Method for arthroscopic tissue damage warning based on temperature-pressure coupling damage development model

Abstract

The present application relates to the technical field of medical data processing, and provides a method for arthroscopic tissue damage early warning based on a temperature-pressure coupling damage development model, which comprises the following steps: acquiring and preprocessing pressure data and temperature data of the front end of an arthroscopic sheath, and extracting pressure characteristic parameters and temperature characteristic parameters; calculating mechanical damage risk values and thermal damage risk values according to the pressure characteristic parameters and the temperature characteristic parameters respectively; calculating a current tissue risk value according to the mechanical damage risk values and the thermal damage risk values, establishing a risk memory model according to the current tissue risk value, and calculating a historical tissue risk value; predicting a future risk state of the tissue according to the current tissue risk value and the historical tissue risk value, calculating a final risk index, determining a risk grade based on the final risk index, and outputting corresponding alarm information. The present application can more truly reflect the stress and heat state of the tissue, evaluate cumulative thermal damage, reflect the influence of recovery and continuation, and improve the safety of surgery.

Description

Technical Field

[0001] This invention relates to the field of medical data processing technology, and in particular to a method for early warning of arthroscopic tissue damage based on a thermo-pressure coupling damage development model. Background Technology

[0002] During arthroscopic surgery, the arthroscopic sheath serves as a crucial instrument for establishing the surgical access, and its operational status directly impacts the joint cavity environment and tissue safety. Continuous infusion of saline solution is typically required during the procedure to maintain joint cavity expansion and a clear surgical field. Radiofrequency ablation and other energy-based devices, such as electrodes, may also be used for tissue manipulation. Therefore, changes in joint cavity pressure and local tissue temperature are significant factors affecting surgical safety.

[0003] Pressure monitoring in existing arthroscopic systems typically obtains pressure information through infusion pumps, fluid lines, or external pressure sensors. Since the pressure sensor is usually located some distance from the tissue-operated area at the tip of the arthroscopic sheath, the results are easily affected by tubing resistance, changes in infusion flow rate, and backflow conditions, making it difficult to fully reflect the actual pressure state experienced by the tissue.

[0004] Existing temperature detection solutions are typically used to detect the operating temperature of equipment or the local temperature status. However, during the operation of energy devices such as radiofrequency ablation, there are differences between the electrode surface temperature, the perfusion fluid temperature, and the actual tissue temperature. Temperature detection results from a single location are difficult to accurately reflect the thermal effects on the tissue.

[0005] In addition, existing risk assessment methods typically employ fixed threshold comparison methods, such as triggering an alarm when the pressure exceeds a preset pressure threshold or when the temperature exceeds a preset temperature threshold.

[0006] However, the risk of tissue damage during arthroscopic surgery depends not only on the current pressure or temperature values, but also on the trend of parameter changes and the duration of action. For example, a rapid increase in pressure may indicate abnormal joint cavity reflux; a rapid increase in temperature may indicate local energy concentration; and prolonged exposure to temperatures exceeding safe limits may lead to cumulative thermal damage.

[0007] Furthermore, there is a mutual influence between abnormal pressure and abnormal temperature. When joint cavity pressure increases, it may reduce the local fluid exchange and heat dissipation capacity of the tissue, causing the tissue temperature to rise further under the same energy conditions. Therefore, judging pressure and temperature independently cannot fully reflect the tissue damage risk caused by the combined effects of mechanical pressure and thermal effects.

[0008] Furthermore, tissue damage exhibits a certain degree of cumulative and delayed effects. Even after pressure or temperature parameters return to normal, the tissue condition does not immediately return to a safe state. If the alarm system relies solely on current detection values, it may prematurely deactivate the alarm before the risk has been eliminated, leading to the overlooking of potential hazards.

[0009] Therefore, a comprehensive risk assessment method based on pressure, temperature, trends, and historical processes is needed to enable early identification and continuous warning of the risks of nerve compression and tissue thermal damage during arthroscopic surgery. Summary of the Invention

[0010] To address the above issues, this invention collects pressure and temperature data in real time at the tip of the arthroscopic sheath. It integrates characteristics such as the rate of change, duration, and heat accumulation, and introduces a heat accumulation calculation and risk memory model. This model causes risk values ​​to decay over time, and integrates current, historical, and predicted risks to construct a risk index. Employing a multi-level alarm mechanism, it can more realistically reflect the stress and heat state of tissues, assess cumulative thermal damage and reflect the impact of recovery progression, predict risk trends in advance, and facilitate timely adjustments by the surgeon, thereby reducing the risk of injury and improving surgical safety.

[0011] According to embodiments of the present invention, a method for early warning of arthroscopic tissue damage based on a thermo-baric coupling damage development model is provided.

[0012] In a first aspect of the invention, a method for arthroscopic tissue damage early warning based on a thermo-baric coupling damage development model is provided. The method includes:

[0013] Step S01: Obtain pressure and temperature data at the tip of the arthroscopic sheath and preprocess them to extract pressure and temperature characteristic parameters;

[0014] Step S02: Calculate the mechanical damage risk value and the thermal damage risk value based on the pressure characteristic parameters and temperature characteristic parameters, respectively;

[0015] Step S03: Calculate the current risk value of the tissue based on the mechanical damage risk value and the thermal damage risk value, establish a risk memory model based on the current risk value of the tissue, and calculate the historical risk value of the tissue;

[0016] Step S04: Predict the organization's future risk status based on the organization's current risk value and historical risk value, calculate the final risk index, determine the risk level based on the final risk index, and output the corresponding alarm information.

[0017] Furthermore, the pressure characteristic parameters include peak pressure, instantaneous pressure change, and duration of continuous pressure application; the temperature characteristic parameters include maximum temperature, instantaneous temperature change, and duration of temperature application.

[0018] Furthermore, the formula for calculating the mechanical damage risk value mentioned in step S02 is as follows:

[0019] ,in, , , These are the weighting coefficients. and ;

[0020] Pressure intensity; This represents the rate of change of pressure. Rate the duration of stress.

[0021] Furthermore, the formula for calculating the thermal damage risk value mentioned in step S02 is as follows: ,in, , , These are the weighting coefficients. and ; For temperature risk scoring, For the rate of temperature change, For the risk score of heat accumulation exposure, This represents the cumulative amount of heat.

[0022] Furthermore, the specific steps of step S03 are as follows:

[0023] The organization's current risk value is calculated using a complementary probability fusion approach. ;

[0024] The continuous-time model of the organizational risk recovery process is represented as follows:

[0025] ,

[0026] Discretizing the above continuous-time model yields:

[0027] ,

[0028] make:

[0029] ,

[0030] but:

[0031] ,

[0032] in, This indicates the organization's historical risk value at the current moment; This indicates the historical risk value organized at the previous sampling time; This indicates the current risk value of the organization at the current moment; This represents the risk memory decay coefficient.

[0033] Furthermore, the formula for calculating the final risk index mentioned in step S04 is as follows: ,in, , , These are the weighting coefficients. ,and ; Indicates future forecast risks; Indicates the current risk value; Indicates the lead time for prediction; This indicates the risk growth rate.

[0034] Furthermore, the risk level determination based on the final risk index described in step S04 specifically includes: This is then considered a normal state. This is then classified as a Level 1 alarm. This is then classified as a Level 2 alarm. This is then classified as a Level 3 high-risk alarm. , , Based on the final risk index The correspondence between the actual organizational state and the actual organizational state is determined.

[0035] In a second aspect of the invention, an arthroscopic tissue damage early warning device based on a thermo-barotropic damage development model is provided. The device includes:

[0036] Feature parameter extraction module: used to acquire and preprocess pressure and temperature data at the tip of the arthroscopic sheath, and extract pressure and temperature feature parameters;

[0037] Damage risk calculation module: used to calculate mechanical damage risk value and thermal damage risk value based on pressure characteristic parameters and temperature characteristic parameters, respectively;

[0038] Historical risk value calculation module: used to calculate the current risk value of the tissue based on the mechanical damage risk value and the thermal damage risk value, establish a risk memory model based on the current risk value of the tissue, and calculate the historical risk value of the tissue;

[0039] Risk Index Calculation Module: This module is used to predict the organization's future risk status based on its current and historical risk values, calculate the final risk index, determine the risk level based on the final risk index, and output corresponding alarm information.

[0040] In a third aspect of the invention, an electronic device is provided. The electronic device includes a memory and a processor, the memory storing a computer program, the processor executing the program to implement the method according to a first aspect of the invention.

[0041] In a fourth aspect of the invention, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the method according to a first aspect of the invention.

[0042] This invention collects pressure and temperature data in real time at the tip of the arthroscopic sheath, and integrates features such as the rate of change, duration, and heat accumulation. It introduces a heat accumulation calculation and risk memory model, which causes the risk value to decay according to a time constant. It integrates current, historical, and predicted risks to construct a risk index and adopts a multi-level alarm mechanism. This can more realistically reflect the stress and heat state of tissues, assess cumulative thermal damage and reflect the impact of recovery and continuation, predict risk trends in advance, and facilitate timely adjustments by the surgeon, thereby reducing the risk of injury and improving surgical safety.

[0043] It should be understood that the description in the Summary of the Invention is not intended to limit the key or essential features of the embodiments of the present invention, nor is it intended to restrict the scope of the invention. Other features of the invention will become readily apparent from the following description.

[0044] The beneficial effects of this invention are:

[0045] 1. By collecting pressure and temperature data in real time at the tip of the arthroscopic sheath and combining them with multi-dimensional features such as pressure change rate, pressure duration, temperature change rate, and heat accumulation for comprehensive analysis, compared with alarms based on a single pressure or temperature threshold, this method can more realistically reflect the stress and heat state of tissues and improve the accuracy of risk identification.

[0046] 2. By introducing the calculation of heat accumulation and incorporating the duration of temperature exposure into the risk assessment process, even if the instantaneous temperature does not reach the highest risk temperature, the risk value of thermal damage can still be increased when the tissue is in a high temperature environment for a long time. This enables the assessment of cumulative thermal damage to the tissue and improves the early warning capability of thermal damage.

[0047] 3. Establish a risk memory model. After the pressure or temperature returns to normal, the risk value gradually decays according to the recovery time constant, rather than being immediately cleared to zero. This allows the risk assessment to reflect the continued impact during the organization's recovery process and improves the consistency between the risk judgment results and the organization's actual state.

[0048] 4. Further integrate current risks, historical risks, and risk prediction results to construct a final risk index, which can not only reflect the current state of the organization, but also predict risk change trends in advance, and achieve early warning in arthroscopic surgery;

[0049] 5. A multi-level risk alarm mechanism is adopted, which outputs different levels of early warning information based on the final risk index, so that the surgeon can adjust the perfusion pressure, heat source power or operation mode in a timely manner, thereby reducing the risk of mechanical and thermal damage to tissues and improving the safety of arthroscopic surgery. Attached Figure Description

[0050] The above and other features, advantages, and aspects of the various embodiments of the present invention will become more apparent from the accompanying drawings and the following detailed description. Wherein:

[0051] Figure 1 A flowchart of an arthroscopic tissue damage early warning method based on a thermo-baric coupling damage development model according to an embodiment of the present invention is shown;

[0052] Figure 2 The original images of Group A test prior to exposure are shown according to an embodiment of the present invention;

[0053] Figure 3 The original image is shown immediately after the Group A test according to an embodiment of the present invention was stopped;

[0054] Figure 4 The original image is shown 5 minutes after the Group A test was stopped according to an embodiment of the present invention;

[0055] Figure 5 The original images of Group B test prior to exposure are shown according to an embodiment of the present invention;

[0056] Figure 6 The original image is shown immediately after the Group B test was stopped, according to an embodiment of the present invention.

[0057] Figure 7 The original image is shown 5 minutes after the Group B test was stopped, according to an embodiment of the present invention.

[0058] Figure 8 The original images of Group C test prior to exposure are shown according to an embodiment of the present invention;

[0059] Figure 9 The original image is shown immediately after the C group test was stopped according to an embodiment of the present invention;

[0060] Figure 10 The original image is shown 5 minutes after the C group test was stopped according to an embodiment of the present invention;

[0061] Figure 11 A block diagram of an arthroscopic tissue damage early warning device based on a thermo-baric coupling damage development model according to an embodiment of the present invention is shown.

[0062] Figure 12A schematic diagram of an arthroscopic tissue damage early warning device based on a thermo-pressure coupling damage development model according to an embodiment of the present invention is shown. Detailed Implementation

[0063] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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 are within the scope of protection of the present invention.

[0064] According to an embodiment of the present invention, a method for early warning of arthroscopic tissue damage based on a thermo-pressure coupled damage development model is proposed. By collecting pressure and temperature in real time at the tip of the arthroscopic sheath, and integrating their rate of change, duration, and heat accumulation, a heat accumulation calculation and risk memory model is introduced to make the risk value decay according to the time constant. A risk index is constructed by integrating current, historical, and predicted risks, and a multi-level alarm mechanism is adopted. This method can more realistically reflect the stress and heat state of tissue, assess cumulative thermal damage and reflect the impact of recovery and continuation, predict risk trends in advance, and facilitate timely adjustments by the surgeon, thereby reducing the risk of damage and improving surgical safety.

[0065] The principles and spirit of the present invention will be explained in detail below with reference to several representative embodiments.

[0066] Figure 1 This is a schematic flowchart of a method for arthroscopic tissue damage early warning based on a thermo-baric coupling damage development model according to an embodiment of the present invention. The method includes:

[0067] Step S01: Obtain pressure and temperature data at the tip of the arthroscopic sheath and preprocess them to extract pressure and temperature characteristic parameters;

[0068] Step S02: Calculate the mechanical damage risk value and the thermal damage risk value based on the pressure characteristic parameters and temperature characteristic parameters, respectively;

[0069] Step S03: Calculate the current risk value of the tissue based on the mechanical damage risk value and the thermal damage risk value, establish a risk memory model based on the current risk value of the tissue, and calculate the historical risk value of the tissue;

[0070] Step S04: Predict the organization's future risk status based on the organization's current risk value and historical risk value, calculate the final risk index, determine the risk level based on the final risk index, and output the corresponding alarm information.

[0071] It should be noted that although the operation of the method of the present invention has been described in a specific order in the above embodiments and figures, this does not require or imply that the operations must be performed in that specific order, or that all the operations shown must be performed to achieve the desired result. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps.

[0072] To provide a clearer explanation of the arthroscopic tissue damage early warning method based on the thermo-pressure coupling damage development model, a specific embodiment is described below. However, it is worth noting that this embodiment is only for better illustrating the present invention and does not constitute an improper limitation of the present invention.

[0073] The following example will further illustrate the method of arthroscopic tissue damage early warning based on the thermo-baric coupling damage development model.

[0074] Step S01: Obtain pressure and temperature data at the tip of the arthroscopic sheath and preprocess them to extract pressure and temperature characteristic parameters.

[0075] In this embodiment, a flexible pressure sensor and a temperature sensor are installed at the tip of the arthroscopic sheath to collect pressure and temperature data in the tissue contact area at the tip of the sheath in real time. The pressure sensor outputs raw pressure data. The temperature sensor outputs raw temperature data. .

[0076] Since the sensor data acquisition process is easily affected by the flow of the perfusion fluid, the movement of the sheath, and environmental noise, the pressure and temperature data are first processed by moving average filtering to reduce random noise and improve the stability of subsequent risk calculations.

[0077] The formula for calculating pressure data filtering is:

[0078] ,

[0079] The formula for temperature data filtering is:

[0080] ,

[0081] in, For the first Counting backwards from the sampling point One sampling point, Sampling frequency, For window time.

[0082] After filtering, pressure characteristic parameters are extracted from the filtered pressure data, including pressure peak value, instantaneous pressure change value and continuous pressure time.

[0083] Among them, the peak pressure This represents the maximum pressure value within the current sampling period; the instantaneous pressure change value is used to characterize the rate of pressure change, and its calculation formula is:

[0084] ,

[0085] Duration of pressure This indicates the cumulative duration of pressure exceeding the set reference pressure.

[0086] Meanwhile, temperature feature parameters are extracted from the filtered temperature data, including the highest temperature, instantaneous temperature change value, and temperature duration.

[0087] Among them, the highest temperature This represents the maximum temperature value within the current sampling period; the instantaneous temperature change value is used to characterize the rate of temperature change, and its calculation formula is:

[0088] ,

[0089] Temperature duration This indicates the cumulative duration during which the temperature remains above the set reference temperature.

[0090] Step S02: Calculate the mechanical damage risk value and the thermal damage risk value based on the pressure characteristic parameters and temperature characteristic parameters, respectively.

[0091] Raw pressure information is converted into quantifiable mechanical damage risk. Through pressure calculation, the risk of tissue damage from mechanical compression, edema, fluid extravasation, and nerve compression is assessed. The main sources of mechanical damage risk are: excessively high pressure; rapid pressure changes; and prolonged periods of high pressure.

[0092] To eliminate the influence of the dimensions of each parameter, the pressure characteristic parameters are normalized:

[0093] Pressure intensity: ,

[0094] Pressure change rate: ,

[0095] Stress duration rating:

[0096] in, Indicates low-risk reference pressure. Indicates high-risk reference pressure. Indicates the threshold of the rate of change of pressure. This indicates the threshold for sustained pressure duration. The function is used to restrict the calculation result to the range of 0 to 1.

[0097] In the pressure gradient test, no obvious indentation, swelling or fluid leakage was observed in many groups of samples when the pressure was higher than this. Therefore, 120 mmHg was used as a high-risk reference pressure in this example.

[0098] In the pressure gradient test, no obvious indentation, swelling or fluid leakage was observed in multiple groups of samples when the pressure was lower than this. Therefore, 40 mmHg was used as a low-risk reference pressure in this embodiment.

[0099] In this embodiment, , , , The calibration method is as follows.

[0100] First, the tissue damage score is converted into a four-level scale of 0, 1, 2, and 3, which is 0, 0.25, 0.5, 0.75, and 1. Among them, 0 means no obvious abnormalities, no obvious extravasation, swelling, discoloration, or elasticity changes, and is considered basically safe; 1 means slight extravasation, slight swelling, slight discoloration, or elasticity changes, and is considered to require attention; 2 means relatively obvious extravasation or heat-affected areas, with visible changes in tissue condition, and is considered to have significant risk; 3 means obvious abnormalities, or reaching the preset stop conditions of the test, and is considered to have high risk.

[0101] A pressure gradient test was conducted, with the rate of pressure change and duration of application fixed, while only the pressure magnitude was varied. Multiple participants scored the test, and the scores were averaged to obtain the average actual score, as shown in Table 1.

[0102] Table 1

[0103] pressure (mmHg) Average actual rating Organizational performance 20 0.05 No significant changes 40 0.18 There were virtually no obvious indentations, swelling, or extravasation. 60 0.32 Slight changes began to appear 80 0.48 Moderate change 100 0.63 Obvious changes 120 0.78 Entering high-risk areas

[0104] Therefore, when the pressure is 40 mmHg, =0.18 < 0.25, still within the low-risk zone; while at a pressure of 60 mmHg, =0.32>0.25, indicating an increasing risk. =40mmHg; when the pressure reaches 120mmHg: =0.78≥0.75, therefore =120mmHg.

[0105] Perform a pressure surge test, for example, increasing the pressure from 40 mmHg to 100 mmHg, with a pressure change of 60 mmHg. Change the rise time to obtain different pressure change rates, as shown in Table 2.

[0106] Table 2

[0107] Ascent time ( ) / s Pressure change rate ( ) / mmHg / s Average actual rating 4.0 15 0.18 2.0 30 0.32 1.5 40 0.46 1.0 60 0.76 0.5 120 0.90

[0108] when =1s, =60mmHg / s, at this time =0.76, which is close to the area of ​​obvious risk, so =60mmHg / s.

[0109] Perform a continuous pressure test, maintaining the pressure at more than The effect of duration on tissue damage was observed at medium to high pressure levels. An intermediate value of 80 mmHg was used, as shown in Table 3.

[0110] Table 3

[0111] Duration ( ) / s Average actual rating 10 0.15 30 0.38 45 0.56 60 0.75 90 0.86 120 0.94

[0112] Duration reached =60, =0.75, so =60S is used as the typical setting.

[0113] After completing the above three scoring calculations, the formula for calculating the mechanical damage risk value is:

[0114] ,

[0115] in, , , These are the weighting coefficients. ,and .

[0116] To determine the weights of each scoring item, methods such as expert evaluation, in vitro tissue experiments, animal experiments, multiple regression analysis, or machine learning training were used. This embodiment is based on a pressure test conducted on in vitro tissues, and the experimental data are shown in Table 4.

[0117] Table 4

[0118] serial number (mmHg) (mmHg / s) (s) 1 56 6 6 2 72 12 12 3 88 18 24 4 104 24 36 5 120 30 48 6 96 48 18 7 64 42 54 8 112 12 60

[0119] The calculated scores for pressure intensity, pressure change rate, and pressure duration are shown in Table 5.

[0120] Table 5

[0121] serial number 1 0.2 0.1 0.1 0.61 2 0.4 0.2 0.2 1.22 3 0.6 0.3 0.4 1.96 4 0.8 0.4 0.6 2.7 5 1 0.5 0.8 3.44 6 0.7 0.8 0.3 2.34 7 0.3 0.7 0.9 2.22 8 0.9 0.2 1 3.31

[0122] A least squares fitting model was established based on the experimental data: , The results were obtained through average evaluations from multiple participants.

[0123] Adjustment , , To minimize the sum of squared errors for all groups, let the matrix be...

[0124] Y= ,

[0125] Using the least squares method, calculate The fitting yielded: =2.1, =0.6, =1.3, at this point, then... , , Converted proportionally to mechanical risk weights: =2.10 / 4.00=0.525, =0.60 / 4.00=0.150, =1.30 / 4.00=0.325.

[0126] Therefore, the mechanical risk is calculated as follows: The results indicate that, in this set of embodiments, pressure level has the greatest impact, followed by duration of pressure application, and then rate of pressure change.

[0127] Raw temperature information is converted into quantifiable thermal damage risk. Temperature calculations are used to assess the risk of tissue thermal damage caused by heat sources such as radiofrequency ablation and plasma ablation. Thermal damage mainly originates from: excessively high temperatures; excessively rapid heating rates; excessively long durations of high temperatures; and excessive heat accumulation.

[0128] To eliminate the influence of the dimensions of each parameter, the temperature characteristic parameters are normalized:

[0129] Temperature risk score: ,

[0130] Rate of temperature change: ,

[0131] Heat accumulation exposure risk score: , , ,in, Indicates a low-risk reference temperature. Indicates a high-risk reference temperature. Indicates the threshold of the rate of temperature change. Indicates the threshold for heat accumulation. The source of this is the idea that temperature and time together determine the effects of heat. The risk of tissue damage from heat cannot be determined solely by the temperature at a single moment; it is also necessary to consider how long the high temperature lasts. The risks of short-term temperature fluctuations differ from those of prolonged exposure to moderate temperatures.

[0132] This is a low-risk reference temperature. In arthroscopic radiofrequency ablation literature, a joint fluid temperature above 43°C is often used as a warning value for potential tissue damage risk. =43℃;

[0133] This is a high-risk reference temperature. Literature generally considers soft tissue coagulation to be related to protein denaturation, with the coagulation / denaturation temperature range commonly between 60 and 100°C. Electrosurgical data also frequently describe 60–90°C as a drying and coagulation-related temperature range, therefore it is considered... =60℃.

[0134] The formula for calculating the risk value of thermal damage is:

[0135] ,

[0136] in, , , These are the weighting coefficients. ,and .

[0137] To determine the weights of each scoring item, methods such as expert evaluation, in vitro tissue experiments, animal experiments, multiple regression analysis, or machine learning training were used. This embodiment is based on a temperature treatment experiment using in vitro tissues, and the experimental data are shown in Table 6.

[0138] Table 6

[0139] serial number 1 42 0.4 12 2 46 0.8 24 3 50 1.2 48 4 54 1.6 60 5 56 2.0 96 6 52 3.2 36 7 48 2.8 108 8 58 0.8 84

[0140] The calculated scores for temperature, rate of temperature change, and heat accumulation are shown in Table 7.

[0141] Table 7

[0142] serial number 1 0.10 0.10 0.10 0.45 2 0.30 0.20 0.20 1.06 3 0.50 0.30 0.40 1.91 4 0.70 0.40 0.50 2.52 5 0.80 0.50 0.80 3.45 6 0.60 0.80 0.30 2.08 7 0.3 0.70 0.90 3.15 8 0.9 0.20 0.70 3.22

[0143] Establish the fitting relationship for the thermal effect experiment: .

[0144] Minimum error fitting obtained , , The method is the same as that for pressure fitting, and will not be repeated here.

[0145] In the data from the above embodiments, the fitting results are as follows: =1.60, =0.50, =2.40.

[0146] Then , , Converted proportionally to thermal risk weights: =1.60 / 4.50=0.356, =0.50 / 4.50=0.111, =2.40 / 4.50=0.533, therefore the thermal risk is calculated as follows: .

[0147] Step S03: Calculate the current risk value of the tissue based on the mechanical damage risk value and the thermal damage risk value, establish a risk memory model based on the current risk value of the tissue, and calculate the historical risk value of the tissue.

[0148] Since mechanical damage and thermal damage may coexist, the overall tissue risk is not simply a linear sum when both risks increase simultaneously. Therefore, this embodiment uses a complementary probability fusion method to calculate the current tissue risk value, and the calculation formula is as follows:

[0149] ,

[0150] In this formula, when the mechanical damage risk value or thermal damage risk value When any one of them increases, All of them rise accordingly; when and When both risks increase simultaneously, they have a cumulative effect, making... Further increases.

[0151] Furthermore, since tissues undergo a certain recovery process even after the external stimulus stops when subjected to pressure or heat, the current risk value alone cannot reflect the lingering effects of tissue damage.

[0152] Therefore, this embodiment is based on the organization's current risk value. Establish a risk memory model to dynamically accumulate the organization's historical risks.

[0153] The organizational risk recovery process conforms to a first-order dynamic system, and its continuous-time model is expressed as:

[0154] ,

[0155] Discretizing the above continuous-time model yields:

[0156] ,

[0157] make:

[0158] ,

[0159] Then we get:

[0160] ,

[0161] in, This indicates the organization's historical risk value at the current moment; This indicates the historical risk value organized at the previous sampling time; This indicates the current risk value of the organization at the current moment; This represents the risk memory decay coefficient.

[0162] To determine the recovery time constant Calibration was performed using an in vitro tissue recovery experiment.

[0163] Specifically, six groups of ex vivo tissue samples were selected and continuously stimulated for 60 seconds at a perfusion pressure of approximately 120 mmHg and a perfusion fluid temperature of approximately 43°C. After stimulation, the perfusion pressure was restored to approximately 40 mmHg and the perfusion fluid temperature was restored to approximately 37°C. The risk memory value during tissue recovery was continuously observed. The decay process.

[0164] =1 indicates that the moment stimulation stops, the situation is at a high risk. =0 indicates that the pressure and temperature have returned to normal; at this time, the risk memory value decays according to the following formula: The continuous form is equivalent to: , hour, That is, the ideal situation.

[0165] The tissue recovery experimental data are shown in Table 8. Six repeated experiments were performed using six ex vivo soft tissue samples. First, a period of high pressure / high temperature stimulation was applied to the tissue; then, the pressure and temperature were restored to normal levels. The decay of the tissue's risk status over time was observed. The recovery curve was fitted using an exponential decay model; the time constant obtained from the fitting was... .

[0166] The evaluation criteria were as follows: 0 points for recovery to the normal baseline; 1 point for slight residual risk; 2 points for moderate residual risk; 3 points for significant residual risk; and 4 points for being close to the moment stimulation was stopped.

[0167] Table 8

[0168] Recovery time / s Group 1 Group 2 Group 3 Group 4 Group 5 Group 6 average value 0 1.000 1.000 1.000 1.000 1.000 1.000 1.000 2 0.825 0.812 0.821 0.816 0.805 0.833 0.819 4 0.636 0.686 0.705 0.663 0.685 0.669 0.674 6 0.519 0.553 0.568 0.549 0.576 0.548 0.552 8 0.407 0.456 0.474 0.462 0.465 0.451 0.453 10 0.382 0.373 0.395 0.373 0.405 0.350 0.380 12 0.307 0.325 0.325 0.256 0.322 0.316 0.309 15 0.236 0.223 0.240 0.230 0.246 0.224 0.233 20 0.134 0.139 0.160 0.107 0.142 0.125 0.135 25 0.086 0.091 0.083 0.057 0.095 0.091 0.084 30 0.030 0.033 0.055 0.048 0.053 0.053 0.045

[0169] According to the table, the value at 10 seconds is close to the theoretical value of 0.368, so it can be considered that... ≈10s.

[0170] therefore, ,in, It is the sampling time interval, set to 0.1, so 0.99, then .

[0171] Through the risk memory model described above, even if the current pressure or temperature returns to normal, the tissue's historical risk will gradually decrease over time rather than immediately returning to zero, thus reflecting the lasting impact of tissue damage.

[0172] Step S04: Predict the organization's future risk status based on the organization's current risk value and historical risk value, calculate the final risk index, determine the risk level based on the final risk index, and output the corresponding alarm information.

[0173] First, calculate the risk growth rate based on the changes in the organization's current risk value over a continuous period of time. The calculation formula is as follows:

[0174] ,

[0175] in, Indicates the risk growth rate; This represents the current risk value at the last point of the forecast window; This represents the current risk value at the first point of the prediction window; , These represent the corresponding times.

[0176] Predicting future risks based on the risk growth rate:

[0177] ,

[0178] in, Indicates future forecast risks; Indicates the current risk value; This indicates the lead time for prediction, which is set according to the desired advance alarm time.

[0179] For example: within the last 5 seconds: ,but: ,when: hour, .

[0180] Establish a final risk index:

[0181] ,

[0182] in, , , These are the weighting coefficients. ,and .

[0183] In this embodiment, different tissue states are considered. , and Conduct combined experiments.

[0184] The experimental data for predicting organizational risk trends are shown in Table 9.

[0185] Table 9

[0186] serial number 1 0.20 0.15 0.25 0.875 2 0.35 0.25 0.40 1.56 3 0.50 0.40 0.55 2.155 4 0.65 0.55 0.70 2.83 5 0.80 0.70 0.85 3.505 6 0.70 0.35 0.90 2.84 7 0.45 0.75 0.60 2.58 8 0.90 0.60 1.00 3.72

[0187] Based on experimental data, ,

[0188] in: Indicates the actual damage score of the tissue; , , These represent the impact coefficients corresponding to current risk, historical risk, and predicted risk, respectively.

[0189] By fitting using the least squares method, we obtain: =2.2, =1.4, =0.9, calculated as follows =2.20 / 4.50=0.489, =1.40 / 4.50=0.311, =0.90 / 4.50=0.200.

[0190] Furthermore, based on the final risk index Conduct risk level assessment.

[0191] Normal state: ;

[0192] Level 1 alarm: ;

[0193] Level 2 alarm: ;

[0194] Level 3 High Risk Alert: ;

[0195] Alarm types include audible alarms, visual alarms, and visual alarms.

[0196] In this embodiment, , , It is determined by the final risk index The correspondence between the actual organizational state and the actual organizational state is determined as shown in Table 10-11:

[0197] Table 10

[0198] Actual organizational status distributed Boundary basis No obvious damage was observed. 0.12～0.34 The highest value for normal samples is approximately 0.34. Mild risk 0.36～0.58 The sample size for mild risk primarily starts from 0.36. Moderate risk 0.62～0.78 The medium-risk sample mainly starts from 0.62. High risk 0.82～0.95 High-risk samples mainly have a risk level of 0.82 or higher.

[0199] Table 11

[0200] Alarm status Judgment conditions Common Explanation normal The risk is at a low level. Level 1 warning A mild risk has been identified, and attention is required. Level 2 alarm The risk has increased, and measures need to be taken. Level 3 High Risk Alert High-risk situations require strong alerts or coordinated control measures.

[0201] Can be taken =0.35, =0.6, =0.8.

[0202] The above methods enable real-time risk assessment, risk memory, and early warning of the tissue condition in the arthroscopic sheath's action area.

[0203] To verify the effectiveness of the present invention, this embodiment also involves relevant experiments, as shown in Table 12:

[0204] Table 12

[0205] Group A 1 No intervention, continue to maintain the same water pressure and electrode operation. A clearly visible change occurs, or an alarm is triggered within a maximum of 20 seconds. Confirmed that the current conditions can cause visible damage. Comparison with pre-exposure images Group B 1 Immediately stop the electrodes and remove the mirror sheath after the alarm is confirmed. Predicting alarm timing Verify whether timely response can reduce damage Compared with Group C Group C 1 Ignore alarms and continue the same operation for 10.0s. 10.0 seconds after the alarm is triggered Verify the subsequent risks corresponding to the alarm. Compared with Group B

[0206] The samples used were three independent samples of fresh, isolated pig muscle from the same source and from similar locations. Each sample was exposed only once, and the same area was not allowed to be used repeatedly for different groups; if the results of the three samples were consistent, they could be used as engineering visualization verification.

[0207] The test equipment and fixtures are as follows:

[0208] Subject mirror sheath, temperature / pressure sensor and early warning algorithm data acquisition system;

[0209] Injection fluid, injection pump or liquid bag height adjustment device, and water circuit pressure display or reference pressure gauge;

[0210] The electrodes and energy platform to be used, the electrode mode and power remain unchanged in the formal test;

[0211] The timing device is used to record the predicted alarm time, the stop time, and the duration of Group C continuing to work for 10 seconds.

[0212] Rapid preliminary experiments were conducted using non-overlapping areas from the same pig thigh. Each area was exposed only once and not included in the formal sample. The purpose was solely to determine a set of fixed conditions that could produce clear image differences. A target perfusion water pressure was first set and kept stable in subsequent preliminary experiments. The proposed electrode mode and power were used, and the predicted alarm time was recorded. It was observed whether there was no clear image damage when the experiment was stopped immediately after the alarm, and whether a clear visible change appeared after continuing for 10 seconds. If the difference was unclear, only one variable was adjusted (prioritizing electrode power or single-point application mode). Once determined, all parameters were frozen.

[0213] The sample was restored to a state similar to its initial temperature and humidity; pre-exposure photographs were taken at the same shooting location. Temperature, water pressure, and algorithm recording were initiated, with baseline recording lasting at least 5 seconds.

[0214] Establish the preset injection water pressure and start the electrode according to the mode, position and operation method.

[0215] Group A: System alarms are only recorded, and operation is not stopped; continue until a visible change of level 2 or above occurs, or until 20 seconds after the alarm is triggered / the preset maximum safe time is reached; stop immediately and remove the sheath.

[0216] Group B: Upon initial confirmation of the predicted alarm, immediately stop the electrodes and remove the sheath; record the response time from the alarm to the actual stop.

[0217] Group C: After the initial confirmation of the predicted alarm, continue the same operation for 10.0s; immediately stop the electrodes and remove the sheath at 10.0s. It is recommended that the timing error not exceed ±0.5s.

[0218] Each sample was photographed at three times: before exposure, immediately after exposure, and 5 minutes after exposure. The image taken 5 minutes after exposure was used as the primary scoring time point.

[0219] The safe stopping conditions are as follows:

[0220] The tissue shows obvious carbonization, smoking, rupture, or rapid liquid leakage;

[0221] The injection water pressure exceeds the preset safety limit for the equipment or test;

[0222] The temperature reaches the preset absolute stop limit;

[0223] Equipment failure, sensor detachment, image interruption, or reaching the maximum safe exposure time.

[0224] The data records are shown in Table 13:

[0225] Table 13

[0226] category Record Item Minimum Record Requirements use water pressure Target value, average value, maximum value Record the entire exposure process or key moments. Confirm that the three sets of water pressure conditions are comparable temperature Initial value, alarm time, highest value Based primarily on the sensor data of the test subjects Explain the trend of electrode heat generation energy Electrode mode, power The formal test remains unchanged. Confirming consistent energy conditions Time series Alarm time, stop time Group B response time; Group C continuation time Confirm that the operation complies with the plan. image Previous, Immediate, 5-minute original image One sample per document Main evaluation evidence

[0227] The rules for success determination and rework are as follows:

[0228] Group A: Both samples reached level 2 or above, proving that the fixed conditions can cause visible damage;

[0229] Group B: Both samples are below level 2, or at least significantly milder than the corresponding Group C;

[0230] Group C: Both samples reach level 2 or above, or their average image score is at least one level higher than that of Group B;

[0231] The water pressure, electrode power, and operating conditions of the three groups should be comparable when predicting alarms; it is recommended that the actual water pressure deviation be controlled within ±10% of the target value.

[0232] The response time after an alarm in Group C should be 10.0 ± 0.5 seconds. Group B should stop as soon as possible after an alarm and record the actual response time.

[0233] The results of the experiment are recorded in Table 14:

[0234] Table 14

[0235] Group Target water pressure (mmHg) Average water pressure (mmHg) Maximum water pressure (mmHg) Set power Alarm temperature ℃ Maximum temperature ℃ Alarm until stop Scoring after the event 5-minute rating A 80 82 84 7 gears 53 67 / 3 3 B 80 83 85 7 gears 53 55 1.2 0 0 C 80 82 83 7 gears 51 68 10 2 2

[0236] Under fixed electrode mode and power, fixed perfusion water pressure, and consistent tissue and imaging conditions, a comparative experiment was conducted on three independent samples. Samples in group A showed visible damage at grade 2 or higher; samples in group B did not reach grade 2 or higher; and samples in group C reached grade 2 or higher. The average image score for group B 5 minutes after cessation was grade 0, and for group C it was grade 2.

[0237] like Figure 2-4 The images shown are the original images of Group A before exposure, immediately after exposure, and 5 minutes after exposure, respectively. Figure 5-7 The images shown are the original images of Group B before exposure, immediately after exposure, and 5 minutes after exposure, respectively. Figure 8-10 The images shown are the original images of Group C before exposure, immediately after exposure, and 5 minutes after exposure.

[0238] The results show that, under the conditions of this ex vivo tissue and fixation test, immediately stopping the electrode and removing the endoscope sheath after a 10-second predictive alarm results in less visible tissue alteration than if operation continued for 10 seconds after ignoring the alarm. This result supports the engineering effectiveness of the early warning system in providing time for operational intervention.

[0239] Based on the same inventive concept, this invention also proposes a device for early warning of arthroscopic tissue damage based on a thermo-baric coupling damage development model. The implementation of this device is similar to the implementation of the method described above, and repeated details will not be elaborated further. Figure 11 As shown, the device 100 includes:

[0240] Feature parameter extraction module 101: used to acquire pressure and temperature data at the tip of the arthroscope sheath and preprocess them to extract pressure and temperature feature parameters;

[0241] Damage risk calculation module 102: used to calculate mechanical damage risk value and thermal damage risk value based on pressure characteristic parameters and temperature characteristic parameters, respectively;

[0242] Historical risk value calculation module 103: used to calculate the current risk value of the tissue based on the mechanical damage risk value and the thermal damage risk value, establish a risk memory model based on the current risk value of the tissue, and calculate the historical risk value of the tissue;

[0243] Risk index calculation module 104: It is used to predict the organization's future risk status based on the organization's current risk value and historical risk value, calculate the final risk index, determine the risk level based on the final risk index, and output the corresponding alarm information.

[0244] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the described module can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0245] like Figure 12 As shown, the device includes a central processing unit (CPU), which can perform various appropriate actions and processes based on computer program instructions stored in read-only memory (ROM) or loaded from storage units into random access memory (RAM). The RAM can also store various programs and data required for device operation. The CPU, ROM, and RAM are interconnected via a bus. Input / output (I / O) interfaces are also connected to the bus.

[0246] Multiple components in the device are connected to the I / O interface, including: input units such as keyboards and mice; output units such as various types of displays and speakers; storage units such as disks and optical discs; and communication units such as network interface cards (NICs), modems, and wireless transceivers. The communication unit allows the device to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0247] The processing unit executes the various methods and processes described above, such as method steps S01 to S04. For example, in some embodiments, method steps S01 to S04 may be implemented as a computer software program tangibly contained in a machine-readable medium, such as a storage unit. In some embodiments, part or all of the computer program may be loaded and / or installed on the device via ROM and / or a communication unit. When the computer program is loaded into RAM and executed by the CPU, one or more steps of method steps S01 to S04 described above may be performed. Alternatively, in other embodiments, the CPU may be configured to execute method steps S01 to S04 by any other suitable means (e.g., by means of firmware).

[0248] The functions described above in this document can be performed at least in part by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: 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), and so on.

[0249] 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.

[0250] 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.

[0251] Furthermore, although the operations are described in a specific order, this should be understood as requiring that such operations be performed in the specific order shown or in sequential order, or requiring that all illustrated operations be performed to achieve the desired result. In certain environments, multitasking and parallel processing may be advantageous. Similarly, although several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of the invention. Certain features described in the context of individual embodiments may also be implemented in combination in a single implementation. Conversely, various features described in the context of a single implementation may also be implemented individually or in any suitable sub-combination in multiple implementations.

[0252] Although the subject matter has been described using language specific to structural features and / or methodological logic, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. Rather, the specific features and actions described above are merely illustrative examples of implementing the claims.

Claims

1. A method for early warning of arthroscopic tissue damage based on a thermo-baric coupling damage development model, characterized in that, The method includes: Step S01: Obtain pressure and temperature data at the tip of the arthroscopic sheath and preprocess them to extract pressure and temperature characteristic parameters; Step S02: Calculate the mechanical damage risk value and the thermal damage risk value based on the pressure characteristic parameters and temperature characteristic parameters, respectively; Step S03: Calculate the current risk value of the tissue based on the mechanical damage risk value and the thermal damage risk value, establish a risk memory model based on the current risk value of the tissue, and calculate the historical risk value of the tissue; Step S04: Predict the organization's future risk status based on the organization's current risk value and historical risk value, calculate the final risk index, determine the risk level based on the final risk index, and output the corresponding alarm information.

2. The method for arthroscopic tissue damage early warning based on a thermo-baric coupling damage development model according to claim 1, characterized in that, The pressure characteristic parameters include peak pressure, instantaneous pressure change, and duration of continuous pressure application; the temperature characteristic parameters include maximum temperature, instantaneous temperature change, and duration of temperature application.

3. The method for arthroscopic tissue damage early warning based on a thermo-baric coupling damage development model according to claim 1, characterized in that, The formula for calculating the mechanical damage risk value mentioned in step S02 is as follows: ,in, , , These are the weighting coefficients. and ; Pressure intensity; This represents the rate of change of pressure. Rate the duration of stress.

4. The method for arthroscopic tissue damage early warning based on a thermo-baric coupling damage development model according to claim 1, characterized in that, The formula for calculating the thermal damage risk value mentioned in step S02 is as follows: ,in, , , These are the weighting coefficients. and ; For temperature risk scoring, For the rate of temperature change, For the risk score of heat accumulation exposure, This represents the cumulative amount of heat.

5. The method for arthroscopic tissue damage early warning based on a thermo-baric coupling damage development model according to claim 1, characterized in that, The specific steps of step S03 are as follows: The organization's current risk value is calculated using a complementary probability fusion approach. ; The continuous-time model of the organizational risk recovery process is represented as follows: ， Discretizing the above continuous-time model yields: ， make: ， but: ， in, This indicates the organization's historical risk value at the current moment; This indicates the historical risk value organized at the previous sampling time; This indicates the current risk value of the organization at the current moment; This represents the risk memory decay coefficient.

6. The method for arthroscopic tissue damage early warning based on a thermo-baric coupling damage development model according to claim 5, characterized in that, The formula for calculating the final risk index mentioned in step S04 is as follows: ,in, , , These are the weighting coefficients. ,and ; Indicates future forecast risks; Indicates the current risk value; Indicates the lead time for prediction; This indicates the risk growth rate.

7. The method for arthroscopic tissue damage early warning based on a thermo-baric coupling damage development model according to claim 6, characterized in that, The risk level determination based on the final risk index mentioned in step S04 specifically includes: This is then considered a normal state. This is then classified as a Level 1 alarm. This is then classified as a Level 2 alarm. This is then classified as a Level 3 high-risk alarm. , , Based on the final risk index The correspondence between the actual organizational state and the actual organizational state is determined.

8. A device for early warning of arthroscopic tissue damage based on a thermo-baric coupling damage development model, characterized in that, The device implements the method as described in any one of claims 1 to 7, comprising: Feature parameter extraction module: used to acquire and preprocess pressure and temperature data at the tip of the arthroscopic sheath, and extract pressure and temperature feature parameters; Damage risk calculation module: used to calculate mechanical damage risk value and thermal damage risk value based on pressure characteristic parameters and temperature characteristic parameters, respectively; Historical risk value calculation module: used to calculate the current risk value of the tissue based on the mechanical damage risk value and the thermal damage risk value, establish a risk memory model based on the current risk value of the tissue, and calculate the historical risk value of the tissue; Risk Index Calculation Module: This module is used to predict the organization's future risk status based on its current and historical risk values, calculate the final risk index, determine the risk level based on the final risk index, and output corresponding alarm information.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the program, it implements the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1 to 7.

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