Newborn intestinal stoma nursing virtual simulation operation system

Abstract

The invention discloses a virtual simulation operation system for neonatal enterostomy nursing. The virtual simulation operation system comprises a biomechanical simulation module which is used for simulating viscoelastic mechanical characteristics of neonatal abdominal wall tissues and hydrodynamic behaviors of enterostomy excreta in real time; the dynamic physiological simulation module generates a quantitative score by fusing operation normalization, complication prevention effect and injury occurrence rate, and maps a score result into a three-dimensional virtual scene in real time; the evaluation and data management module constructs a seamless connection system of hospital-community-home nursing scenes, supports continuous execution and data synchronization of nursing plans across the scenes, is internally provided with a complication dynamic evolution prediction model, and deduces the occurrence probability and severity of stoma stenosis and skin injury in real time according to the nursing operation quality; by simulating mechanical properties of newborn abdominal wall tissues and fluid dynamic behaviors of excreta, the system provides a highly realistic nursing operation environment, and the reality sense and effectiveness of training are enhanced.

Description

Technical Field

[0001] The invention relates to a virtual simulation operating system, in particular to a virtual simulation operating system for neonatal enterostomy care. Background Art

[0002] In the medical field, the rapid development of virtual simulation technology has provided innovative teaching and practical training methods for medical care. Traditional training methods, such as using models or animals for simulated operations, are often limited by a lack of realism and interactivity, making it difficult to achieve ideal training results. Furthermore, traditional teaching methods may overemphasize theory and neglect practice, resulting in students having a weak grasp of key knowledge points and a lack of practical experience. While existing medical simulation systems simulate medical scenarios to a certain extent, they still lack a high degree of realism and real-time feedback mechanisms in the niche field of neonatal colostomy care, making it impossible to accurately simulate the complex physiological and pathological processes of neonatal colostomy.

[0003] Neonatal colostomy, while a crucial life-saving treatment, also presents a host of nursing challenges. Due to the immaturity of newborns, their skin is less resilient, and their treatment compliance is low, they are susceptible to various postoperative infections and complications. Therefore, improving the care provided by healthcare professionals for neonatal colostomies is crucial. However, traditional training methods struggle to meet this demand, and a more efficient and safer training approach is urgently needed.

[0004] Although the existing virtual simulation teaching system for colostomy care can provide virtual scenes for operational practice, its interactivity with real nursing scenes is weak and it cannot realistically simulate the various complex situations and emergencies that may occur in neonatal colostomy care, resulting in users being unable to cope with actual problems.

[0005] Therefore, there is an urgent need for a virtual simulation operating system for neonatal colostomy care to solve the technical problems existing in the above-mentioned prior art. Summary of the Invention

[0006] The present invention overcomes the deficiencies of the prior art and provides a virtual simulation operating system for neonatal enterostomy care.

[0007] To achieve the above-mentioned object, the technical solution adopted by the present invention is: a virtual simulation operating system for neonatal enterostomy care, comprising: a biomechanical simulation module, a dynamic physiological simulation module, and an evaluation and data management module;

[0008] The biomechanical simulation module is used to simulate the viscoelastic mechanical properties of neonatal abdominal wall tissue and the fluid dynamics behavior of intestinal stoma excreta in real time;

[0009] The dynamic physiological simulation module generates a quantitative score by integrating the standardization of operation, complication prevention effect and injury incidence, and maps the score results to the three-dimensional virtual scene in real time;

[0010] The evaluation and data management module builds a seamless connection system for hospital-community-home care scenarios, supports the continued execution of care plans across scenarios and data synchronization, and has a built-in dynamic evolution prediction model for complications, which can deduce the probability and severity of stoma stenosis and skin damage in real time based on the quality of nursing operations.

[0011] In a preferred embodiment of the present invention, the formula for the viscoelastic mechanical property is: Where E is the elastic modulus, ranging from 0.8 to 1.2 MPa, and is dynamically adjusted according to the newborn's age. is the viscosity coefficient, ranging from 0.5 to 0.8 Pa·s, and is negatively correlated with the ostomy bag sticking time t; is the initial stress offset, calculated from the newborn's weight G, =0.1G+0.5.

[0012] In a preferred embodiment of the present invention, the formula for the fluid dynamics behavior is: Where, is the density of excrement, ranging from 1000 to 1050 kg / m 3 , dynamically adjusted according to feeding type; is viscosity, ranging from 0.001 to 0.002 Pa·s, and is positively correlated with the frequency of intestinal motility f; It is the combined force of gravity and changes in body position.

[0013] In a preferred embodiment of the present invention, the graded warning mechanism is automatically activated when the dynamic physiological simulation module detects dangerous operations; the dangerous operations include: the disinfection operation timeout or abnormal contact area, the pressure applied by the instrument exceeds the tolerance threshold of the newborn skin, and the sealing of the stoma device shows a gradual deterioration trend.

[0014] In a preferred embodiment of the present invention, the formula for the quantitative score is: Where A is the accuracy of the operation steps; L is the leakage rate; S is the proportion of skin injury area; I is the incidence of device-related pressure injuries; 、 、 、 are all weight coefficients.

[0015] In a preferred embodiment of the present invention, the biomechanical simulation module integrates force feedback, displacement tracking, and temperature sensing data by using an adaptive filtering algorithm to eliminate interference noise between the data, and dynamically adjusts the parameters of the biomechanical simulation module according to the real-time physiological state of the newborn;

[0016] The formula expression of the filtering algorithm is: Where, is the estimated value of the state at time k, that is, based on the current measurement value and the predicted value at the previous moment ; is the Kalman gain; is the observation value; H is the observation matrix; J is the unit matrix.

[0017] In a preferred embodiment of the present invention, the correction rules for dynamically correcting the simulation model parameters according to the real-time physiological state of the newborn include:

[0018] The elastic modulus E is dynamically adjusted with the newborn's age M. The adjustment formula is: =E+0.1M (M≤6 months), =E+0.05M (M>6 months);

[0019] Viscosity coefficient Dynamic adjustment with intestinal peristalsis frequency f, the adjustment formula is: = +0.0001f.

[0020] In a preferred embodiment of the present invention, the virtual simulation operating system further comprises constructing a four-dimensional evaluation system to comprehensively evaluate the training effect from the perspectives of trainees' subjective feedback, knowledge mastery, operational skill transfer, and clinical outcome improvement. The calculation model of the four-dimensional evaluation system is:

[0021] .

[0022] In a preferred embodiment of the present invention, the formula expression of the complication dynamic evolution prediction model is:

[0023] ;

[0024] Where m is the reaction rate constant; C is the current evolution state; It is a state of historical evolution; is the attenuation coefficient; t is the unit time.

[0025] The present invention solves the defects existing in the background technology and has the following beneficial effects:

[0026] (1) By simulating the mechanical properties of neonatal abdominal wall tissue and the fluid dynamics of feces, this system provides a highly realistic nursing operation environment, enhancing the realism and effectiveness of training.

[0027] (2) The real-time feedback and quantitative evaluation mechanism enables medical staff to understand their own operating level and improvement direction in a timely manner, so that they can carry out targeted training and improve the training effect.

[0028] (3) The dynamic physiological simulation module and graded warning mechanism provide medical staff with immediate operational feedback, helping them to correct incorrect operations in a timely manner, reduce the occurrence of complications, and improve the quality of care.

[0029] (4) The evaluation and data management module supports the continued execution of nursing plans across scenarios and data synchronization, ensuring the continuity and effectiveness of nursing and improving the overall quality of nursing.

[0030] (5) The dynamic evolution prediction model of complications can deduce the probability and severity of complications in real time based on the quality of nursing operations, providing medical staff with a scientific basis for prevention and treatment, and helping to formulate more accurate nursing plans. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments described in the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive efforts.

[0032] Figure 1 It is a system diagram of a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0033] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0034] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the scope of protection of the present invention is not limited to the specific embodiments disclosed below.

[0035] It should be noted in advance that finite element analysis is a widely used numerical simulation method. Its core idea is to divide complex geometric shapes into a finite number of simple small units (i.e., finite elements), perform mechanical analysis on each small unit, and then obtain the overall mechanical behavior by combining the results of these small units.

[0036] In the virtual simulation system for neonatal colostomy care, a finite element analysis model is used to simulate the viscoelastic mechanical properties of neonatal abdominal wall tissue. By defining key material parameters such as the elastic modulus and viscosity coefficient, and setting appropriate boundary and loading conditions, the deformation and stress distribution of tissues when subjected to external forces (such as pressure during nursing operations) can be simulated.

[0037] Specific implementation methods include:

[0038] Finite element analysis software such as ANSYS and Abaqus is used to establish a three-dimensional model based on the actual anatomical structure of the neonatal abdominal wall tissue.

[0039] Define the tissue's material properties, such as elastic modulus and viscosity, based on experimental data or literature. For viscoelastic materials, use more complex models such as the Maxwell model or the Kelvin model to describe their mechanical behavior.

[0040] According to the actual situation of nursing operation, the boundary conditions and loading conditions of the model are set.

[0041] Finite element analysis software is used to perform numerical calculations to obtain the deformation and stress distribution of the tissue when subjected to external forces.

[0042] Analyze and visualize simulation results to help medical staff more intuitively understand the impact of nursing operations on the organization.

[0043] In the virtual simulation system for neonatal colostomy care, computational fluid dynamics models can be used to simulate the fluid dynamics of colostomy excreta. By defining fluid parameters such as density and viscosity, and setting flow boundary conditions and initial conditions, the flow of excreta through the colostomy can be simulated, including flow rate and pressure distribution.

[0044] Specific implementation methods include:

[0045] Computational fluid dynamics software such as ANSYS Fluent and COMSOL Multiphysics was used to establish a three-dimensional flow model based on the actual anatomical structure of the stoma.

[0046] Define the density, viscosity and other fluid properties of excreta based on experimental data or literature.

[0047] According to the flow of excrement, the boundary conditions of the model, such as velocity inlet, pressure outlet, etc., and initial conditions, such as initial flow velocity, pressure, etc., are set.

[0048] Computational fluid dynamics software was used to perform numerical calculations to obtain the flow of excreta at the stoma.

[0049] Analyzing and visualizing simulation results allows medical staff to more intuitively understand the flow characteristics of feces and optimize nursing operations.

[0050] like Figure 1 As shown, the virtual simulation operating system for neonatal colostomy care includes: a biomechanical simulation module, a dynamic physiological simulation module, and an evaluation and data management module.

[0051] The biomechanical simulation module is used to simulate the viscoelastic mechanical properties of neonatal abdominal wall tissue and the fluid dynamics behavior of intestinal stoma excretion in real time.

[0052] Furthermore, the formula for the viscoelastic mechanical properties is: Where E is the elastic modulus, ranging from 0.8 to 1.2 MPa, and is dynamically adjusted according to the newborn's age. is the viscosity coefficient, ranging from 0.5 to 0.8 Pa·s, and is negatively correlated with the ostomy bag sticking time t; is the initial stress offset, calculated from the newborn's weight G, =0.1G+0.5.

[0053] The fluid dynamics behavior is expressed as: Where, is the density of excrement, ranging from 1000 to 1050 kg / m 3 , dynamically adjusted according to feeding type; is viscosity, ranging from 0.001 to 0.002 Pa·s, and is positively correlated with the frequency of intestinal motility f; It is the combined force of gravity and changes in body position.

[0054] Dynamic adjustment of fecal matter density based on feeding type (breast milk vs. formula) is achieved by establishing a feeding type-density mapping table. Based on the input feeding type, the system automatically selects the corresponding density value to more realistically simulate the physical properties of feces.

[0055] In order to improve the accuracy of state estimation, multi-sensor data fusion technology is introduced, combining force feedback, displacement tracking, temperature perception and pressure distribution data, and the extended Kalman filter (EKF) or unscented Kalman filter (UKF) is used to optimize the simulation process.

[0056] In the filtering algorithm, the observation matrix H can be dynamically adjusted according to the sensor type and layout to adapt to the simulation requirements in different application scenarios.

[0057] The biomechanical simulation module integrates force feedback, displacement tracking, and temperature sensing data using an adaptive filtering algorithm to eliminate interference noise between data and dynamically adjust the parameters of the biomechanical simulation module based on the real-time physiological state of the newborn.

[0058] The formula of the filtering algorithm is: Where, is the estimated value of the state at time k, that is, based on the current measurement value and the predicted value at the previous moment ; is the Kalman gain; is the observation value; H is the observation matrix; J is the unit matrix.

[0059] The correction rules for dynamically correcting the simulation model parameters according to the real-time physiological state of the newborn include:

[0060] The elastic modulus E is dynamically adjusted with the newborn's age M. The adjustment formula is: =E+0.1M (M≤6 months), =E+0.05M (M>6 months);

[0061] Viscosity coefficient Dynamic adjustment with intestinal peristalsis frequency f, the adjustment formula is: = +0.0001f.

[0062] The dynamic physiological simulation module generates a quantitative score by integrating operational standardization, complication prevention effect and injury incidence, and maps the score results to a three-dimensional virtual scene in real time.

[0063] Furthermore, when the dynamic physiological simulation module detects dangerous operations, the graded warning mechanism is automatically activated; dangerous operations include: disinfection operation timeout or abnormal contact area, instrument pressure exceeding the neonatal skin tolerance threshold, and the ostomy device sealing showing a gradual deterioration trend.

[0064] The formula for quantitative scoring is: Where A is the accuracy of the operation steps; L is the leakage rate; S is the proportion of skin injury area; I is the incidence of device-related pressure injuries; 、 、 、 are all weight coefficients.

[0065] The accuracy rate (A) of the operation steps is weighted according to key nodes in the operation process, such as the location of the ostomy bag attachment and the disinfection range. A qualified threshold is set for each node, and points are deducted if the standard is not met, so as to quantitatively evaluate the standardization of the operation.

[0066] The calculation of leakage rate L introduces two dimensions, leakage area and leakage time, to comprehensively evaluate the leakage situation and more comprehensively reflect the nursing effect.

[0067] The skin injury area ratio S and the device-related pressure injury incidence I are automatically calculated using image recognition technology and scored based on the preset injury grade classification standards to objectively evaluate the safety of the nursing process.

[0068] Using virtual reality (VR) or augmented reality (AR) technology, quantitative scoring results are mapped to a three-dimensional virtual scene in real time. Nursing outcomes are intuitively displayed through color coding and dynamic icons, allowing medical staff to more directly understand problems encountered during the nursing process.

[0069] The introduction of tactile feedback devices can simulate the touch under different operating forces, enhance the immersion of medical staff, and improve the training effect.

[0070] Set multiple levels of warning thresholds, such as yellow warning (minor risk), orange warning (medium risk), and red warning (high risk). Different warning levels correspond to different prompts and treatment suggestions to help medical staff take timely measures to avoid complications.

[0071] The evaluation and data management module builds a seamless connection system for hospital-community-home care scenarios, supports the continued execution of care plans across scenarios and data synchronization, and has a built-in dynamic evolution prediction model for complications, which can deduce the probability and severity of stoma stenosis and skin damage in real time based on the quality of nursing operations.

[0072] The virtual simulation operating system also includes the construction of a four-dimensional evaluation system to comprehensively evaluate the training effect from the perspectives of trainees' subjective feedback, knowledge mastery, operational skill transfer, and clinical outcome improvement. The calculation model of the four-dimensional evaluation system is:

[0073] .

[0074] The formula of the dynamic evolution prediction model of complications is:

[0075] ;

[0076] Where m is the reaction rate constant; C is the current evolution state; It is a state of historical evolution; is the attenuation coefficient; t is the unit time.

[0077] Furthermore, the biomechanical simulation module is implemented as follows: Based on the neonatal abdominal CT data, a three-dimensional abdominal wall tissue model including skin, subcutaneous fat, and muscle layer is constructed using Mimics software; Abaqus software is imported to divide the tetrahedral mesh, with a cell size of ≤1mm to ensure the simulation accuracy of the subtle deformation of the neonatal skin; the skin layer adopts a viscoelastic model, and the fat layer adopts a linear elastic model with E=0.5MPa and Poisson's ratio=0.45; the displacement of the lumbar region is fixed, and the ostomy bag adhesive pressure is applied at 0.5~1.0N / cm² to simulate the clinical operation force. Multi-sensor data fusion:

[0078] The operating pressure is collected through force feedback gloves, the movement trajectory of the stoma device is tracked by the displacement sensor, and the temperature sensor monitors the changes in skin surface temperature. The extended Kalman filter algorithm is used to fuse the data and correct the model parameters.

[0079] Disinfection operation timeout: Set a single disinfection time threshold of 15±2 seconds and monitor in real time through the timer module; Abnormal contact area: Based on machine vision algorithms, such as OpenCV contour detection, identify whether the contact area between the disinfection cotton ball and the skin is less than the standard value of 2.0±0.5cm²; the force feedback sensor collects pressure in real time. If it exceeds the newborn skin tolerance threshold of 0.8N / cm², an early warning will be triggered in accordance with the "Newborn Skin Care Guidelines".

[0080] Quantitative scoring weight coefficient: The weight is determined using the hierarchical analysis method: 、 、 、 According to a questionnaire survey conducted by clinical experts, the priority is: operation steps > leakage risk > skin damage > instrument pressure.

[0081] The cross-scenario data synchronization mechanism of the assessment and data management module adopts the HL7 FHIR standard to define the nursing plan data structure, including stoma assessment form, replacement frequency, and complication warning records, and realizes real-time synchronization of data between the hospital HIS system, community health records, and home care APP through the REST API.

[0082] Complication prediction model parameters were dynamically adjusted based on the nursing operation quality score (m = 0.1 / hour when Q ≥ 80 minutes and m = 0.5 / hour when Q < 60 minutes). The attenuation coefficient λ was fixed at 0.05 / hour, reflecting the attenuation effect of historical operations on current risk.

[0083] Based on the above, further explanation is needed:

[0084] Viscoelastic mechanical properties: This refers to the mechanical behavior of neonatal abdominal wall tissue, which has both elasticity (recovery after deformation) and viscosity (deformation changes over time). It is described by the Kelvin-Voigt model, which quantifies the stress-strain relationship of the tissue under load through elastic modulus, viscosity coefficient, and initial stress offset.

[0085] Fluid dynamics: The flow characteristics of digitostomy feces, including flow velocity, pressure distribution, and interaction with changes in body position, are modeled using the Navier-Stokes equations, combining density, viscosity, and gravity to simulate fecal dynamics.

[0086] Gradual warning mechanism: Based on the risk level of dangerous operations (mild, moderate, high risk), tiered intervention is achieved through color coding (yellow / orange / red), sound prompts and operation blocking strategies (such as limiting pressure application). The specific threshold is determined by neonatal skin tolerance test data, such as the ISO 10993 biocompatibility standard.

[0087] The above description is based on the ideal embodiment of the present invention. Based on the above description, relevant personnel can make various changes and modifications without departing from the technical scope of this invention. The technical scope of this invention is not limited to the content of the specification and must be determined according to the scope of the claims.

Claims

1. A virtual simulation operating system for neonatal enterostomy care, characterized in that: include: Biomechanical simulation module, dynamic physiological simulation module, and evaluation and data management module; The biomechanical simulation module is used to simulate the viscoelastic mechanical properties of neonatal abdominal wall tissue and the fluid dynamics behavior of intestinal stoma excreta in real time; The dynamic physiological simulation module generates a quantitative score by integrating the standardization of operation, complication prevention effect and injury incidence, and maps the score results to the three-dimensional virtual scene in real time; The evaluation and data management module builds a seamless connection system for hospital-community-home care scenarios, supports the continued execution of care plans across scenarios and data synchronization, and has a built-in dynamic evolution prediction model for complications, which can deduce the probability and severity of stoma stenosis and skin damage in real time based on the quality of nursing operations.

2. The virtual simulation operating system for neonatal enterostomy care according to claim 1, characterized in that: The formula for the viscoelastic mechanical properties is: Where E is the elastic modulus, ranging from 0.8 to 1.2 MPa, and is dynamically adjusted according to the newborn's age. is the viscosity coefficient, ranging from 0.5 to 0.8 Pa·s, and is negatively correlated with the ostomy bag sticking time t; is the initial stress offset, calculated from the newborn's weight G, =0.1G+0.

5.

3. The virtual simulation operating system for neonatal enterostomy care according to claim 2, characterized in that: The fluid dynamics behavior is expressed as follows: Where, is the density of excrement, ranging from 1000 to 1050 kg / m 3 , dynamically adjusted according to feeding type; is viscosity, ranging from 0.001 to 0.002 Pa·s, and is positively correlated with the frequency of intestinal motility f; It is the combined force of gravity and changes in body position.

4. The virtual simulation operating system for neonatal enterostomy care according to claim 1, characterized in that: When the dynamic physiological simulation module detects dangerous operations, the graded warning mechanism is automatically activated; the dangerous operations include: disinfection operation timeout or abnormal contact area, instrument pressure exceeding the newborn skin tolerance threshold, and the ostomy device sealing showing a gradual deterioration trend.

5. The virtual simulation operating system for neonatal enterostomy care according to claim 1, characterized in that: The formula for the quantitative score is: Where A is the accuracy of the operation steps; L is the leakage rate; S is the proportion of skin injury area; I is the incidence of device-related pressure injuries; 、 、 、 are all weight coefficients.

6. The virtual simulation operating system for neonatal enterostomy care according to claim 3, characterized in that: The biomechanical simulation module integrates force feedback, displacement tracking, and temperature sensing data using an adaptive filtering algorithm to eliminate interference noise between the data and dynamically adjusts the parameters of the biomechanical simulation module based on the real-time physiological state of the newborn; The formula expression of the filtering algorithm is: Where, is the estimated value of the state at time k, that is, based on the current measurement value and the predicted value at the previous moment ; is the Kalman gain; is the observation value; H is the observation matrix; J is the unit matrix.

7. The virtual simulation operating system for neonatal enterostomy care according to claim 6, characterized in that: The correction rules for dynamically correcting the simulation model parameters according to the real-time physiological state of the newborn include: The elastic modulus E is dynamically adjusted with the newborn's age M. The adjustment formula is: =E+0.1M (M≤6 months), =E+0.05M (M>6 months); Viscosity coefficient Dynamic adjustment with intestinal peristalsis frequency f, the adjustment formula is: = +0.0001f.

8. The virtual simulation operating system for neonatal enterostomy care according to claim 1, characterized in that: The virtual simulation operating system also includes building a four-dimensional evaluation system to comprehensively evaluate the training effect from the perspectives of trainees' subjective feedback, knowledge mastery, operational skill transfer, and clinical outcome improvement. The calculation model of the four-dimensional evaluation system is: 。 9. The virtual simulation operating system for neonatal enterostomy care according to claim 1, characterized in that: The formula expression of the complication dynamic evolution prediction model is: ； Where m is the reaction rate constant; C is the current evolution state; It is a state of historical evolution; is the attenuation coefficient; t is the unit time.

Images