and determining the corresponding chronic renal insufficiency staging information based on the similar glomerular filtration rate values.

[0009] Optionally, the user's own attribute information includes gender and age, wherein the following formula is used to determine the glomerular filtration rate: eGFR=170×(Scr)-0.999×(age)-0.176×(BUN)-0.170×(ALB)0.318×(constant corresponding to gender).

[0010] Optionally, the database includes glomerular filtration rate information, staging, and mapping relationships between renal function levels.

[0011] Optionally, the mapping relationship includes: a mapping relationship between eGFR of 90-120 ml / min, stage 1, and normal renal function; a mapping relationship between eGFR of 60-29 ml / min, stage 1, and mild renal impairment; a mapping relationship between eGFR of 30-59 ml / min, stage 3, and significantly impaired renal function; a mapping relationship between eGFR of 15-29 ml / min, stage 4, and severely impaired renal function; and a mapping relationship between eGFR <15 ml / min, stage 5, and severely impaired renal function.

[0012] According to a second aspect of the present invention, a non-invasive assessment device for chronic renal function based on near-infrared spectroscopy is provided, comprising a preprocessing unit for acquiring a near-infrared image of a face to be assessed and preprocessing the near-infrared image of the face to obtain a blood component spectrum; a calculation unit for determining glomerular filtration rate information based on the preprocessed information and corresponding user attribute information; and an assessment unit for inputting the glomerular filtration rate information into an assessment model to output chronic renal insufficiency stage information.

[0013] According to a third aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing the computer to perform the method described in any one of the first aspects.

[0014] This embodiment presents a non-invasive assessment method and device for chronic kidney function based on near-infrared spectroscopy. The method includes acquiring a near-infrared image of the face to be assessed and preprocessing the image to obtain a blood component spectrum; inputting the blood component spectrum into a discrimination model to output the content of a specified biomarker; determining glomerular filtration rate (GFR) values ​​based on the preprocessed information and corresponding user attribute information; and inputting the GFR values ​​into the assessment model to output chronic kidney failure staging information. Near-infrared spectroscopy technology achieves the goal of assessing kidney function in a convenient, sensitive, accurate, non-contact, and non-invasive manner. Attached Figure Description

[0015] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0016] Figure 1 This is a flowchart of a non-invasive assessment method for chronic kidney function based on near-infrared spectroscopy according to an embodiment of the present invention;

[0017] Figure 2 This is a schematic diagram of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0018] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. 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 should fall within the scope of protection of the present invention.

[0019] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate for the embodiments of the invention described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0020] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0021] According to embodiments of the present invention, a non-invasive assessment method for chronic renal function based on near-infrared spectroscopy is provided, such as... Figure 1 As shown, steps 101 to 103 are included below:

[0022] Step 101: Acquire a near-infrared image of the face to be evaluated, and preprocess the near-infrared image of the face to obtain a blood component spectrum;

[0023] In this step, the blood component spectral data is input into the discriminant model to obtain the corresponding levels of biomarkers such as blood urea nitrogen, creatinine, and plasma albumin in the blood of the tested patient. The method for constructing a discriminant model using known blood component content data and their corresponding blood spectral data includes: acquiring the blood component content data and their corresponding blood spectral data; preprocessing the blood spectral data using a chemometrics algorithm; and constructing the discriminant model using principal component analysis based on the blood component content data and the preprocessed blood spectral data. The blood component spectral data is then input into the discriminant model to obtain the corresponding levels of biomarkers such as blood urea nitrogen, creatinine, and plasma albumin in the blood of the tested patient.

[0024] As an optional implementation of this embodiment, acquiring a near-infrared image of the face to be evaluated and preprocessing the near-infrared image to obtain a blood component spectrum includes: illuminating the patient's face with a near-infrared camera, and then using a beam splitter to receive photoelectric pulse waves corresponding to the emitted light of each wavelength; a photoelectric converter and an analog-to-digital converter converting the photoelectric pulse waves to obtain data on the varying emitted light intensity; calculating the absorbance data of each component in the blood based on the varying emitted light intensity data; obtaining spectral samples of different blood components based on the absorbance data; and processing the spectral samples to obtain a blood component spectrum.

[0025] In this optional implementation, a near-infrared camera illuminates the face; a beam splitter receives the photoelectric pulse waves corresponding to the emitted light of each wavelength; a photoelectric converter and an analog-to-digital converter perform signal conversion on the photoelectric pulse waves to obtain data on the changing emitted light intensity; based on the data on the changing emitted light intensity, absorbance data of each component in the blood is calculated, including absorbance data of biomarkers such as blood urea nitrogen, creatinine, and plasma albumin in the blood of the tested patient; and spectral samples of different blood components are obtained based on the absorbance data.

[0026] Furthermore, the method for preprocessing the spectral samples to obtain the blood component spectrum includes: determining the discrete points of the spectral samples using Euclidean distance and removing invalid spectral samples; removing interference noise from the spectral samples using wavelet transform denoising; removing the spectral baseline of the spectral samples using one or more of the following methods: peak-valley leveling, offset subtraction, differential processing, and baseline tilting; and normalizing the preprocessed spectral samples to obtain the preprocessed blood component spectrum.

[0027] Step 102: Determine the glomerular filtration value based on the preprocessed information and the corresponding user's own attribute information.

[0028] As an optional implementation of this embodiment, the user's own attribute information includes gender and age. The following formula is used to determine the glomerular filtration rate information: eGFR=170×(Scr)-0.999×(age)-0.176×(BUN)-0.170×(ALB)0.318×(constant corresponding to gender).

[0029] In this optional implementation, the method for obtaining the glomerular filtration rate (eGFR) numerical information of patients with chronic renal insufficiency according to the formula for calculating glomerular filtration rate in patients with chronic renal insufficiency is as follows:

[0030] eGFR = 170 × (Scr) - 0.999 × (age) - 0.176 × (BUN) - 0.170 × (ALB) 0.318 × (0.742 female). Where eGFR is the estimated glomerular filtration rate (ml / min / 1.73m²), Scr is serum creatinine (mg / dl), BUN is blood urea nitrogen (mg / dl), and ALB is plasma albumin (g / dl).

[0031] Step 103: Input the glomerular filtration value information into the evaluation model to output the staging information of chronic renal insufficiency.

[0032] In this optional implementation, the glomerular filtration value information is input into the evaluation model to output the staging information of chronic renal insufficiency.

[0033] As an optional implementation of this embodiment, inputting the glomerular filtration rate information into the evaluation model to output chronic renal insufficiency staging information includes: the evaluation model matching similar glomerular filtration rate values ​​from the database based on the glomerular filtration rate information; and determining the corresponding chronic renal insufficiency staging information based on the similar glomerular filtration rate values.

[0034] As an optional implementation of this embodiment, the database includes glomerular filtration rate information, staging, and the mapping relationship between renal function levels.

[0035] In the above-mentioned optional implementation methods, a spectral database is constructed based on the blood component spectrum; the spectral database is constructed with an ideal value sub-database and sub-databases for excessively low and excessively high values, based on the control standards for blood urea nitrogen, creatinine, and plasma albumin content.

[0036] As an optional implementation of this embodiment, the mapping relationship includes:

[0037] The mapping relationship between eGFR 90-120 ml / min, stage 1, and normal renal function; the mapping relationship between eGFR 60-29 ml / min, stage 1, and mild renal impairment; the mapping relationship between eGFR 30-59 ml / min, stage 3, and significantly impaired renal function; the mapping relationship between eGFR 15-29 ml / min, stage 4, and severely impaired renal function; and the mapping relationship between eGFR <15 ml / min, stage 5, and severely impaired renal function.

[0038] Among the above-mentioned optional implementation methods, chronic renal insufficiency is staged into 5 stages, mainly by estimating the glomerular filtration rate (eGFR). The specific stages are as follows: Stage 1: eGFR 90-120 ml / min, representing normal renal function; Stage 2: eGFR 60-29 ml / min, indicating mild renal impairment; Stage 3: eGFR 30-59 ml / min, indicating significant renal impairment; Stage 3 is further divided into 3a and 3b. Stage 3a has an eGFR of 45-59 ml / min, and Stage 3b has an eGFR of 30-44 ml / min. Stage 3b, in particular, indicates moderate renal impairment, which is prone to progression to chronic renal failure and uremia; Stage 4: eGFR 15-29 ml / min, indicating severe renal impairment; Stage 5: eGFR < 15 ml / min, indicating severely impaired renal function, reaching end-stage renal disease, requiring preparation for or initiation of renal replacement therapy.

[0039] The similarity model is used to calculate the similarity between the standard vector matching assessment model of the patient's glomerular filtration rate (eGFR) and the glomerular filtration rate values ​​of the patients with chronic renal insufficiency to be matched. A corresponding similarity range is set, and then the calculated similarity is compared with the similarity range to generate a corresponding matching report. For example, if a patient's eGFR is 90-120 ml / min, it generally indicates normal renal function, classifying it as stage 1.

[0040] This embodiment presents a method and system for assessing chronic renal insufficiency based on near-infrared spectroscopy. Utilizing near-infrared dynamic spectroscopy, it scans facial and spectral samples of patients with chronic renal insufficiency to obtain the levels of biomarkers such as blood urea nitrogen, creatinine, and plasma albumin in the patients' blood. Then, based on the formula for calculating the glomerular filtration rate (GFR) of patients with chronic renal insufficiency, it obtains the patients' GFR values. Through similarity assessment and matching models, it achieves the staging assessment of renal function status.

[0041] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0042] According to an embodiment of the present invention, a non-invasive assessment device for chronic renal function based on near-infrared spectroscopy is also provided, comprising a preprocessing unit for acquiring a near-infrared image of the face to be assessed and preprocessing the near-infrared image of the face to obtain a blood component spectrum; a calculation unit for determining glomerular filtration rate information based on the preprocessed information and the corresponding user's own attribute information; and an assessment unit for inputting the glomerular filtration rate information into an assessment model to output chronic renal insufficiency stage information.

[0043] As an optional implementation of this embodiment, after a near-infrared camera illuminates a face, a beam splitter receives the photoelectric pulse waves corresponding to the emitted light of each wavelength; a photoelectric converter and an analog-to-digital converter perform signal conversion on the photoelectric pulse waves to obtain data on the changing emitted light intensity; absorbance data of each component in the blood are calculated based on the data on the changing emitted light intensity; spectral samples of different blood components are obtained based on the absorbance data; and the spectral samples are processed to obtain a blood component spectrum.

[0044] As an optional implementation of this embodiment, processing the spectral sample to obtain a blood component spectrum includes: determining the discrete points of the spectral sample using Euclidean distance and removing invalid spectral samples; removing interference noise from the spectral sample using wavelet transform denoising; removing the spectral baseline of the spectral sample using one or more of the following methods: peak-valley leveling, offset subtraction, differential processing, and baseline tilting; and normalizing the preprocessed spectral sample to obtain a preprocessed blood component spectrum.

[0045] As an optional implementation of this embodiment, inputting the glomerular filtration rate information into the evaluation model to output chronic renal insufficiency staging information includes: the evaluation model matching similar glomerular filtration rate values ​​from the database based on the glomerular filtration rate information; and determining the corresponding chronic renal insufficiency staging information based on the similar glomerular filtration rate values.

[0046] As an optional implementation of this embodiment, the user's own attribute information includes gender and age. The glomerular filtration rate information determined based on the preprocessed information and the corresponding user's own attribute information includes: eGFR = 170 × (Scr) - 0.999 × (age) - 0.176 × (BUN) - 0.170 × (ALB) 0.318 × (constant corresponding to gender).

[0047] As an optional implementation of this embodiment, the database includes glomerular filtration rate information, staging, and the mapping relationship between renal function levels.

[0048] As an optional implementation of this embodiment, the mapping relationship includes: a mapping relationship between eGFR of 90-120 ml / min, stage 1, and normal renal function; a mapping relationship between glomerular filtration rate of 60-29 ml / min, stage 1, and mild renal impairment; a mapping relationship between eGFR of 30-59 ml / min, stage 3, and significantly impaired renal function; a mapping relationship between eGFR of 15-29 ml / min, stage 4, and severely impaired renal function; and a mapping relationship between eGFR <15 ml / min, stage 5, and severely impaired renal function.

[0049] According to embodiments of the present invention, the present invention also provides an electronic device, the electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to implement the methods described in any of the above embodiments.

[0050] According to embodiments of the present invention, the present invention also provides a readable storage medium storing computer instructions that enable a computer to perform the methods described in any of the above embodiments when executed.

[0051] According to embodiments of the present invention, the present invention also provides a computer program product that, when executed by a processor, can implement the methods described in any of the above embodiments.

[0052] Figure 2A schematic block diagram of an example electronic device 300 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices.

[0053] like Figure 2 As shown, the electronic device 300 includes a computing unit 301, which can perform various appropriate actions and processes based on a computer program stored in a read-only memory (ROM) 302 or a computer program loaded from a storage unit 308 into a random access memory (RAM) 303. The RAM 303 may also store various programs and data required for the operation of the electronic device 300. The computing unit 301, ROM 302, and RAM 303 are interconnected via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0054] Multiple components in electronic device 300 are connected to I / O interface 305, including: input unit 306, such as keyboard, mouse, etc.; output unit 307, such as various types of displays, speakers, etc.; storage unit 308, such as disk, optical disk, etc.; and communication unit 309, such as network card, modem, wireless transceiver, etc. Communication unit 309 allows electronic device 300 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0055] The computing unit 301 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 301 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 301 performs the various methods and processes described above, such as the object matching method. For example, in some embodiments, the object matching method may be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 308. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 300 via ROM 302 and / or communication unit 309. When the computer program is loaded into RAM 303 and executed by the computing unit 301, one or more steps of the methods described above may be performed.

[0056] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0057] The program code used to implement the methods of the present invention can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code can be executed entirely on the machine, partially on the machine, as a standalone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0058] In the context of this invention, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

Claims

1. A non-invasive assessment method for chronic renal function based on near-infrared spectroscopy, characterized in that, include: Acquire near-infrared images of the face to be evaluated, and preprocess the near-infrared images of the face to obtain blood component spectra; The blood component spectrum is input into the discrimination model to output the content of a specified biomarker; The glomerular filtration value is determined based on the preprocessed information and the corresponding user's own attribute information; The glomerular filtration rate information is input into the evaluation model to output the staging information of chronic renal insufficiency.

2. The non-invasive assessment method for chronic renal function based on near-infrared spectroscopy according to claim 1, characterized in that, Acquiring a near-infrared image of the face to be evaluated, and preprocessing the near-infrared image to obtain a blood component spectrum, includes: After a near-infrared camera illuminates a face, a beam splitter receives the photoelectric pulse waves corresponding to the emitted light of each wavelength; a photoelectric converter and an analog-to-digital converter convert the photoelectric pulse waves into signals to obtain data on the changing emitted light intensity. The absorbance data of each component in the blood are calculated based on the data of the changing emitted light intensity; spectral samples of different blood components are obtained based on the absorbance data. The spectral sample is processed to obtain a blood component spectrum.

3. The non-invasive assessment method for chronic renal function based on near-infrared spectroscopy according to claim 2, characterized in that, The process of processing the spectral sample to obtain the blood component spectrum includes: The discrete points of the spectral samples are determined using Euclidean distance, and invalid spectral samples are discarded. Wavelet transform denoising method is used to remove interference noise from spectral samples; The spectral baseline of the spectral sample is de-spectralized using one or more of the following methods: peak-valley point flattening, offset subtraction, differential processing, and baseline tilting. The preprocessed spectral samples were normalized to obtain the preprocessed blood component spectrum.

4. The non-invasive assessment method for chronic renal function based on near-infrared spectroscopy according to claim 3, characterized in that, The glomerular filtration rate information is input into the evaluation model to output chronic renal insufficiency staging information, including: The evaluation model matches similar glomerular filtration values ​​from the database based on the glomerular filtration value information; The corresponding chronic kidney function stage information is determined based on similar glomerular filtration values.

5. The non-invasive assessment method for chronic renal insufficiency based on near-infrared spectroscopy according to claim 1 or 4, characterized in that, The user's own attribute information includes gender and age, and the following formula is used to determine the glomerular filtration value: eGFR = 170 × (Scr) - 0.999 × (age) - 0.176 × (BUN) - 0.170 × (ALB) 0.318 × (constant corresponding to sex), where Scr is the serum creatinine value, BUN is the blood urea nitrogen value, and ALB is the plasma albumin value.

6. The non-invasive assessment method for chronic renal function based on near-infrared spectroscopy according to claim 5, characterized in that, The database includes mapping relationships between glomerular filtration rate information, staging, and kidney function levels.

7. The non-invasive assessment method for chronic renal function based on near-infrared spectroscopy as described in claim 6, characterized in that, The mapping relationship includes: The mapping relationship between eGFR of 90-120 ml / min, stage 1, and normal renal function; the mapping relationship between glomerular filtration rate of 60-29 ml / min, stage 1, and mild renal impairment. The mapping relationship between eGFR of 30-59 ml / min, stage 3, and significantly impaired renal function; The mapping relationship between eGFR of 15-29 ml / min, stage 4, and severe renal impairment; The mapping relationship between eGFR < 15 ml / min, stage 5, and severely impaired renal function.

8. A non-invasive assessment device for chronic renal function based on near-infrared spectroscopy, characterized in that, include: A preprocessing unit is used to acquire a near-infrared image of the face to be evaluated and to preprocess the near-infrared image of the face to obtain a blood component spectrum. The calculation unit is used to determine the glomerular filtration value based on the preprocessed information and the corresponding user's own attribute information; The evaluation unit inputs the glomerular filtration value information into the evaluation model to output the staging information of chronic renal insufficiency.

9. An electronic device, characterized in that, include: At least one processor; And a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to cause the at least one processor to perform the method according to any one of claims 1-7.

10. A computer program product, characterized in that, When executed by a processor, the computer program implements the method described in any one of claims 1-7.

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