Intrarenal pressure prediction method, endoscope control system, medical equipment and storage medium

Abstract

The present application provides a method for predicting intrarenal pressure, an endoscope control system, a medical device, and a storage medium, relating to the field of data detection and calibration technology. The method obtains current detection data and a current pressure detection value, wherein the current detection data includes the current perfusion fluid input flow rate, the current negative pressure suction output flow rate, and the current fluid resistance, and the current pressure detection value is the detection value obtained by the pressure sensor installed at the liquid inlet of the operating handle. The predicted intrarenal pressure value can then be determined based on the current detection data, and the target intrarenal pressure can be obtained based on the current pressure detection value and the predicted intrarenal pressure value. Both the current detection data and the current pressure detection value can be obtained using existing components of the endoscope. The current pressure detection value is calibrated using the predicted intrarenal pressure value, which can improve the accuracy of intrarenal pressure detection without adding additional endoscope components.

Description

Technical Field

[0001] The present application relates to the technical field of data detection and calibration, and in particular to a method for predicting intrarenal pressure, an endoscope control system, a medical device, and a storage medium. Background Art

[0002] Currently, there are three types of intrarenal pressure measurements. The first type is to embed a pressure sensor at the distal end of the endoscope insertion part, which can achieve high-precision real-time measurement, but the cost is high and the production and processing are difficult; the second type is to set up a pressure sensor outside the body, and the detection end extends to the insertion part. The measurement accuracy is high, but due to the long feedback path, the detection result has a certain delay. In addition, if the detection end enters through the instrument channel, it will limit the use of surgical instruments. If it is embedded inside the endoscope insertion part, the existing endoscope structure will be adjusted and modified, increasing production cost and difficulty; the third type is large model prediction, which has low cost and small changes to the endoscope. It can instantly measure the current time and predict the intrarenal pressure data after a period of time, but the predicted data accuracy is low. Summary of the Invention

[0003] The present application provides a method for predicting intrarenal pressure, an endoscope control system, a medical device, and a storage medium, which can improve the accuracy of intrarenal pressure detection without adding endoscope components.

[0004] This application provides a method for predicting intrarenal pressure, comprising:

[0005] Obtaining current test data and current pressure detection value; the current test data includes the current perfusion fluid input flow, the current negative pressure suction output flow, and the current fluid resistance; the current pressure detection value is the detection value obtained by the pressure sensor installed at the liquid inlet of the operating handle;

[0006] Determine the predicted value of renal calyceal pressure based on current test data;

[0007] The target renal calyceal pressure is obtained based on the current pressure detection value and the predicted renal calyceal pressure.

[0008] To achieve the above objectives and other related objectives, the present application provides an endoscope control system, comprising:

[0009] A data acquisition module is used to obtain current test data and current pressure detection value; the current test data includes the current perfusion fluid input flow rate, the current negative pressure suction output flow rate and the current fluid resistance; the current pressure detection value is the detection value obtained by the pressure sensor installed at the liquid inlet of the operating handle;

[0010] A first determination module is used to determine a predicted value of renal calyceal pressure based on current detection data;

[0011] The second determination module is configured to obtain a target renal calyceal pressure based on the current pressure detection value and the renal calyceal pressure prediction value.

[0012] To achieve the above-mentioned objectives and other related objectives, the present application also provides a medical device, including an endoscope host and an operating handle, wherein the endoscope host includes a memory and a processor, and the processor is used to execute program instructions stored in the memory to implement one or more of the aforementioned intrarenal pressure prediction methods.

[0013] To achieve the above-mentioned purpose and other related purposes, the present application also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a computer processor, the computer executes one or more of the aforementioned intrarenal pressure prediction methods.

[0014] As described above, the present application provides a method for predicting intrarenal pressure, an endoscope control system, a medical device, and a storage medium, which have the following beneficial effects:

[0015] The present application discloses a method for predicting intrarenal pressure. This method obtains current detection data and a current pressure detection value, wherein the current detection data includes the current perfusion fluid input flow rate, the current negative pressure suction output flow rate, and the current fluid resistance, and the current pressure detection value is the detection value obtained by a pressure sensor installed at the liquid inlet of the operating handle. Based on the current detection data, a predicted intrarenal calyx pressure value is then determined, and based on the current pressure detection value and the predicted intrarenal calyx pressure value, a target intrarenal calyx pressure is obtained. Both the current detection data and the current pressure detection value can be obtained using existing components of an endoscope. By calibrating the current pressure detection value using the predicted intrarenal pressure value, the accuracy of intrarenal calyx pressure detection can be improved without adding additional endoscope components.

[0016] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings are incorporated into and constitute a part of the specification, illustrating embodiments consistent with the present application and, together with the specification, serving to explain the principles of the present application. It is obvious that the drawings described below are merely some embodiments of the present application, and a person of ordinary skill in the art can derive other drawings based on these drawings without inventive effort. In the drawings:

[0018] Figure 1 is a schematic diagram of an implementation environment of a method for predicting intrarenal pressure according to an exemplary embodiment of the present application;

[0019] Figure 2 is a flow chart of a method for predicting intrarenal pressure shown in an exemplary embodiment of the present application;

[0020] Figure 3 It is a structural block diagram of an endoscope control system shown in an exemplary embodiment of the present application. DETAILED DESCRIPTION

[0021] The following will describe the embodiments of the present application with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand the other advantages and effects of the present application from the contents disclosed in this specification. The present application can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present application. It should be understood that the preferred embodiments are only for the purpose of illustrating the present application and are not intended to limit the scope of protection of the present application.

[0022] It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present application. Therefore, the illustrations only show components related to the present application and are not drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component can be changed at will, and the component layout type may also be more complicated.

[0023] In the following description, a large number of details are discussed to provide a more thorough explanation of the embodiments of the present application. However, it is obvious to those skilled in the art that the embodiments of the present application can be implemented without these specific details. In other embodiments, well-known structures and devices are shown in the form of block diagrams rather than in detail to avoid making the embodiments of the present application difficult to understand.

[0024] See also Figure 1 , which is a schematic diagram of an implementation environment of a method for predicting intrarenal pressure according to an exemplary embodiment of the present application. The implementation environment may include an operating handle 110 and an endoscope host 120.

[0025] The operating handle 110 and the endoscope host 120 can be connected by wire or wirelessly. Figure 1 The operating handle 110 and the endoscope host 120 are connected in a wired manner. When the operating handle 110 and the endoscope host 120 are connected in a wired manner, they can be connected via an optical fiber or cable. When the operating handle 110 and the endoscope host 120 are connected in a wireless manner, they can be connected to the endoscope host via a wireless communication protocol such as Wi-Fi or Bluetooth.

[0026] The operating handle 110 is connected to an insertion tube 111, with a lens disposed at the front end. The insertion tube is inserted into the renal calyx 200. The lens is responsible for capturing images within the renal calyx 200 and displaying them on the display screen of the endoscope main unit 120. The operating handle 110 may also include an operating handle liquid inlet 112, an infusion module 113, a pressure sensor 114, and a negative pressure suction module 115. The operating handle liquid inlet 112 may also be referred to as an instrument port.

[0027] The perfusion module 113 is connected to the liquid inlet 112 of the operating handle through a pipe. The perfusion module 113 can be used to pump perfusion fluid into the renal calyx through the liquid inlet 112 of the operating handle and the input pipe on the operating handle. A flow sensor and a pressure sensor can be integrated in the perfusion module 113. The flow sensor of the perfusion module 113 can be used to collect the current perfusion fluid input flow rate, and the pressure sensor of the perfusion module 113 can collect the current perfusion end pressure. The perfusion module 113 can also transmit the flow data collected by the flow sensor and the pressure data collected by the pressure sensor to the endoscope host 120.

[0028] The pressure sensor 114 can be provided on the connection line between the perfusion module 113 and the operating handle liquid inlet 112, and the pressure sensor 114 is installed at one end close to the operating handle liquid inlet 112. The pressure sensor 114 can be used to collect the current pressure detection value and transmit the current pressure detection value to the endoscope host 120.

[0029] The negative pressure suction module 115 can be connected to the liquid outlet channel ( Figure 1 Connected via a pipeline (not shown), the negative pressure suction module 115 can aspirate the debris and other materials along with the perfusion fluid out of the renal calyx, while maintaining the pressure of the calyx at a stable value to ensure surgical safety. The negative pressure suction module 115 can be integrated with a pressure sensor and a flow sensor. The pressure sensor of the negative pressure suction module 115 can be used to detect the current negative pressure at the suction end, and the flow sensor of the negative pressure suction module 115 can be used to detect the current negative pressure suction output flow rate. The negative pressure suction module 115 can also transmit data collected by the flow sensor and the pressure sensor to the endoscope host 120.

[0030] It should be noted that when the endoscope is a multi-lumen endoscope, the liquid outlet channel can be one of the multiple lumens. When the endoscope works in conjunction with a sheath, the liquid outlet channel can be the gap between the sheath and the endoscope insertion portion, or it can be an independent lumen on the sheath; preferably, the gap between the sheath and the endoscope insertion portion can be used as the liquid outlet channel.

[0031] It should be noted that the arrows indicate the flow direction of the perfusion fluid. The perfusion fluid can be pumped into the renal calyx from the perfusion module 113 and can be sucked out of the renal calyx by the negative pressure suction module 115 .

[0032] See also Figure 2 , Figure 2 This is a flow chart of a method for predicting intrarenal pressure according to an exemplary embodiment of the present invention. Figure 1 The implementation environment shown. Figure 2 It can be seen that the intrarenal pressure prediction method may include:

[0033] Step S210: Acquire current detection data and current pressure detection value.

[0034] Among them, the current detection data includes the current perfusion fluid input flow, the current negative pressure suction output flow and the current fluid resistance; the current pressure detection value is the detection value obtained by the pressure sensor installed at the liquid inlet of the operating handle.

[0035] In one embodiment of the present application, current detection data and current pressure detection values ​​can be obtained. The current pressure detection value can be obtained by a pressure sensor installed at the liquid inlet of the operating handle. The perfusion module can be integrated with a flow sensor, and the current perfusion fluid input flow rate can be obtained by the flow sensor of the perfusion module. The negative pressure suction module can also be integrated with a flow sensor, and the current negative pressure suction output flow rate can be obtained by the flow sensor of the negative pressure suction module.

[0036] For example, the current fluid resistance can be determined by the pressure detection value, the current perfusion fluid input flow rate, and the current negative pressure suction output flow rate. The formula for determining the current fluid resistance can be expressed as:

[0037] ;

[0038] in, is the current fluid resistance, Enter the flow rate for the current perfusate, is the current negative pressure suction output flow, It is the current pressure detection value.

[0039] Step S220: determining a predicted value of the renal calyceal pressure based on the current detection data.

[0040] In one embodiment of the present application, a predicted renal calyceal pressure value can be determined based on current pressure detection data. The current detection data is detection data related to the renal calyceal pressure, and the renal calyceal pressure prediction value can be obtained by estimating the renal calyceal pressure based on the current detection data.

[0041] Optionally, determining the predicted value of the renal calyceal pressure based on the current detection data may include: obtaining the predicted value of the renal calyceal pressure based on the current detection data and a pre-trained renal intra-pressure prediction model.

[0042] Optionally, before obtaining the intra-calyceal pressure prediction value based on the current detection data and the pre-trained intra-renal pressure prediction model, the method may further include: obtaining a training sample set, the training sample set including multiple training sample pairs, each training sample pair including a training sample and a sample label, the training sample including the perfusion fluid input flow, the negative pressure suction output flow, and the fluid resistance, and the sample label including the intra-calyceal pressure; training the intra-renal pressure prediction model based on the training sample set until the training stop condition is reached.

[0043] In one embodiment of the present application, a training sample set can be obtained. The process of obtaining the training sample set can include collecting raw data, including the perfusion fluid input flow, negative pressure suction output flow, and fluid resistance; performing preprocessing on the raw data, including missing value processing, outlier detection, data standardization, and data normalization, to obtain training samples; labeling the intra-calyx pressure corresponding to each training sample to obtain a sample label; combining the training samples and the corresponding sample labels to obtain multiple training sample pairs, and the multiple training sample pairs constitute the training sample set. A 3D model of the renal calyx can be reconstructed using CT scanning, and the 3D model can be simulated by CFD based on simulation conditions to obtain the sample label intra-calyx pressure.

[0044] Exemplarily, the intrarenal pressure prediction model can be a fully connected neural network (FCNN) regression model, which may include an input layer, two hidden layers, and an output layer. One hidden layer may include 64 neurons, which output to another hidden layer via a nonlinear ReLU activation function and a fully connected layer. The other hidden layer may include 32 neurons, which output to the output layer via a nonlinear ReLU activation function and a fully connected layer. A loss function, such as the mean squared error (MSE) loss function, may be set, and the training termination condition may include stopping training when the maximum number of training rounds is reached.

[0045] Optionally, the current detection data may further include the current perfusion end pressure and the current suction end negative pressure. The process of determining the predicted value of the renal calyceal pressure based on the current detection data may further include: determining the predicted value of the renal calyceal pressure based on the current detection data and a renal calyceal pressure prediction formula. The renal calyceal pressure prediction formula may be expressed as:

[0046] ;

[0047] in, is the predicted value of renal calyceal pressure, Enter the flow rate for the current perfusion fluid, is the current negative pressure suction output flow, is the current negative pressure at the suction end, is the current perfusion end pressure, and R is the current fluid resistance.

[0048] It should be noted that a pressure sensor can be integrated in the perfusion module, and the value detected by the pressure sensor in the perfusion module at the current moment is the current perfusion end pressure; a pressure sensor can be integrated in the negative pressure suction module, and the value detected by the pressure sensor in the negative pressure suction module at the current moment is the current suction end negative pressure.

[0049] In one possible approach, the current test data may also include a 3D renal calyx model, and the training sample may also include a 3D renal calyx model. The 3D renal calyx model can be obtained by 3D reconstruction using CT scanning, and the 3D renal calyx model can be represented as a voxelized grid. The training sample may include the perfusion fluid input flow rate, the negative pressure suction output flow rate, the fluid resistance, and the corresponding 3D renal calyx model. The 3D renal calyx model may be the patient's preoperative 3D renal calyx model.

[0050] It should be noted that before using an endoscope to access the patient's calyces for surgery, a CT scan of the patient's calyces can be performed to reconstruct a corresponding 3D calyceal model. When applying the current test data and a pre-trained renal pressure prediction model to derive a predicted calyceal pressure value, the inputs to the model can include the current perfusion fluid input flow rate, the current negative pressure suction output flow rate, the current fluid resistance, and the 3D calyceal model. Since the 3D calyceal model varies from patient to patient, inputting the 3D calyceal model into the pre-trained model can extract key features of the 3D calyceal model and, based on these features, derive a predicted calyceal pressure value, thereby improving the accuracy of the predicted calyceal pressure value.

[0051] Step S230 : obtaining a target renal calyceal pressure based on the current pressure detection value and the renal calyceal pressure prediction value.

[0052] In one embodiment of the present application, a target calyceal pressure can be obtained based on the current pressure measurement value and the predicted calyceal pressure. The current pressure measurement value is the pressure value near the inlet end. Due to the distance from the calyceum, there may be a certain difference between the current pressure measurement value and the calyceal pressure. The predicted calyceal pressure is a theoretical value estimated based on the current measurement data. The target calyceal pressure can be obtained by fusing and correcting the current pressure measurement value and the predicted calyceal pressure value.

[0053] Intracalyceal pressure refers to the fluid pressure inside the renal calyces. It is one of the urodynamic parameters and usually reflects the pressure state of urine accumulation in the renal calyces.

[0054] Optionally, the process of obtaining the target renal calyceal pressure based on the current pressure detection value and the renal calyceal pressure prediction value in S230 may include steps S231 to S233.

[0055] Step S231 , performing fast Fourier transform on the current pressure detection value to obtain pressure data to be processed.

[0056] In one embodiment of the present application, the current pressure detection value can be subjected to a fast Fourier transform to obtain the pressure data to be processed. The fast Fourier transform can convert the current pressure detection value from a time domain signal into a frequency domain signal. The frequency distribution of the signal can be visually viewed through a spectrum diagram, which facilitates the identification of periodic or abnormal frequency components.

[0057] Step S232: When the pressure data to be processed is greater than a preset threshold, a prompt message is generated.

[0058] The prompt information is used to inform the user that the current pressure detection is abnormal.

[0059] In one embodiment of the present application, when the pressure data to be processed is greater than a preset threshold, the current pressure detection value is abnormal data, and a prompt message can be generated. After the prompt message is generated, the prompt message can be displayed through the voice module of the endoscope host, and the prompt message can also be displayed through the display screen of the endoscope host. When the pressure data to be processed is greater than the preset threshold, it indicates high-frequency oscillation of the pressure signal, which may be caused by factors such as pipeline bubbles. At this time, the current pressure detection value is not accurate. The prompt message can be used to inform the user of pipeline abnormalities. The endoscope host can determine the predicted renal calyceal pressure as the target renal calyceal pressure, which can be used to detect the stability of the instrument port pipeline connection.

[0060] Exemplarily, the preset threshold may be 10 Hz.

[0061] Step S233: When the pressure data to be processed is less than or equal to the preset threshold, the current pressure detection value and the predicted renal calyceal pressure are fused and corrected to obtain the target renal calyceal pressure.

[0062] In one embodiment of the present application, when the pressure data to be processed is less than or equal to a preset threshold, the current pressure detection value is normal data, and the current pressure detection value and the predicted renal calyceal pressure value can be fused and corrected to obtain the target renal calyceal pressure.

[0063] Optionally, a fusion correction is performed based on the current pressure detection value and the predicted renal calyceal pressure to obtain the target renal calyceal pressure, including: using an extended Kalman filter method to fuse and correct the current pressure detection value and the predicted renal calyceal pressure to obtain the target renal calyceal pressure.

[0064] In one embodiment of the present application, an extended Kalman filter method may be used to perform fusion correction on the current pressure detection value and the predicted renal calyceal pressure value to obtain the target renal calyceal pressure.

[0065] Illustratively, the process of fusing and correcting the current pressure detection value and the predicted renal calyceal pressure using the extended Kalman filter method to obtain the target renal calyceal pressure may include:

[0066] 1) Obtain state variables and observation variables. State variables can be expressed as: ,in, is the state variable at the kth moment, is the target intracalyceal pressure, is the fluid resistance, is the pipeline delay compensation factor, Can be used to correct sensor hysteresis; the observed variable can be expressed as ,in, is the observed variable, is the current pressure detection value, is the predicted value of renal calyceal pressure.

[0067] 2) Design the state equation to describe the physical laws of the evolution of state variables over time:

[0068] ;

[0069] in, System input, including the current perfusate input flow And the current negative pressure suction output flow ; is process noise, process noise Obey Gaussian distribution , is the process noise covariance, is the first function. The state equation can be expressed as:

[0070] ;

[0071] Where, C is the renal calyx compliance, is the sampling time interval.

[0072] 3) Design observation equations to map state variables to observation values:

[0073] ;

[0074] in, is the observation noise, observation noise Obey Gaussian distribution , is the observation noise covariance, is the second function. The observation equation can be expressed as:

[0075] ;

[0076] in, is the current pressure detection value at the kth moment, is the predicted value of renal calyceal pressure at the kth moment.

[0077] 4) Execute the prediction step, state prediction:

[0078] ;

[0079] Error covariance prediction:

[0080] ;

[0081] in, is the first Jacobian matrix. The first Jacobian matrix is ​​the partial derivative matrix of the state equation with respect to the state variables. For the linear state equation, the first Jacobian matrix can be expressed as:

[0082] ;

[0083] in, is the first Jacobian matrix at the k-1th moment.

[0084] 5) Perform the update step and calculate the Kalman gain:

[0085] ;

[0086] in, is the second Jacobian matrix, which is the partial derivative matrix of the observation equation with respect to the state variable. The second Jacobian matrix can be expressed as:

[0087] ;

[0088] Status Update:

[0089] ;

[0090] Covariance update:

[0091] ;

[0092] in, is the identity matrix, and The dimension of is the same as that of the state vector.

[0093] It should be noted that the process noise covariance can be initialized and the observation noise covariance , and set the initial values ​​of the state variables and the initial values ​​of the covariance.

[0094] For example, after determining the fluid resistance at time k-1 in the state variable, the fluid resistance at time k-1 can be determined as the fluid resistance at time k, and the predicted value of the renal calyceal pressure at time k is determined based on the fluid resistance at time k, the perfusion fluid input flow at time k, and the negative pressure suction output flow at time k.

[0095] Optionally, a fusion correction is performed based on the current pressure measurement value and the predicted calyceal pressure value to obtain a target calyceal pressure. This includes determining the target calyceal pressure as the average of the current pressure measurement value and the predicted calyceal pressure value. Averaging the current pressure measurement value and the predicted calyceal pressure value to obtain the average value partially offsets random noise in the two values, bringing the target calyceal pressure closer to the true value and further improving the accuracy of the target calyceal pressure.

[0096] It should be noted that the intrarenal pressure prediction method provided in the embodiment of the present application can be executed by the endoscope host.

[0097] Figure 3 FIG. 1 is a block diagram of an endoscope control system according to an exemplary embodiment of the present application. Figure 3 As shown, the exemplary endoscope control system 300 includes:

[0098] The data acquisition module 310 is used to obtain the current detection data and the current pressure detection value; the current detection data includes the current perfusion fluid input flow, the current negative pressure suction output flow and the current fluid resistance; the current pressure detection value is the detection value obtained by the pressure sensor installed at the liquid inlet of the operating handle.

[0099] The first determination module 320 is configured to determine a predicted value of the renal calyceal pressure based on current detection data.

[0100] The second determination module 330 is configured to obtain a target renal calyceal pressure based on the current pressure detection value and the renal calyceal pressure prediction value.

[0101] It should be noted that the endoscope control system provided in the above embodiment and the intrarenal pressure prediction method provided in the above embodiment are based on the same concept. The specific manner in which each module and unit performs operations has been described in detail in the method embodiment and will not be repeated here. In actual applications, the endoscope control system provided in the above embodiment can, as needed, allocate the above functions to different functional modules, that is, divide the internal structure of the system into different functional modules to complete all or part of the functions described above, and this is not limited here.

[0102] An embodiment of the present application also provides a medical device, including an endoscope host and an operating handle, wherein the endoscope host includes a memory and a processor, and the processor is used to execute program instructions stored in the memory to implement the intrarenal pressure prediction method provided in any of the above embodiments.

[0103] Another aspect of the present application provides a computer-readable storage medium having a computer program stored thereon. When executed by a computer processor, the computer program causes the computer to perform the intrarenal pressure prediction method provided in each of the aforementioned embodiments. The computer-readable storage medium may be included in the electronic device described in the aforementioned embodiments, or may exist independently and not be incorporated into the electronic device.

[0104] Another aspect of the present application provides a computer program product or computer program, comprising computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the intrarenal pressure prediction method provided in each of the above-described embodiments.

[0105] In the embodiments of this application, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance. Throughout the specification and claims, the terms "including" and "comprising" are open-ended terms and should be interpreted as "including but not limited to."

[0106] The above embodiments are merely illustrative of the principles and effects of this application and are not intended to limit this application. Anyone skilled in the art may modify or alter the above embodiments without departing from the spirit and scope of this application. Therefore, any equivalent modifications or alterations accomplished by a person of ordinary skill in the art without departing from the spirit and technical concepts disclosed in this application shall be covered by the claims of this application.

Claims

1. A method for predicting intrarenal pressure, characterized in that: include: Get the current detection data and current pressure detection value; The current detection data includes the current perfusion fluid input flow rate, the current negative pressure suction output flow rate, and the current fluid resistance; the current pressure detection value is the detection value obtained by the pressure sensor installed at the liquid inlet of the operating handle; the current fluid resistance is determined based on the pressure detection value, the current perfusion fluid input flow rate, and the current negative pressure suction output flow rate; Determine the predicted value of renal calyceal pressure based on current test data; The target renal calyceal pressure is obtained based on the current pressure detection value and the predicted renal calyceal pressure.

2. The method for predicting intrarenal pressure according to claim 1, wherein: Determine the predicted value of renal calyceal pressure based on the current test data, including: Based on the current test data and the pre-trained intrarenal pressure prediction model, the predicted value of the renal calyceal pressure is obtained.

3. The method for predicting intrarenal pressure according to claim 2, wherein: Before obtaining the predicted value of the renal calyceal pressure based on the current detection data and the pre-trained renal intra-pressure prediction model, the method further includes: Acquire a training sample set, the training sample set including multiple training sample pairs, each training sample pair including a training sample and a sample label, the training sample including perfusate input flow, negative pressure suction output flow, and fluid resistance, and the sample label including renal calyceal pressure; The intrarenal pressure prediction model is trained based on the training sample set until the training stop condition is reached.

4. The method for predicting intrarenal pressure according to claim 3, wherein: The current detection data also includes a three-dimensional model of the renal calyx, and the training sample also includes a three-dimensional model of the renal calyx.

5. The method for predicting intrarenal pressure according to claim 1, wherein: Based on the current pressure detection value and the predicted renal calyceal pressure value, the target renal calyceal pressure is obtained, including: Performing fast Fourier transform on the current pressure detection value to obtain the pressure data to be processed; When the pressure data to be processed is greater than the preset threshold, a prompt message is generated; the prompt message is used to inform the user that the current pressure detection is abnormal; When the pressure data to be processed is less than or equal to a preset threshold, the current pressure detection value and the predicted renal calyceal pressure are fused and corrected to obtain the target renal calyceal pressure.

6. The method for predicting intrarenal pressure according to claim 5, wherein: Based on the current pressure detection value and the predicted renal calyceal pressure, a fusion correction is performed to obtain the target renal calyceal pressure, including: The extended Kalman filter method is used to fuse and correct the current pressure detection value and the predicted renal calyceal pressure to obtain the target renal calyceal pressure.

7. The method for predicting intrarenal pressure according to claim 5, wherein: Based on the current pressure detection value and the predicted renal calyceal pressure, a fusion correction is performed to obtain the target renal calyceal pressure, including: The mean of the current pressure detection value and the predicted renal calyceal pressure is determined as the target renal calyceal pressure.

8. An endoscope control system, characterized in that: include: Data acquisition module, used to obtain current detection data and current pressure detection value; The current detection data includes the current perfusion fluid input flow rate, the current negative pressure suction output flow rate, and the current fluid resistance; the current pressure detection value is the detection value obtained by the pressure sensor installed at the liquid inlet of the operating handle; the current fluid resistance is determined based on the pressure detection value, the current perfusion fluid input flow rate, and the current negative pressure suction output flow rate; A first determination module is used to determine a predicted value of renal calyceal pressure based on current detection data; The second determination module is configured to obtain a target renal calyceal pressure based on the current pressure detection value and the renal calyceal pressure prediction value.

9. A medical device, characterized in that: The endoscope comprises an endoscope host and an operating handle, wherein the endoscope host comprises a memory and a processor, and the processor is used to execute program instructions stored in the memory to implement the intrarenal pressure prediction method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that A computer program is stored thereon, and when the computer program is executed by a processor of a computer, the computer is caused to execute the method for predicting intrarenal pressure according to any one of claims 1 to 7.

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