Diabetes breast surgery patient blood glucose level nursing assessment system

Abstract

This invention relates to the field of medical information processing technology, and more particularly to a blood glucose level nursing assessment system for diabetic breast surgery patients. The system includes: a data acquisition module for collecting surgical plan data, breast tissue imaging data, and menstrual cycle data of diabetic breast surgery patients; a surgical stress quantification module for constructing breast structure modulation factors to modulate the transmission and amplification effects of surgical trauma stress at the breast organ level; a periodic phase modulation module for generating periodic phase modulation factors; and a risk warning module for performing coupled analysis of the surgical trauma stress baseline, breast structure modulation factors, and periodic phase modulation factors to assess the patient's blood glucose fluctuation risk and provide blood glucose risk warnings. This invention effectively reduces the risk of perioperative blood glucose dysregulation by quantifying the surgical trauma stress effect under the coupled influence of breast structural characteristics and endogenous hormone rhythm characteristics.

Description

Technical Field

[0001] This invention relates to the field of medical information processing technology, and in particular to a blood glucose level nursing assessment system for diabetic breast surgery patients. Background Technology

[0002] Perioperative blood glucose stabilization is a core element in ensuring surgical safety and reducing postoperative complications for diabetic patients. Breast surgery, as a common surgical treatment for women, causes traumatic stress that significantly disrupts the body's glucose metabolism balance and exacerbates the risk of abnormal blood glucose fluctuations in diabetic patients.

[0003] As an organ containing complex glandular structures, the breast's tissue density, glandular distribution, and structural characteristics affect the extent of tissue traction and the path of trauma propagation during surgical procedures. Differences in breast structure among different patients will lead to varying degrees of physiological stress even with the same surgical plan. However, current techniques typically treat surgical trauma as a uniform external stimulus, failing to consider the coupling relationship between the surgical procedure path and the breast organ structure. There is a lack of technical means to model and modulate the stress transmission process based on individual breast structural characteristics, resulting in insufficient individualization of assessment results.

[0004] Meanwhile, breast surgery patients are predominantly female, and the rhythmic changes in estrogen and progesterone accompanying their menstrual cycle directly affect insulin sensitivity. The body's stress response to surgical trauma varies significantly in different phases of the cycle. For example, physiological insulin resistance exists during the luteal phase, which, when combined with surgical stress, can further exacerbate blood glucose fluctuations and significantly increase the incidence of adverse events such as incision infection, delayed healing, and cardiovascular and cerebrovascular accidents.

[0005] In summary, existing perioperative blood glucose assessment systems neglect the coupled effects of endogenous hormonal rhythms and exogenous surgical stress on blood glucose fluctuations, resulting in insufficient perioperative blood glucose control stability in diabetic breast surgery patients, which fails to meet the clinical needs of precision medicine. Summary of the Invention

[0006] To overcome the defects and shortcomings of existing technologies, this invention provides a blood glucose level nursing assessment system for diabetic breast surgery patients. By quantifying the stress effect modulated by breast characteristics and the influence of endogenous hormone rhythms on insulin sensitivity, it effectively reduces the risk of blood glucose out-of-control during the perioperative period.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a blood glucose level assessment system for diabetic breast surgery patients, comprising: The data acquisition module is used to collect surgical plan data, breast tissue imaging data, and menstrual cycle data from diabetic breast surgery patients. The surgical stress quantification module is used to extract surgical path planning information from surgical plan data and generate surgical trauma stress baseline values ​​based on the spatial geometric features of the surgical operation path. The breast feature modulation module is used to extract breast structural features from breast tissue imaging data and construct breast structural modulation factors based on the breast structural features to modulate the transmission and amplification effects of surgical trauma stress at the breast organ level. The cycle phase modulation module is used to determine the cycle phase position of the operation time in the physiological cycle based on physiological cycle data, and to generate a cycle phase modulation factor that characterizes the changes in the patient's insulin sensitivity with the physiological cycle. The risk warning module is used to perform coupled analysis of surgical trauma stress base value, breast structure modulation factor and periodic phase modulation factor to assess the patient's blood glucose fluctuation risk and provide blood glucose risk warning.

[0008] Furthermore, the specific execution steps of the surgical stress quantification module include: Obtain surgical plan data, including incision location information, target lesion location information, and surgical approach information; A three-dimensional spatial model of breast tissue is constructed based on breast tissue imaging data, and the surgical operation path trajectory is generated by combining surgical plan feature data. The spatial distance of the surgical operation path is accumulated to obtain the surgical path length, which characterizes the degree of tissue manipulation accumulation during the operation. The length of the surgical path is used as the baseline value of surgical trauma stress to characterize the basic stress intensity generated by the surgical procedure on the patient's body.

[0009] Furthermore, the specific modulation steps of the breast feature modulation module include: The three-dimensional spatial model of breast tissue is discretized into breast tissue voxel units, and the breast gland density corresponding to each breast tissue voxel unit is extracted using breast tissue image data. Based on the mammary gland density corresponding to the mammary gland unit and the surgical operation path trajectory, a stress transmission matrix matching individual mammary gland structure and surgical operation path is constructed. By performing matrix transformation on the surgical trauma stress baseline value through the stress transmission matrix, the conduction distribution characteristic value is obtained. The conduction distribution characteristic value is used to characterize the conduction distribution characteristics of surgical trauma stress along the surgical operation path in the breast organ tissue. By using the conduction distribution characteristic value as a breast structure modulation factor, the surgical trauma stress base value is modulated to obtain the stress effect value modulated by breast characteristics.

[0010] Furthermore, the construction of the stress transmission matrix matching individual breast structure and surgical operation path includes: discretizing the surgical operation path trajectory into path points, determining the breast tissue units corresponding to the path points, and using the normalized value of the breast gland density corresponding to the breast tissue units as the local stress transmission intensity; combining all path points and local stress transmission intensities to generate a stress transmission matrix, where the matrix rows represent breast tissue units, the matrix columns represent path points, and the matrix elements represent the local stress transmission intensity of the breast tissue units corresponding to the path points.

[0011] Furthermore, the physiological cycle data includes time-series data on hormone concentrations during the follicular phase, ovulation phase, and luteal phase; the specific modulation steps of the cycle phase modulation module include: Fourier transform was used to perform spectral analysis on hormone concentration time-series data to extract the dominant frequency and phase characteristics of hormone rhythms; Based on the dominant frequency and phase characteristics, the periodic phase of the operation time is determined; Preset the blood glucose response sensitivity modulation coefficient under different period phases; the blood glucose response sensitivity modulation coefficient is positively correlated with hormone concentration. By using the blood glucose response sensitivity modulation coefficient to correct the stress effect value modulated by breast characteristics, the predicted stress response value modulated by the periodic phase mechanism is obtained.

[0012] Furthermore, the specific execution steps of the risk warning module include: A multivariate linear regression model was constructed, with the surgical trauma stress baseline, the stress effect value modulated by breast characteristics, and the predicted stress response value modulated by the periodic phase mechanism as independent variables; Historical clinical datasets were acquired, and regression training was performed on the historical clinical datasets using the least squares method to obtain predicted values ​​for blood glucose fluctuation risks. When the predicted risk value of blood glucose fluctuation is greater than the preset blood glucose fluctuation risk threshold, a blood glucose risk warning is issued and preoperative intervention is carried out. When the predicted risk value of blood glucose fluctuation is less than or equal to the preset blood glucose fluctuation risk threshold, no blood glucose risk warning is issued and the surgery is performed according to the surgical plan data.

[0013] Secondly, this invention provides a method for assessing blood glucose levels in diabetic breast surgery patients, including: Collect surgical plan data, breast tissue imaging data, and menstrual cycle data from diabetic breast surgery patients; Surgical path planning information is extracted from surgical plan data, and surgical trauma stress baseline values ​​are generated based on the spatial geometric features of the surgical operation path. Breast structural features were extracted from breast tissue imaging data, and breast structural modulation factors were constructed based on these features to modulate the transmission and amplification effects of surgical trauma stress at the breast organ level. Based on physiological cycle data, the cyclic phase position of the operation time within the physiological cycle is determined, and a cyclic phase modulation factor characterizing the changes in the patient's insulin sensitivity with the physiological cycle is generated. Coupled analysis of surgical trauma stress base, breast structure modulation factor, and periodic phase modulation factor was performed to assess the risk of blood glucose fluctuations in patients and to provide early warning of blood glucose risk.

[0014] Thirdly, the present invention provides an electronic device, comprising: a processor and a memory, wherein the memory stores a computer program that can be called by the processor, and the processor executes a method for nursing assessment of blood glucose levels in diabetic breast surgery patients by calling the computer program stored in the memory.

[0015] Fourthly, the present invention provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform a method for assessing blood glucose levels in patients undergoing diabetic breast surgery.

[0016] Compared with the prior art, the present invention has the following advantages and beneficial effects: 1. This invention extracts the spatial geometric features of the surgical operation path, combines a three-dimensional model of breast tissue with glandular density to construct an individualized stress transmission matrix, quantifies the transmission distribution and amplification effect of surgical trauma in breast tissue, and then generates a breast feature-modulated stress effect value that fits the individual patient's situation, significantly improving the individualization and refinement of stress assessment.

[0017] 2. This invention introduces the phase modulation mechanism of the female menstrual cycle, incorporates the influence of endogenous hormone rhythm on insulin sensitivity into a unified analysis framework, and dynamically corrects the stress response under the same surgical stimulation through the cycle phase modulation factor. This achieves coupled modeling of exogenous surgical stress and endogenous hormone fluctuations, thereby predicting the differences in blood glucose fluctuations in patients at different cycle stages, effectively reducing the risk of perioperative blood glucose out-of-control, and improving the scientific and forward-looking nature of nursing decisions. Attached Figure Description

[0018] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 This is a schematic diagram of the blood glucose level nursing assessment system for diabetic breast surgery patients provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of the prediction process for the blood glucose fluctuation risk prediction value provided in the embodiments of the present invention. Detailed Implementation

[0019] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations thereof. In the absence of conflict, the embodiments of the present invention and the technical features in the embodiments can be combined with each other.

[0020] like Figure 1 As shown, Figure 1 This is a schematic diagram of the blood glucose level nursing assessment system for diabetic breast surgery patients provided in an embodiment of the present invention, including: The data acquisition module 101 is used to collect surgical plan data, breast tissue imaging data, and menstrual cycle data of diabetic breast surgery patients. The surgical plan data is obtained by accessing the hospital's electronic medical record system and surgical planning system; the breast tissue imaging data is obtained through breast ultrasound examination; and the menstrual cycle data is obtained through patient medical history inquiry, menstrual cycle records, and hormone level testing. The surgical stress quantification module 102 is used to extract surgical path planning information from surgical plan data and generate surgical trauma stress baseline values ​​based on the spatial geometric features of the surgical operation path. The breast feature modulation module 103 is used to extract breast structural features from breast tissue imaging data and construct breast structural modulation factors based on the breast structural features to modulate the transmission and amplification effect of surgical trauma stress at the breast organ level. The cycle phase modulation module 104 is used to determine the cycle phase position of the operation time in the physiological cycle based on the physiological cycle data, and to generate a cycle phase modulation factor that characterizes the change of the patient's insulin sensitivity with the physiological cycle. The risk warning module 105 is used to perform coupled analysis of surgical trauma stress base value, breast structure modulation factor and periodic phase modulation factor to assess the patient's blood glucose fluctuation risk and provide blood glucose risk warning.

[0021] In this embodiment of the invention, the data acquisition module 101 is used to collect surgical plan data, breast tissue imaging data and menstrual cycle data of diabetic breast surgery patients; In this embodiment of the invention, the surgical stress quantification module 102 is used to extract surgical path planning information from surgical plan data and generate surgical trauma stress baseline value based on the spatial geometric features of the surgical operation path. The core exogenous cause of perioperative blood glucose fluctuations is surgical trauma stress. Diabetic patients have impaired glucose metabolism regulation, making them far more sensitive to surgical stress than non-diabetic patients. Breast surgery, due to the unique glandular structure of the breast organ, exhibits significant individual variations in surgical pathways and inconsistent trauma transmission. The surgical stress quantification module, based on standardized surgical protocol data and breast tissue imaging data, extracts the basic trauma stress signal from the surgical procedure itself. By quantifying the spatial geometric features of the surgical path, it generates a baseline value for surgical trauma stress, providing standardized basic stress parameters for subsequent breast structural feature modulation and physiological cycle phase modulation. The specific execution steps of the surgical stress quantification module include: Obtain surgical plan data, including incision location information, target lesion location information, and surgical approach information; A three-dimensional spatial model of breast tissue was constructed based on breast tissue imaging data. Combined with surgical procedure feature data, a surgical path trajectory was generated. Specifically, breast tissue imaging data, including DICOM format breast MRI images, was acquired. The raw image data was preprocessed using medical image processing software (such as MITK, 3D Slicer, and Matlab Medical Imaging Toolbox). This included grayscale normalization, noise filtering, boundary segmentation between the lesion area and normal breast glandular tissue, adipose tissue, and chest wall tissue, removal of artifacts and irrelevant background interference, and conversion of all image data into a uniformly voxel-spaced three-dimensional image matrix format. The voxel spacing was set to 0.5mm × 0.5mm × 0.5mm to ensure spatial accuracy in subsequent modeling. Based on the preprocessed three-dimensional image matrix, a surface rendering method (Marching) was employed. The Cubes algorithm is used to perform three-dimensional reconstruction of key anatomical structures such as the overall contour of the breast, breast glandular tissue, lesion area, nipple, areola, and chest wall attachment points, generating a three-dimensional spatial model of breast tissue containing spatial coordinate information. A three-dimensional rectangular coordinate system is assigned to this model, with the patient's sternal midline as the Y-axis, the horizontal axis as the X-axis, and the vertical axis as the Z-axis, determining the three-dimensional spatial coordinates of all anatomical structures within the model. By extracting incision location information, target lesion location information, and surgical approach information from the surgical plan feature data, and using the incision start point coordinates as the path starting point and the lesion center and resection boundary coordinates as the path core target points, the surgical approach is discretized into equally spaced path points, with an adjacent path point spacing of 1 mm. Each path point corresponds to a unique three-dimensional spatial coordinate, forming a discretized surgical operation path trajectory point set. The surgical operation path trajectory point set is then connected sequentially to generate the surgical operation path trajectory, completing the spatial position calibration. The surgical path trajectory is calculated by accumulating spatial distances to obtain the surgical path length, which characterizes the degree of tissue manipulation accumulation during the operation. Specifically, the three-dimensional spatial coordinates corresponding to all path points in the surgical path trajectory point set are extracted. Using the three-dimensional Euclidean distance formula as the standard for calculating the distance between adjacent path points, the path distance between each group of adjacent path points is calculated segment by segment, starting from the first feature point (path starting point) and successively substituted into the three-dimensional Euclidean distance formula. The path distances between all adjacent path points are summed to obtain the surgical path length, which reflects the overall degree of manipulation accumulation of breast tissue during the operation. The longer the surgical path, the larger the scope of the operation and the stronger the surgical trauma stress effect. The length of the surgical path is used as the baseline value of surgical trauma stress to characterize the basic stress intensity generated by the surgical procedure on the patient's body. The surgical path trajectory is the complete spatial trajectory of the surgical instruments as they travel, cut, separate, and pull within the breast tissue. The length of the surgical path directly corresponds to the cumulative range of effects of the surgical operation on normal breast glands, connective tissue, microvessels, and nerve endings. Therefore, the longer the surgical path, the greater the range of tissue separation during the operation, the more tissue is pulled and cut by the instruments, the stronger the cumulative effect of mechanical trauma, the more significant the traumatic stimulation to the body, and the stronger the activated stress response of the body.

[0022] In this embodiment of the invention, the breast feature modulation module 103 is used to extract breast structural features from breast tissue image data and construct breast structural modulation factors based on the breast structural features to modulate the transmission and amplification effect of surgical trauma stress at the breast organ level. Current perioperative blood glucose assessment technologies generally treat surgical trauma as a uniform external stimulus, completely ignoring the influence of individual structural differences in the breast organ on the transmission of trauma stress. However, the breast, as a superficial solid organ composed of glands, fat, and connective tissue, has structural characteristics such as glandular density, glandular distribution, tissue thickness, and the location of lesions within the breast quadrant, which directly determine the transmission path, diffusion range, and intensity of surgical trauma stress. Furthermore, for diabetic breast surgery patients, glucose metabolism regulation is impaired, making the body more sensitive to structure-mediated stress amplification. The breast feature modulation module individually modulates the surgical trauma stress baseline based on breast structure, effectively avoiding blood glucose control errors caused by assessment bias. The specific modulation steps of the breast feature modulation module include: The three-dimensional spatial model of breast tissue was discretized into breast voxel units, and the breast gland density corresponding to each breast voxel unit was extracted using breast tissue image data. Specifically, the three-dimensional spatial model of breast tissue and its corresponding three-dimensional rectangular coordinate system constructed by the surgical stress quantification module were retrieved. Using an equal-voxel discretization algorithm, the three-dimensional spatial model of breast tissue was uniformly divided into regular cubic breast voxel units according to a preset voxel specification. The preset voxel specification can be set to 0.5mm×0.5mm×0.5mm, consistent with the voxel spacing in the image preprocessing mentioned above. At the same time, a unique unit number and corresponding three-dimensional coordinates were assigned to each breast voxel unit, and the spatial position of all voxel units was marked. The patient's DICOM format breast MRI image data was retrieved, and the three-dimensional coordinates of each breast voxel unit were matched to locate the corresponding image region of each breast voxel unit in the original image. Using a medical image density analysis tool, the breast gland density value corresponding to the image region was extracted, and the breast gland density extraction of all breast voxel units was completed one by one. Based on the mammary gland density corresponding to the mammary gland unit and the surgical operation path trajectory, a stress transmission matrix matching individual mammary gland structure and surgical operation path is constructed. Specifically, the surgical operation path trajectory point set generated by the surgical stress quantification module is extracted, and each path point is matched to the corresponding mammary gland unit. The gland density values ​​of all mammary gland units are normalized to obtain the local stress transmission intensity of each path point. The local stress transmission intensity is the ratio of the gland density value of the mammary gland unit corresponding to a single path point to the maximum gland density value of all mammary gland units. A stress transmission matrix is ​​constructed, with matrix rows corresponding to mammary gland units, matrix columns corresponding to discrete path points of the surgical operation path trajectory, and the value of each element in the matrix representing the local stress transmission intensity corresponding to the path point. The surgical trauma stress baseline value is transformed using the stress transfer matrix to obtain the conduction distribution characteristic value. This characteristic value characterizes the conduction distribution of surgical trauma stress along the surgical procedure path within the breast tissue. Specifically, following matrix multiplication rules, the surgical trauma stress baseline value is coupled with the stress transfer matrix. All elements of the matrix are traversed, and the cumulative conduction value of surgical trauma stress along the surgical procedure path within each breast voxel unit is calculated. The cumulative conduction values ​​of all voxel units are then summed to obtain the conduction distribution characteristic value. The calculation process for the cumulative conduction value is as follows: the stress transfer matrix is ​​denoted as... The matrix dimension is , The number of mammary gland somatic units. Let be the number of discrete path points in the surgical procedure trajectory, and let any element in the matrix be denoted as . , indicating the first The path point corresponds to the first Local stress transmission intensity per mammary gland unit; targeting mammary gland unit Traverse all path feature points surgical trauma stress baseline With corresponding matrix elements Multiply each product individually and sum them up to obtain the mammary gland somatic unit. The corresponding conduction accumulation value; Using the conduction distribution characteristic value as a breast structure modulation factor, the surgical trauma stress base value is modulated to obtain the stress effect value modulated by breast characteristics. Specifically, the stress effect value can be the product of the surgical trauma stress base value and the breast structure modulation factor. Constructing a stress transmission matrix that matches individual breast structure with surgical procedure path includes: discretizing the surgical procedure path into path points, determining the breast voxel units corresponding to the path points, and using the normalized value of the breast gland density corresponding to the breast voxel unit as the local stress transmission intensity; combining all path points and local stress transmission intensities to generate a stress transmission matrix, where matrix rows represent breast voxel units, matrix columns represent path points, and matrix elements represent the local stress transmission intensity of the breast voxel unit corresponding to the path point.

[0023] In this embodiment of the invention, the periodic phase modulation module 104 is used to determine the periodic phase position of the operation time in the physiological cycle based on the physiological cycle data, and generate a periodic phase modulation factor that characterizes the change of the patient's insulin sensitivity with the physiological cycle. The majority of breast surgery patients are women of childbearing age and perimenopausal women. Their estrogen and progesterone levels fluctuate regularly throughout their menstrual cycle. These fluctuations directly regulate insulin sensitivity, resulting in cyclical physiological differences in glucose metabolism: during the follicular phase, estrogen levels gradually increase, insulin sensitivity is relatively good, and the blood glucose response to surgical trauma is weak; during ovulation, hormone levels change abruptly, and insulin sensitivity fluctuates slightly; during the luteal phase, progesterone levels rise significantly, leading to physiological insulin resistance. The combined effect of surgical trauma and this fluctuation amplifies blood glucose fluctuations, significantly increasing the risk of complications such as incision infection, poor healing, and cardiovascular events. Diabetic patients, who already have insufficient insulin secretion or impaired insulin function, are even more sensitive to these hormone-mediated differences in stress sensitivity. The physiological cycle data includes time-series data on hormone concentrations during the follicular, ovulatory, and luteal phases. The specific modulation steps of the cycle phase modulation module include: Fourier transform was used to perform spectral analysis on time-series hormone concentration data to extract the dominant frequency and phase characteristics of the hormone rhythm. Specifically, daily serum estrogen and progesterone concentrations were collected from patients throughout their entire menstrual cycle to construct a hormone time-series sequence. The Fast Fourier Transform (FFT) algorithm was used to perform spectral transformation on the time-series data, converting the time-domain hormone concentration data into frequency-domain data and generating a spectral analysis graph. The frequency components in the spectral graph were traversed, and the frequency component with the largest amplitude was selected as the dominant frequency of the hormone rhythm, used to characterize the core rhythm of hormone fluctuations in the patient's menstrual cycle. Based on the phase spectrum corresponding to the dominant frequency, the initial phase at that frequency was extracted to characterize the initial phase characteristics of hormone fluctuations. The combined rhythmic phase of estrogen and progesterone was simultaneously calculated to obtain the hormone rhythm phase characteristics. Based on the dominant frequency and phase characteristics, the periodic phase of the operation time is determined. Specifically, based on the dominant frequency... Combined with the initial phase Construct a formula for calculating the phase of the menstrual cycle: In the formula This indicates the number of days in the menstrual cycle corresponding to the surgery date (the first day of menstruation is recorded as 1). The timing of the surgery was determined by combining the menstrual cycle phase division criteria, where the follicular phase corresponds to the following phase range: The phase range corresponding to ovulation is The phase range corresponding to the luteal phase is ;pass The blood glucose response sensitivity modulation coefficients are preset for different cyclic phases. The blood glucose response sensitivity modulation coefficients are positively correlated with hormone concentration. Specifically, based on training with large clinical sample historical data, standardized blood glucose response sensitivity modulation coefficients are preset, with the following specific values: follicular phase modulation coefficient is 1.0-1.2, ovulation phase modulation coefficient is 1.3-1.5, and luteal phase modulation coefficient is 1.6-2.0. The stress effect value modulated by breast characteristics is corrected by the blood glucose response sensitivity modulation coefficient to obtain the stress response prediction value modulated by the periodic phase mechanism. Specifically, the stress response prediction value is the product of the stress effect value and the blood glucose response sensitivity modulation coefficient.

[0024] In this embodiment of the invention, the risk warning module 105 is used to perform coupled analysis of surgical trauma stress base value, breast structure modulation factor and periodic phase modulation factor to assess the patient’s blood glucose fluctuation risk and provide blood glucose risk warning. like Figure 2 As shown, Figure 2 This is a schematic diagram of the prediction process for the blood glucose fluctuation risk prediction value provided in an embodiment of the present invention. The specific execution steps of the risk warning module include: A multivariate linear regression model was constructed, using the surgical trauma stress baseline, the stress effect value modulated by breast characteristics, and the predicted stress response modulated by the periodic phase mechanism as independent variables. Specifically, the multivariate linear regression model formula can be: In the formula This indicates the predicted risk value for blood glucose fluctuations. , and These represent the surgical trauma stress baseline, the stress effect value modulated by breast characteristics, and the predicted stress response value modulated by the periodic phase mechanism, respectively. , and These represent the regression coefficients corresponding to the independent variables. This represents the model constant correction term; Historical clinical datasets are acquired and regression training is performed on them using the least squares method to obtain predicted values ​​for blood glucose fluctuation risk. Specifically, SPSS, Python, or R language data analysis tools can be used to perform regression training on historical clinical datasets based on the least squares method, fit the regression coefficients corresponding to each independent variable and the model constant correction term, and substitute the surgical trauma stress base value, the stress effect value modulated by breast characteristics, and the stress response prediction value modulated by the periodic phase mechanism as independent variables into the multivariate linear regression model formula to obtain predicted values ​​for blood glucose fluctuation risk. When the predicted risk value of blood glucose fluctuation is greater than the preset blood glucose fluctuation risk threshold, a blood glucose risk warning is issued and preoperative intervention is carried out. When the predicted risk value of blood glucose fluctuation is less than or equal to the preset blood glucose fluctuation risk threshold, no blood glucose risk warning is issued and the surgery is performed according to the surgical plan data. Specifically, the preset blood glucose fluctuation risk threshold can be determined by fitting historical clinical data. Preoperative intervention includes dietary and nutritional intervention as well as drug intervention.

[0025] The method for assessing blood glucose levels in diabetic breast surgery patients provided in this invention specifically includes the following steps: Surgical plan data, breast tissue imaging data, and menstrual cycle data were collected from diabetic breast surgery patients.

[0026] Surgical path planning information is extracted from surgical plan data, and surgical trauma stress baseline values ​​are generated based on the spatial geometric features of the surgical operation path. Specific steps include: Obtain surgical plan data, including incision location information, target lesion location information, and surgical approach information; A three-dimensional spatial model of breast tissue is constructed based on breast tissue imaging data, and the surgical operation path trajectory is generated by combining surgical plan feature data. The spatial distance of the surgical operation path is accumulated to obtain the surgical path length, which characterizes the degree of tissue manipulation accumulation during the operation. The length of the surgical path is used as the baseline value of surgical trauma stress to characterize the basic stress intensity generated by the surgical procedure on the patient's body.

[0027] Breast structural features are extracted from breast tissue imaging data, and breast structural modulation factors are constructed based on these features to modulate the transmission and amplification effects of surgical trauma stress at the breast organ level. Specific steps include: The three-dimensional spatial model of breast tissue is discretized into breast tissue voxel units, and the breast gland density corresponding to each breast tissue voxel unit is extracted using breast tissue image data. Based on the mammary gland density corresponding to the mammary gland unit and the surgical operation path trajectory, a stress transmission matrix matching individual mammary gland structure and surgical operation path is constructed. This includes: discretizing the surgical operation path trajectory into path points, determining the mammary gland unit corresponding to the path point, and using the normalized value of the mammary gland density corresponding to the mammary gland unit as the local stress transmission intensity; combining all path points and local stress transmission intensities to generate a stress transmission matrix, where the matrix rows represent mammary gland units, the matrix columns represent path points, and the matrix elements represent the local stress transmission intensity of the mammary gland unit corresponding to the path point. By performing matrix transformation on the surgical trauma stress baseline value through the stress transmission matrix, the conduction distribution characteristic value is obtained. The conduction distribution characteristic value is used to characterize the conduction distribution characteristics of surgical trauma stress along the surgical operation path in the breast organ tissue. By using the conduction distribution characteristic value as a breast structure modulation factor, the surgical trauma stress base value is modulated to obtain the stress effect value modulated by breast characteristics.

[0028] Based on menstrual cycle data, the cyclical phase position of the surgery time within the menstrual cycle is determined, and a cyclical phase modulation factor characterizing the changes in the patient's insulin sensitivity with the menstrual cycle is generated. Specific steps include: Fourier transform was used to perform spectral analysis on hormone concentration time-series data to extract the dominant frequency and phase characteristics of hormone rhythms; Based on the dominant frequency and phase characteristics, the periodic phase of the operation time is determined; Preset the blood glucose response sensitivity modulation coefficient under different period phases; the blood glucose response sensitivity modulation coefficient is positively correlated with hormone concentration. By using the blood glucose response sensitivity modulation coefficient to correct the stress effect value modulated by breast characteristics, the predicted stress response value modulated by the periodic phase mechanism is obtained.

[0029] Coupled analysis of surgical trauma stress base, breast structure modulation factors, and periodic phase modulation factors was performed to assess the patient's risk of blood glucose fluctuations and to provide early warning of blood glucose risk. Specific steps included: A multivariate linear regression model was constructed, with the surgical trauma stress baseline, the stress effect value modulated by breast characteristics, and the predicted stress response value modulated by the periodic phase mechanism as independent variables; Historical clinical datasets were acquired, and regression training was performed on the historical clinical datasets using the least squares method to obtain predicted values ​​for blood glucose fluctuation risks. When the predicted risk value of blood glucose fluctuation is greater than the preset blood glucose fluctuation risk threshold, a blood glucose risk warning is issued and preoperative intervention is carried out. When the predicted risk value of blood glucose fluctuation is less than or equal to the preset blood glucose fluctuation risk threshold, no blood glucose risk warning is issued and the surgery is performed according to the surgical plan data.

[0030] Embodiments of the present invention also provide an electronic device, including a memory, a processor, and a communication bus; the memory and the processor are connected via the communication bus. The memory stores a method for assessing blood glucose levels in diabetic breast surgery patients that can be loaded by the processor and executed as provided in the above embodiments.

[0031] The memory can be used to store instructions, programs, code, code sets, or instruction sets. The memory may include a program storage area and a data storage area. The program storage area may store instructions for implementing an operating system, instructions for at least one function, and instructions for implementing the blood glucose level nursing assessment method for diabetic breast surgery patients provided in the above embodiments, etc. The data storage area may store data involved in the blood glucose level nursing assessment method for diabetic breast surgery patients provided in the above embodiments, etc.

[0032] A processor may include one or more processing cores. The processor executes instructions, programs, code sets, or instruction sets stored in memory, and calls data stored in memory to perform various functions and process data according to the present invention. The processor may be at least one of the following: Application Specific Integrated Circuit (ASIC), Digital Signal Processor (DSP), Digital Signal Processing Device (DSPD), Programmable Logic Device (PLD), Field Programmable Gate Array (FPGA), Central Processing Unit (CPU), controller, microcontroller, and microprocessor. It is understood that, for different devices, the electronic devices used to implement the above-described processor functions may also be other types, and the embodiments of the present invention do not specifically limit this.

[0033] A communication bus can include a pathway for transmitting information between the aforementioned components. The communication bus can be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Communication buses can be categorized into address buses, data buses, control buses, etc.

[0034] This invention provides a computer-readable storage medium storing a computer program that can be loaded by a processor and executed as described in the above embodiments, a method for assessing blood glucose levels in diabetic breast surgery patients.

[0035] In this embodiment of the invention, the computer-readable storage medium can be a tangible device that holds and stores instructions used by an instruction execution device. The computer-readable storage medium can be, but is not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination thereof. Specifically, the computer-readable storage medium can be a portable computer disk, a hard disk, a USB flash drive, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), lectern random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory stick, floppy disk, optical disk, magnetic disk, mechanical encoding device, or any combination thereof.

[0036] The terms “comprising,” “including,” or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0037] The above description is merely a preferred embodiment of the present invention and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of this application is not limited to the technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the foregoing concept. For example, technical solutions formed by substituting the above-described features with (but not limited to) technical features with similar functions claimed in this invention.

Claims

1. A blood glucose level nursing assessment system for diabetic breast surgery patients, characterized in that, include: The data acquisition module is used to collect surgical plan data, breast tissue imaging data, and menstrual cycle data from diabetic breast surgery patients. The surgical stress quantification module is used to extract surgical path planning information from surgical plan data and generate surgical trauma stress baseline values ​​based on the spatial geometric features of the surgical operation path. The breast feature modulation module is used to extract breast structural features from breast tissue imaging data and construct breast structural modulation factors based on the breast structural features to modulate the transmission and amplification effects of surgical trauma stress at the breast organ level. The cycle phase modulation module is used to determine the cycle phase position of the operation time in the physiological cycle based on physiological cycle data, and to generate a cycle phase modulation factor that characterizes the changes in the patient's insulin sensitivity with the physiological cycle. The risk warning module is used to perform coupled analysis of surgical trauma stress base value, breast structure modulation factor and periodic phase modulation factor to assess the patient's blood glucose fluctuation risk and provide blood glucose risk warning.

2. The blood glucose level nursing assessment system for diabetic breast surgery patients according to claim 1, characterized in that, The specific execution steps of the surgical stress quantification module include: Obtain surgical plan data, including incision location information, target lesion location information, and surgical approach information; A three-dimensional spatial model of breast tissue is constructed based on breast tissue imaging data, and the surgical operation path trajectory is generated by combining surgical plan feature data. The spatial distance of the surgical operation path is accumulated to obtain the surgical path length, which characterizes the degree of tissue manipulation accumulation during the operation. The length of the surgical path is used as the baseline value of surgical trauma stress to characterize the basic stress intensity generated by the surgical procedure on the patient's body.

3. The blood glucose level nursing assessment system for diabetic breast surgery patients according to claim 1, characterized in that, The specific modulation steps of the breast feature modulation module include: The three-dimensional spatial model of breast tissue is discretized into breast tissue voxel units, and the breast gland density corresponding to each breast tissue voxel unit is extracted using breast tissue image data. Based on the mammary gland density corresponding to the mammary gland unit and the surgical operation path trajectory, a stress transmission matrix matching individual mammary gland structure and surgical operation path is constructed. By performing matrix transformation on the surgical trauma stress baseline value through the stress transmission matrix, the conduction distribution characteristic value is obtained. The conduction distribution characteristic value is used to characterize the conduction distribution characteristics of surgical trauma stress along the surgical operation path in the breast organ tissue. By using the conduction distribution characteristic value as a breast structure modulation factor, the surgical trauma stress base value is modulated to obtain the stress effect value modulated by breast characteristics.

4. The blood glucose level nursing assessment system for diabetic breast surgery patients according to claim 3, characterized in that, The process of constructing a stress transmission matrix that matches individual breast structure with surgical procedure path includes: discretizing the surgical procedure path trajectory into path points, determining the breast tissue units corresponding to the path points, and using the normalized value of the breast tissue density corresponding to the breast tissue units as the local stress transmission intensity; combining all path points and local stress transmission intensities to generate a stress transmission matrix, where matrix rows represent breast tissue units, matrix columns represent path points, and matrix elements represent the local stress transmission intensity of the breast tissue units corresponding to the path points.

5. The blood glucose level nursing assessment system for diabetic breast surgery patients according to claim 1, characterized in that, The physiological cycle data includes time-series data on hormone concentrations during the follicular phase, ovulation phase, and luteal phase; The specific modulation steps of the periodic phase modulation module include: Fourier transform was used to perform spectral analysis on hormone concentration time-series data to extract the dominant frequency and phase characteristics of hormone rhythms; Based on the dominant frequency and phase characteristics, the periodic phase of the operation time is determined; Preset the blood glucose response sensitivity modulation coefficient under different period phases; the blood glucose response sensitivity modulation coefficient is positively correlated with hormone concentration. By using the blood glucose response sensitivity modulation coefficient to correct the stress effect value modulated by breast characteristics, the predicted stress response value modulated by the periodic phase mechanism is obtained.

6. The blood glucose level nursing assessment system for diabetic breast surgery patients according to claim 1, characterized in that, The specific execution steps of the risk warning module include: A multivariate linear regression model was constructed, with the surgical trauma stress baseline, the stress effect value modulated by breast characteristics, and the predicted stress response value modulated by the periodic phase mechanism as independent variables; Historical clinical datasets were acquired, and regression training was performed on the historical clinical datasets using the least squares method to obtain predicted values ​​for blood glucose fluctuation risks. When the predicted risk value of blood glucose fluctuation is greater than the preset blood glucose fluctuation risk threshold, a blood glucose risk warning is issued and preoperative intervention is carried out. When the predicted risk value of blood glucose fluctuation is less than or equal to the preset blood glucose fluctuation risk threshold, no blood glucose risk warning is issued and the surgery is performed according to the surgical plan data.

7. A method for assessing blood glucose levels in diabetic breast surgery patients, applied to the blood glucose level assessment system for diabetic breast surgery patients as described in any one of claims 1-6, characterized in that, The method includes: Collect surgical plan data, breast tissue imaging data, and menstrual cycle data from diabetic breast surgery patients; Surgical path planning information is extracted from surgical plan data, and surgical trauma stress baseline values ​​are generated based on the spatial geometric features of the surgical operation path. Breast structural features were extracted from breast tissue imaging data, and breast structural modulation factors were constructed based on these features to modulate the transmission and amplification effects of surgical trauma stress at the breast organ level. Based on physiological cycle data, the cyclic phase position of the operation time within the physiological cycle is determined, and a cyclic phase modulation factor characterizing the changes in the patient's insulin sensitivity with the physiological cycle is generated. Coupled analysis of surgical trauma stress base, breast structure modulation factor, and periodic phase modulation factor was performed to assess the risk of blood glucose fluctuations in patients and to provide early warning of blood glucose risk.

8. An electronic device, comprising: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; characterized in that the processor executes the blood glucose level nursing assessment method for diabetic breast surgery patients as described in claim 7 by calling the computer program stored in the memory.

9. A computer-readable storage medium, characterized in that, The device stores instructions that, when executed on a computer, cause the computer to perform the method for assessing blood glucose levels in diabetic breast surgery patients as described in claim 7.

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