Abdominal cavity pressure monitoring device and system for peritoneal dialysis

Abstract

The present invention provides an abdominal cavity pressure monitoring device and system for peritoneal dialysis. The abdominal cavity pressure monitoring device for peritoneal dialysis includes: a pressure monitoring module for monitoring the abdominal cavity pressure of a subject to be measured in real time during the current pressure monitoring process to obtain real-time first pressure data; a posture recognition module for recognizing the posture of the subject to be measured in real time to determine the real-time target posture of the subject to be measured; a labeling module for labeling the first pressure data at the corresponding moment according to the real-time target posture of the subject to be measured to determine the labeled second pressure data; and a data analysis module for analyzing whether the cause of the abnormality is related to the target posture based on the second pressure data when an abnormality exists in the second pressure data. The technical solution of the present invention can better utilize and analyze the abdominal cavity pressure data of the subject to be measured.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a peritoneal cavity pressure monitoring device and system for peritoneal dialysis. Background Art

[0002] Peritoneal dialysis primarily involves the infusion of dialysate into the patient's abdominal cavity to remove metabolic products and toxic substances and correct water and electrolyte imbalances. Intraperitoneal pressure is a key indicator of peritoneal dialysis. On the one hand, it can affect the effectiveness of peritoneal dialysis—increased intra-abdominal pressure can reduce dialysis efficiency. On the other hand, higher intra-abdominal pressure can lead to complications such as hernia and leakage, increasing the risk of other dangerous conditions. Therefore, monitoring intra-abdominal pressure is essential.

[0003] In the related art, abdominal pressure monitoring of patients is mostly carried out using related abdominal pressure monitoring devices. These abdominal pressure monitoring devices mainly use pressure gauges or sensor technology to measure and record the patient's abdominal pressure. Therefore, how to better use the abdominal pressure obtained by these technologies is a technical problem that needs to be solved urgently. Summary of the Invention

[0004] The present invention provides an abdominal pressure monitoring device and system for peritoneal dialysis, which is used to solve the defect in the prior art that, under continuous monitoring, the abdominal pressure data cannot be interpreted for guiding diagnosis and treatment due to the interference of the posture of the subject to be measured, and achieves the purpose of posture annotation and abnormality analysis of the abdominal pressure data of the subject to be measured.

[0005] In a first aspect, the present invention provides a peritoneal pressure monitoring device for peritoneal dialysis, comprising:

[0006] The pressure monitoring module is used to monitor the abdominal pressure of the subject to be tested in real time during the current pressure monitoring process to obtain real-time first pressure data;

[0007] The posture recognition module is used to recognize the posture of the object to be measured in real time and determine the real-time target posture of the object to be measured;

[0008] a marking module, configured to mark the first pressure data at a corresponding moment according to the real-time target posture of the object to be measured, and determine the marked second pressure data;

[0009] The data analysis module is used to analyze whether the cause of the abnormality is related to the target posture according to the second pressure data when the second pressure data has an abnormality.

[0010] According to the peritoneal pressure monitoring device for peritoneal dialysis provided by the present invention, the above-mentioned posture recognition module is specifically used for

[0011] Acquire the image of the object to be measured in real time, classify the posture of the object to be measured in the image to determine the initial posture corresponding to the object to be measured;

[0012] According to the initial posture and the image to be measured, the posture of the object to be measured is identified to determine the target posture corresponding to the object to be measured; the accuracy of the above target posture is higher than the accuracy of the initial posture.

[0013] According to the peritoneal pressure monitoring device for peritoneal dialysis provided by the present invention, the above-mentioned posture recognition module is specifically used for

[0014] Using a preset human posture estimation model to identify key points of the object to be measured in the image to be measured, and determine multiple key points corresponding to the object to be measured;

[0015] Determine a plurality of target key points associated with the initial pose from the plurality of key points;

[0016] According to the position information of each target key point, the target posture of the object to be measured is determined.

[0017] According to the peritoneal pressure monitoring device for peritoneal dialysis provided by the present invention, the above-mentioned initial posture includes any one of sitting, standing and lying positions, the above-mentioned target key points include hip key points, shoulder key points and knee key points, and the above-mentioned posture recognition module is specifically used for

[0018] Calculating a first vector based on the position information of the hip key point and the position information of the shoulder key point; and calculating a second vector based on the position information of the hip key point and the position information of the knee key point;

[0019] Calculating the angle between the first vector and the second vector, and determining the degree of waist bending of the subject to be measured according to the angle;

[0020] The target posture is determined based on the degree of waist bending.

[0021] According to the peritoneal pressure monitoring device for peritoneal dialysis provided by the present invention, the above-mentioned posture recognition module is specifically used for

[0022] A neural network is used to classify the posture of the object to be measured in the image to determine the initial posture corresponding to the object to be measured;

[0023] The neural network is trained based on multiple training images and the annotation posture corresponding to each training image, and the annotation postures corresponding to the training images are not exactly the same.

[0024] According to the peritoneal cavity pressure monitoring device for peritoneal dialysis provided by the present invention, in the current pressure monitoring process, the target posture of the above-mentioned object to be measured at each moment is not completely the same;

[0025] The data analysis module is further configured to extract second pressure data belonging to the same target posture at different times from the second pressure data, and determine a change trend of the abdominal cavity pressure under the corresponding target posture based on the second pressure data at different times for each target posture; the change trend of the abdominal cavity pressure under each target posture is different;

[0026] Furthermore, the changing trends of the abdominal cavity pressure under each target posture are compared, and the optimal posture of the subject to be measured in the next pressure monitoring process is determined from each target posture.

[0027] According to the present invention, a peritoneal pressure monitoring device for peritoneal dialysis is provided.

[0028] The data analysis module is specifically configured to adjust the target posture of the object at the current moment, and after the adjustment, detect whether the second pressure data at a subsequent moment is abnormal; if the second pressure data at the subsequent moment is not abnormal, determine that the cause of the abnormality in the second pressure data at the current moment is related to the target posture at the current moment;

[0029] If the second pressure data at the subsequent moment is still abnormal, it is determined that the reason why the second pressure data at the current moment is abnormal is related to the peritoneal dialysis fluid infusion volume or ultrafiltration volume of the subject to be measured.

[0030] According to the present invention, a peritoneal pressure monitoring device for peritoneal dialysis further comprises:

[0031] an alarm module, configured to compare the real-time second pressure data with a preset pressure threshold range, and when the second pressure data at any moment exceeds the pressure threshold range, determine that the second pressure data at any moment is abnormal and output an alarm message;

[0032] The alarm information includes the reason why the second pressure data at any moment is abnormal and decision-making suggestion information determined based on the reason.

[0033] According to the present invention, a peritoneal pressure monitoring device for peritoneal dialysis further comprises:

[0034] The auxiliary decision-making module is used to generate a personalized treatment plan corresponding to the subject to be tested based on the second pressure data of the subject to be tested in multiple pressure monitoring processes, the clinical treatment data of the subject to be tested, and the living habit data of the subject to be tested; the above-mentioned clinical treatment data includes the initial treatment plan and / or predicted treatment results.

[0035] In a second aspect, the present invention further provides a peritoneal pressure monitoring system for peritoneal dialysis, comprising an image acquisition device and the peritoneal pressure monitoring device for peritoneal dialysis according to the first aspect;

[0036] The above-mentioned image acquisition device is used to obtain the image to be measured of the object to be measured in real time, and transmit the image to be measured to the abdominal cavity pressure monitoring device used for peritoneal dialysis.

[0037] The present invention provides an abdominal cavity pressure monitoring device and system for peritoneal dialysis, wherein the abdominal cavity pressure monitoring device for peritoneal dialysis includes a pressure monitoring module, a posture recognition module, a labeling module and a data analysis module, wherein the pressure monitoring module can monitor the abdominal cavity pressure of the object to be measured in real time during the current pressure monitoring process to obtain real-time first pressure data; the posture recognition module can identify the neutron state of the object to be measured in real time to determine the real-time target posture of the object to be measured; the labeling module can label the first pressure data at the corresponding moment according to the real-time target posture of the object to be measured to determine the labeled second pressure data; and the data analysis module can analyze whether the cause of the abnormality is related to the target posture based on the second pressure data when an abnormality exists in the second pressure data. The abdominal pressure monitoring device for peritoneal dialysis of the present invention can recognize the real-time posture of the subject to be measured and mark the recognized posture on the real-time pressure data. In this way, when the abdominal pressure is continuously monitored, the pressure data with real-time posture marking can be used to quickly analyze whether the cause of the abnormality is related to the posture when there is an abnormality in the abdominal pressure, so that the abdominal pressure data of the subject to be measured caused by posture interference can be well interpreted and can be used to guide subsequent diagnosis and treatment, thereby realizing the effective use and accurate analysis of the abdominal pressure data of the subject to be measured. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction is given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0039] Figure 1 It is a schematic structural diagram of the peritoneal cavity pressure monitoring device for peritoneal dialysis provided by the present invention.

[0040] Figure 2 It is a schematic diagram of key points of the body parts of the subject to be tested provided by the present invention.

[0041] Figure 3 It is a schematic diagram of calculating the first vector and the second vector and the angle between them provided by the present invention.

[0042] Figure 4 It is a schematic diagram of various target postures provided by the present invention.

[0043] Figure 5It is a structural schematic diagram of the peritoneal cavity pressure monitoring system for peritoneal dialysis provided by the present invention.

[0044] Description of reference numerals:

[0045] 10: Abdominal cavity pressure monitoring device for peritoneal dialysis; 110: Pressure monitoring module; 120: Posture recognition module; 130: Labeling module; 140: Data analysis module; 20: Image acquisition device. DETAILED DESCRIPTION

[0046] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0047] Intraperitoneal pressure (IAP) is a key indicator of peritoneal dialysis. On the one hand, it affects dialysis effectiveness; increased IAP reduces dialysis efficiency. On the other hand, high IAP can lead to complications such as hernia, leakage, thoracoabdominal fistula, gastroesophageal reflux, or delayed emptying, increasing the risk of enteric peritonitis and contributing to mortality and technical failure. Therefore, real-time and accurate monitoring of IAP can help improve treatment outcomes, prevent complications, and ultimately enhance patients' quality of life. Currently, several IAP monitoring devices primarily utilize pressure gauges or sensors to measure and record IAP. However, existing IAP monitoring devices suffer from a significant limitation: they fail to fully account for the significant impact of body posture on IAP. In fact, IAP is significantly affected by body posture. For example, when a patient is sitting, IAP is significantly higher than when standing, while it is lowest when lying flat. Because current IAP monitoring devices fail to account for these posture-induced pressure variations, the data they provide is often difficult to accurately interpret and apply, undoubtedly increasing the complexity and uncertainty of treatment decisions. Based on this, an embodiment of the present invention provides an abdominal cavity pressure monitoring device and system for peritoneal dialysis, which can solve this technical problem.

[0048] The following combination Figure 1-Figure 4 An abdominal cavity pressure monitoring device for peritoneal dialysis according to an embodiment of the present invention is described.

[0049] Figure 1 FIG. 1 is a structural block diagram of the peritoneal pressure monitoring device for peritoneal dialysis provided by the present invention, such as Figure 1As shown, the peritoneal pressure monitoring device 10 for peritoneal dialysis includes a pressure monitoring module 110, a posture recognition module 120, a labeling module 130, and a data analysis module 140. The pressure monitoring module 110 is used to monitor the peritoneal pressure of the subject in real time during the current pressure monitoring process to obtain real-time first pressure data; the posture recognition module 120 is used to recognize the subject's posture in real time and determine the subject's real-time target posture; the labeling module 130 is used to label the first pressure data at the corresponding moment according to the subject's real-time target posture and determine the labeled second pressure data; and the data analysis module 140 is used to analyze, if the second pressure data has an abnormality, whether the cause of the abnormality is related to the target posture based on the second pressure data.

[0050] The pressure monitoring module 110 may include a high-precision, high-sensitivity pressure sensor, which may be positioned outside the abdomen or within the peritoneal cavity of the subject being tested. The subject being tested may be a patient undergoing peritoneal dialysis. Typically, the subject may undergo multiple peritoneal dialysis cycles during a single peritoneal dialysis treatment, with corresponding pressure monitoring procedures performed during each peritoneal dialysis cycle.

[0051] The pressure monitoring module 110 can use a sensor or other device to collect the subject's intraperitoneal pressure in real time during the current pressure monitoring process (i.e., the current peritoneal dialysis process) while the subject is undergoing peritoneal dialysis. The module can also convert the measured intraperitoneal pressure into an electrical signal to obtain intraperitoneal pressure data at each moment, which is recorded as first pressure data. This first pressure data is the raw data collected of the subject's intraperitoneal pressure. This first pressure data can include intraperitoneal pressure data at a single moment, or data at multiple moments.

[0052] While monitoring the subject's intra-abdominal pressure data in real time, the posture recognition module 120 can also be used to identify the subject's posture at each moment in real time to obtain the subject's target posture at each moment during the pressure monitoring process. When identifying the subject's posture, posture recognition can be performed on captured images of the subject's movements to obtain the subject's target posture. Alternatively, sensors installed on the subject's body parts can be used to collect the subject's motion data to identify the subject's target posture (for example, a pressure sensor installed on the subject's buttocks can identify the subject's target posture as sitting when a pressure signal is detected). Of course, other methods can also be used to identify the subject's target posture at each moment, which are not specifically limited here. The target posture here can include, for example, standing, sitting, lying (such as lying flat or sideways), etc., or can also include specific angles when standing / sitting / lying flat.

[0053] After identifying the target posture of the object to be measured at each moment, since posture recognition and pressure monitoring are both performed in real time, there is a time correlation between the two. Then, the target posture of the object to be measured at each moment can be marked accordingly to the pressure data at the corresponding moment of the first pressure data through the marking module 130. After marking, the pressure data carrying the posture label can be obtained, which is recorded as the second pressure data.

[0054] Furthermore, the annotation module 130 can also annotate the identified target posture on the first pressure data in real time. During this real-time annotation process, the second pressure data at each moment can also be checked in real time to see if there are any anomalies. If the second pressure data at a particular moment is abnormal, the target posture of the subject at that moment can be determined from the second pressure data at that moment. The second pressure data at that moment can then be analyzed based on the subject's target posture to obtain an analysis result. This analysis result indicates whether the cause of the abnormality in the second pressure data at that moment is related to the target posture at that moment. After obtaining the analysis result, different adjustments can be performed based on the content of the analysis result to ensure that the second pressure data at the next moment is normal / restored to normal. For example, if the cause of the abnormality in the second pressure data at that moment is related to the target posture at that moment, the subject can be prompted to adjust the target posture so that the second pressure data at the next moment is normal / restored to normal. If the cause of the abnormality in the second pressure data at that moment is unrelated to the target posture at that moment, the influence of the target posture can be eliminated by addressing other factors, such as ultrafiltration volume or infusion volume, to eliminate the cause of the abnormality and eliminate the abnormality, ensuring that the second pressure data at the next moment is normal / restored to normal.

[0055] When the above-mentioned real-time detection of whether there is an abnormality in the second pressure data at each moment is performed, it may specifically include: obtaining the pressure threshold range pre-set for the object to be measured, and determining whether the second pressure data at each moment exceeds the pressure threshold range. If exceeded, it can be analyzed that there is an abnormality in the second pressure data at that moment. The pressure threshold range here may be composed of a threshold value, or may be composed of a range consisting of a lower threshold value and an upper threshold value. In addition, the pressure threshold range of the above-mentioned object to be measured may be a personalized pressure threshold range for the object to be measured, which may be specifically set in advance according to the attribute information of the object to be measured, and the attribute may include, for example, age, gender, height, weight, etc., or may also include other information. The pressure threshold ranges of different objects to be measured may be different.

[0056] In this embodiment, the peritoneal pressure monitoring device for peritoneal dialysis includes a pressure monitoring module, a posture recognition module, a labeling module, and a data analysis module. The pressure monitoring module can monitor the peritoneal pressure of the subject to be measured in real time during the current pressure monitoring process to obtain real-time first pressure data; the posture recognition module can recognize the neutron state of the subject to be measured in real time to determine the real-time target posture of the subject to be measured; the labeling module can label the first pressure data at the corresponding time according to the real-time target posture of the subject to be measured to determine the labeled second pressure data; and the data analysis module can analyze whether the cause of the abnormality is related to the target posture based on the second pressure data when an abnormality exists. The peritoneal pressure monitoring device for peritoneal dialysis of the present invention can recognize the real-time posture of the subject to be measured and label the recognized posture on the real-time pressure data. Therefore, when the peritoneal pressure is continuously monitored, the real-time pressure data labeled with the posture can quickly analyze whether the cause of the abnormality is related to the posture, so that the peritoneal pressure data caused by the posture interference of the subject to be measured can be well interpreted and used to guide subsequent diagnosis and treatment, thereby achieving effective use and accurate analysis of the peritoneal pressure data of the subject to be measured.

[0057] The following embodiment describes a possible implementation method of identifying the target posture of an object to be measured through an image of the object to be measured.

[0058] In one embodiment, the posture recognition module 120 is specifically used to obtain an image of the object to be measured in real time, classify the posture of the object to be measured in the image to be measured, and determine the initial posture corresponding to the object to be measured; based on the initial posture and the image to be measured, the posture of the object to be measured is recognized to determine the target posture corresponding to the object to be measured; the accuracy of the target posture is higher than the accuracy of the initial posture.

[0059] An image acquisition device may be pre-installed around the subject to be tested. The image acquisition device is used to capture images of the subject to be tested, including full-body or partial body images. The image acquisition device may be, for example, a camera or a video camera. The image acquisition device can capture images of the subject to be tested, including the entire body or partial body parts, in real time and transmit the captured images to the gesture recognition module 120 for processing. The captured images may be referred to as images to be tested.

[0060] The posture recognition module 120 can acquire the image to be tested transmitted by the image acquisition device in real time and perform two-level posture recognition on the object to be tested in the image, including coarse posture recognition and fine posture recognition. Coarse posture recognition can be implemented using a preset neural network, and fine posture recognition can be implemented using a preset human posture estimation model (such as the MediaPipe model).

[0061] During the rough posture recognition process, optionally, the posture recognition module 120 is specifically used to classify the posture of the object to be measured in the image to be measured using a neural network, and determine the initial posture corresponding to the object to be measured; wherein, the neural network is trained based on multiple training images and the annotated posture corresponding to each training image, and the annotated posture corresponding to each training image is not exactly the same.

[0062] The neural network can be a CNN (Convolutional Neural Network), VGG (Visual Geometry Group), ResNet (Deep Residual Network), etc. It can be pre-trained. Specifically, a dataset of images labeled with different human poses can be collected. This dataset includes multiple training images. These labeled training images are used to train an initial neural network. By adjusting the model parameters and structure of the initial neural network, the initial neural network is enabled to learn and recognize human pose features in images, such as standing, lying, or sitting, and meet certain performance requirements, ultimately obtaining a trained neural network. This trained neural network can recognize the pose of a person in an image after inputting it. After obtaining images of the subject to be tested at various moments in real time, each image can be input into the trained neural network for pose recognition or classification, obtaining the pose of the subject to be tested, which is recorded as the initial pose.

[0063] It should be noted that the image to be tested may be a single frame of image or a video sequence including multiple frames of image.

[0064] After obtaining the initial posture of the object to be measured, the posture of the object to be measured can be further refined. During the refined posture recognition process, the posture recognition module 120 is optionally configured to use a preset human posture estimation model to identify key points of the object to be measured in the image to be measured, determine multiple key points corresponding to the object to be measured, determine multiple target key points related to the initial posture from the multiple key points, and determine the target posture of the object to be measured based on the position information of each target key point.

[0065] The preset human body posture estimation model may be, for example, a MediaPipe model, or other human body posture estimation models.

[0066] After the initial posture of the object to be measured is recognized in real time, the image to be measured at a certain moment can be input into the human posture estimation model, and multiple key points on the object to be measured can be identified through its built-in key point detection model. For example, see Figure 2The key points of the subject's body parts are shown in the figure. The identified key points may include 33 (0-32) key points such as shoulders, elbows, wrists, hips, knees and ankles. The key points of the subject in the image can be represented as Figure 2 The two-dimensional coordinate points in the , each two-dimensional coordinate point can correspond to a specific part on the object to be measured.

[0067] Then, based on the initial posture of the subject to be measured, multiple target key points corresponding to the initial state can be selected from the multiple key points detected above. For example, if the initial posture is sitting or standing, the multiple target key points may include hip key points (such as points 23 and 24), shoulder key points (such as points 11 and 12), and knee key points (such as points 25 and 26). It is understood that the multiple target key points selected for different initial postures are not exactly the same.

[0068] After selecting multiple target key points related to the initial state from the multiple key points obtained by detection, a more precise target pose of the object to be measured can be calculated based on the positional relationship between each target key point. Here, by selecting multiple target key points related to the initial state from the multiple key points, and the number of selected multiple target key points is also smaller than the number of multiple key points obtained by detection, this can reduce the computational cost of pose recognition while improving pose recognition accuracy.

[0069] Furthermore, when a more precise target posture of the object to be measured is calculated through the positional relationship between each target key point, optionally, taking the initial posture including a sitting posture or a standing posture, and the multiple target key points including a hip key point, a shoulder key point and a knee key point as an example, the above-mentioned posture recognition module 120 is specifically used to calculate a first vector based on the position information of the hip key point and the position information of the shoulder key point; and calculate a second vector based on the position information of the hip key point and the position information of the knee key point; calculate the angle between the first vector and the second vector, and determine the degree of waist bending of the object to be measured based on the angle; and determine the target posture based on the degree of waist bending.

[0070] Among them, when the initial posture is any of sitting, standing and lying, the degree / angle of waist bending has a greater impact on the abdominal cavity pressure. After selecting the multiple target key points of the hip key point, the shoulder key point and the knee key point, the position information of each target key point in the image to be measured can also be obtained accordingly; then, the vector between the hip key point and the shoulder key point (recorded as the first vector a) and the vector between the hip key point and the knee key point (recorded as the second vector b) can be calculated based on the position information of these target key points; then, the angle between the first vector and the second vector can be calculated by the calculation formula of the angle between the two vectors, and the angle can be used as the waist bending degree of the object to be measured, or the angle can be post-processed to obtain the waist bending degree of the object to be measured; then, the waist bending degree of the object to be measured and the initial posture can be combined to determine the target posture of the object to be measured at this time, for example, the target posture can be: a bent-over standing posture with an angle close to 90 degrees. In this way, the degree of body bending when sitting, standing or lying (such as side-lying) can be finely judged.

[0071] For example, see Figure 3 As shown in the schematic diagram of calculating the first vector and the second vector and the angle between them, it can be seen that the angle between the first vector and the second vector is different, and the posture of the object to be measured is also different.

[0072] Continue to see Figure 4 From the schematic diagrams of various target postures shown, it can be seen that there are different refined postures for sitting postures, such as "sitting leaning back", "sitting upright", "sitting leaning forward", "sitting bending forward", etc. The abdominal pressure data under different target postures are also different. By refining the target posture of the object to be measured, the accuracy of subsequent analysis of the pressure data marked with the target posture can be improved, thereby providing a more accurate data basis for subsequent processing.

[0073] In this embodiment, by performing two-stage recognition of the posture of the object under test in the image under test, namely, coarse recognition and fine recognition, the accuracy or precision of the target posture ultimately obtained can be improved. Furthermore, a neural network is used to perform coarse recognition of the posture of the object under test, which can improve recognition efficiency while ensuring posture recognition accuracy. Furthermore, a human posture estimation model is used to detect the key points of the object under test and, in combination with the initial posture, multiple target key points are selected for refined posture recognition. This can reduce the amount of posture recognition computation and improve posture recognition accuracy. Furthermore, the position information of the target key points can be used to calculate the relevant vectors and the angles between the vectors. This refined calculation process can further improve posture recognition accuracy.

[0074] The following embodiment illustrates the process of analyzing the posture change trend in the current pressure monitoring process.

[0075] In one embodiment, in the current pressure monitoring process, the target posture of the above-mentioned object to be measured is not exactly the same at each moment; the above-mentioned data analysis module is also used to extract the second pressure data belonging to the same target posture at different moments from the second pressure data, and determine the change trend of the abdominal pressure under the corresponding target posture according to the second pressure data of each target posture at different moments; the change trend of the abdominal pressure under each target posture is different; and, the change trend of the abdominal pressure under each target posture is compared, and the optimal posture of the object to be measured in the next pressure monitoring process is determined from each target posture.

[0076] During the current pressure monitoring process, the subject may change postures, resulting in a variety of different target postures. For example, if the current pressure monitoring duration is 2 hours, the subject may be in a 90-degree sitting position for 0-5 minutes, and a 180-degree standing position for 6-10 minutes, and may change postures further in the future. The second pressure data belonging to the same target posture can be extracted from the second pressure data at each moment in the current pressure monitoring process. The second pressure data for the same target posture can then be arranged in chronological order and curve-fitted to obtain the changing trend of the abdominal pressure under that target state.

[0077] The changing trends of the abdominal pressure under different target postures are generally different. Therefore, here we can compare the changing trends of the abdominal pressure corresponding to each target posture, select the optimal changing trend, and use the target posture corresponding to the optimal changing trend as the optimal posture in the next cycle / next pressure monitoring process.

[0078] When making a selection here, you can choose according to the following conditions, such as the smoothness of the change trend, whether the second pressure data on the change trend exceeds the pressure threshold range, etc.; for example, here you can choose the change trend with the smoothest change trend and the second pressure data within the pressure threshold range as the optimal trend, and then obtain the corresponding optimal posture.

[0079] In this embodiment, the optimal posture of the object to be measured in the next pressure monitoring process is determined by comparing the changing trends of the abdominal pressure of different target postures in the current pressure monitoring process. This can ensure that the pressure data in the next pressure monitoring process is as close as possible to the pressure threshold range, thereby improving the effect of pressure monitoring.

[0080] The following embodiment describes the process of analyzing whether the cause of the abnormality in the second pressure data is related to the target posture in the current pressure monitoring process.

[0081] In one embodiment, the data analysis module is specifically configured to adjust the target posture of the subject at the current moment, and after the adjustment, detect whether there is an abnormality in the second pressure data at a subsequent moment; if there is no abnormality in the second pressure data at the subsequent moment, it is determined that the cause of the abnormality in the second pressure data at the current moment is related to the target posture at the current moment; if there is still an abnormality in the second pressure data at the subsequent moment, it is determined that the cause of the abnormality in the second pressure data at the current moment is related to the peritoneal dialysis fluid infusion volume or ultrafiltration volume of the subject.

[0082] When the second pressure data at the current moment is abnormal (generally due to high intra-abdominal pressure), the target posture at the current moment can be adjusted. For example, the target posture at the current moment can be adjusted to a more appropriate posture (for example, when the intra-abdominal pressure is too high, the posture can be adjusted to a posture with lower intra-abdominal pressure). The intra-abdominal pressure data at the adjusted posture can then be tested for abnormalities. Specifically, the second pressure data at subsequent moments (which can be one or more moments) after the current moment can be tested for abnormalities. If the second pressure data at the subsequent moments is not abnormal, it is determined that the cause of the abnormality in the second pressure data at the current moment is related to the target posture.

[0083] Furthermore, if the second pressure data at a subsequent moment still exhibits an abnormality (generally, high intraperitoneal pressure) after the posture adjustment, the cause of the abnormality in the second pressure data at the current moment is determined to be unrelated to the target posture and may be related to the subject's peritoneal dialysis fluid infusion or ultrafiltration volume, such as excessive infusion or ultrafiltration. Furthermore, if the abnormality in the second pressure data at the current moment indicates low intraperitoneal pressure, the cause of the abnormality is insufficient peritoneal dialysis fluid infusion or insufficient ultrafiltration volume.

[0084] For each of the above abnormal causes, the subsequent actions may vary. For example, if the abnormal cause is the subject's posture, the subject will be reminded to adjust their posture; if the abnormal cause is an excessive amount of peritoneal dialysis fluid infused, the medical staff will be reminded to modify the prescription and recommend an appropriate infusion volume based on the monitored second pressure data; if the abnormal cause is an excessive amount of ultrafiltration, the medical staff will be reminded to modify the prescription and recommend an appropriate fluid change time based on the monitored second pressure data.

[0085] In this embodiment, when there is an abnormality in the second pressure data, it is first determined whether it is related to the posture of the object to be measured. If it is not related, it is then determined whether it is due to other reasons. In this way, the accuracy and efficiency of determining the cause of the abnormality in the pressure data can be improved through a multi-layer judgment method, thereby improving the accuracy and efficiency of the subsequent execution operations.

[0086] The following embodiment illustrates the process of generating an alarm when the second pressure data is abnormal.

[0087] In one embodiment, the above-mentioned peritoneal pressure monitoring device for peritoneal dialysis may further include: an alarm module, which is used to compare the real-time second pressure data with a preset pressure threshold range, and when the second pressure data at any moment exceeds the pressure threshold range, determine that the second pressure data at any moment is abnormal, and output an alarm message; wherein the above-mentioned alarm message includes the reason why the second pressure data at any moment is abnormal and decision-making recommendation information determined based on the reason.

[0088] As mentioned above, during real-time pressure monitoring, the real-time annotated second pressure data can be compared with a pre-set pressure threshold range for the subject to be measured, thereby determining whether the second pressure data at each moment exceeds the pressure threshold range. If the second pressure data at any moment exceeds the upper limit of the pressure threshold range (i.e., is greater than the upper limit) or exceeds the lower limit of the pressure threshold range (i.e., is less than the lower limit), the second pressure data at that moment is determined to be abnormal.

[0089] When an abnormality is determined in the second pressure data at any moment, the cause of the abnormality and the decision-making recommendation information corresponding to the cause of the abnormality can also be determined according to the above method (i.e., the subsequent operation described above). The presence of the abnormality in the second pressure data at any moment, the cause of the abnormality, and the corresponding decision-making recommendation information can then be output to the user / medical staff via an alarm message so that appropriate actions can be taken in a timely manner. In addition, the alarm message can be output in the form of voice, text, sound, light, etc.

[0090] Furthermore, when the second pressure data at any moment is abnormal, different alarm messages can be output to distinguish between exceeding the upper limit of the pressure threshold range and exceeding the lower limit of the pressure threshold range. For example, when the pressure exceeds the lower limit of the pressure threshold range, a low abdominal pressure value reminder can be issued to medical staff and the subject, suggesting that the medical staff increase the peritoneal dialysis fluid infusion volume and infusion time based on the actual situation.

[0091] In this embodiment, by outputting an alarm message when the second pressure data at any moment exceeds the pressure threshold range, it is convenient for medical staff and the subject to be tested to promptly know that there is an abnormality in the current pressure data and take corresponding operations in time to avoid the occurrence of abnormal situations, thereby ensuring the effectiveness of the treatment process.

[0092] The following embodiment illustrates the process of generating a user-personalized treatment plan based on multiple pressure monitoring results.

[0093] In one embodiment, the peritoneal pressure monitoring device for peritoneal dialysis may further include: an auxiliary decision-making module for generating a personalized treatment plan corresponding to the subject to be tested based on the second pressure data of the subject to be tested in multiple pressure monitoring processes, the clinical treatment data of the subject to be tested, and the living habit data of the subject to be tested; the above-mentioned clinical treatment data includes the initial treatment plan and / or predicted treatment results.

[0094] Among them, in the long-term multiple pressure monitoring process of the subject to be tested, the second pressure data of each marked posture can be collected, and the living habit data of the subject to be tested (such as sleep time, activity pattern, etc.) can be collected at the same time, and the initial treatment plan (such as treatment prescription) set by the doctor for the subject to be tested in advance and the treatment results predicted based on the initial treatment plan (such as dialysis adequacy, ultrafiltration volume, etc.) can also be obtained. Then, based on these data, the influence of the subject's activity pattern, living habits, and treatment prescription on the abdominal pressure and treatment results can be analyzed, and a comprehensive analysis can be performed to generate a personalized treatment plan for the subject to be tested, including lifestyle and treatment prescription recommendations. This personalized treatment plan, for example: (1) It is found that the abdominal pressure of the subject to be tested is generally low when sleeping at night, which may be related to the sleeping posture, and it is recommended that the subject adjust the sleeping posture or mattress hardness; (2) It is found that in the sitting position of 90 degrees, the abdominal pressure reaches the maximum after each dialysis for 2 hours, and the efficiency is significantly reduced or the risk of complications increases if dialysis is continued thereafter. When lying flat, it takes 3 hours for the abdominal pressure to reach the maximum. Therefore, it is recommended that medical staff adjust the dialysis time of the subject to be tested according to the body posture to improve the dialysis efficiency.

[0095] In this embodiment, by generating a corresponding personalized treatment plan based on the second pressure data, clinical treatment data, and lifestyle data of the subject in multiple pressure monitoring processes, a treatment plan that is more suitable for the subject can be determined for the subject, which helps to optimize the treatment plan and improve the treatment effect of peritoneal dialysis on the subject.

[0096] It can be seen from the description of the above embodiments that the technical solutions of the embodiments of the present invention have the following technical effects:

[0097] Improved data accuracy: Through posture recognition, the interference of posture changes on abdominal pressure measurement is eliminated, making the data closer to the actual value.

[0098] Real-time monitoring and feedback: This peritoneal pressure monitoring device for peritoneal dialysis can continuously and in real time monitor the abdominal pressure and patient posture, providing immediate data feedback to medical staff.

[0099] Decision support: Accurate data provides strong support for medical staff to formulate and adjust treatment plans, helping to improve treatment outcomes and prevent complications.

[0100] Enhanced patient self-management capabilities: By displaying real-time peritoneal pressure data and posture information, patients can more intuitively understand their physical condition. Based on the system's reminders and suggestions, patients can independently adjust their treatment plans, such as adjusting their posture and controlling the volume and timing of peritoneal dialysis fluid infusion. This enhanced self-management capability not only reduces the burden on medical staff but also increases patient engagement and satisfaction with treatment.

[0101] Improved patient quality of life: Timely intervention and personalized treatment can significantly reduce the incidence of peritoneal dialysis-related complications. For example, high intra-abdominal pressure can cause symptoms such as abdominal discomfort and dyspnea, while low intra-abdominal pressure can lead to inadequate dialysis. The technical solutions of the embodiments of the present invention can promptly identify these problems and take appropriate measures, thereby reducing the occurrence of complications and improving patient treatment outcomes.

[0102] Reduced medical costs: Timely monitoring and feedback can reduce unnecessary medical interventions and complications, thereby lowering medical costs. At the same time, optimized treatment decisions and personalized treatment management can improve treatment efficiency and further reduce medical costs.

[0103] The following describes an abdominal cavity pressure monitoring system for peritoneal dialysis provided by an embodiment of the present invention.

[0104] Figure 5 This is a schematic diagram of the structure of the peritoneal pressure monitoring system for peritoneal dialysis provided by the present invention, see Figure 5 As shown, the system may include an image acquisition device 20 and the peritoneal pressure monitoring device 10 for peritoneal dialysis in the above embodiment; the above image acquisition device 20 is used to obtain the image to be measured of the object to be measured in real time and transmit the image to the peritoneal pressure monitoring device 10 for peritoneal dialysis.

[0105] The image acquisition device 20 can be a camera, a video camera, or other acquisition device with a camera head, etc. Its specific location can be set according to actual conditions, as long as it can capture the entire body or part of the body of the subject to be measured.

[0106] For the processes of joint abdominal pressure monitoring, posture recognition, labeling, data analysis, etc. performed between the image acquisition device 20 and the abdominal pressure monitoring device 10 for peritoneal dialysis, please refer to the explanation in the above-mentioned embodiment of the abdominal pressure monitoring device 10 for peritoneal dialysis, and will not be repeated here.

[0107] In this embodiment, the peritoneal pressure monitoring system for peritoneal dialysis includes an image acquisition device and a peritoneal pressure monitoring device for peritoneal dialysis, wherein the image acquisition device can transmit the acquired image to the peritoneal pressure monitoring device for peritoneal dialysis for posture recognition, and the peritoneal pressure monitoring device for peritoneal dialysis includes a pressure monitoring module, a posture recognition module, a labeling module and a data analysis module, wherein the pressure monitoring module can monitor the peritoneal pressure of the object to be measured in real time during the current pressure monitoring process to obtain real-time first pressure data; the posture recognition module can identify the neutron state of the object to be measured in real time to determine the real-time target posture of the object to be measured; the labeling module can label the first pressure data at the corresponding moment according to the real-time target posture of the object to be measured to determine the labeled second pressure data; the data analysis module can analyze whether the cause of the abnormality is related to the target posture based on the second pressure data when there is an abnormality in the second pressure data. The abdominal pressure monitoring system for peritoneal dialysis of the present invention can recognize the real-time posture of the subject to be measured and mark the recognized posture on the real-time pressure data. In this way, when the abdominal pressure is continuously monitored, the pressure data with real-time posture marking can be used to quickly analyze whether the cause of the abnormality is related to the posture when there is an abnormality in the abdominal pressure. The abdominal pressure data of the subject to be measured caused by posture interference can be well interpreted and can be used to guide subsequent diagnosis and treatment, thereby realizing the effective use and accurate analysis of the abdominal pressure data of the subject to be measured.

[0108] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0109] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0110] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A peritoneal pressure monitoring device for peritoneal dialysis, characterized in that: include: The pressure monitoring module is used to monitor the abdominal pressure of the subject to be tested in real time during the current pressure monitoring process to obtain real-time first pressure data; A posture recognition module is used to recognize the posture of the object to be measured in real time and determine the real-time target posture of the object to be measured; a marking module, configured to mark the first pressure data at a corresponding moment according to the real-time target posture of the object to be measured, and determine the second pressure data after real-time marking; a data analysis module for analyzing, if an abnormality exists in the real-time annotated second pressure data, whether a cause of the abnormality is related to the target posture based on the real-time annotated second pressure data; determining whether the abnormality exists in the real-time annotated second pressure data is obtained by comparing the real-time annotated second pressure data with a personalized pressure threshold range of the object to be measured, where the personalized pressure threshold range of the object to be measured is determined based on attribute information of the object to be measured; The posture recognition module is specifically used to obtain a test image of the test object in real time, and use a neural network to classify the posture of the test object in the test image to determine the initial posture corresponding to the test object; based on the initial posture and the test image, the posture of the test object is recognized to determine the target posture corresponding to the test object; The accuracy of the target posture is higher than the accuracy of the initial posture; The posture recognition module is specifically used to identify the key points of the object to be measured in the image to be measured using a preset human posture estimation model, and determine multiple key points corresponding to the object to be measured; determining a plurality of target key points associated with the initial pose from the plurality of key points; Determining the target posture of the object to be measured based on the position information and position relationship of each target key point; The human posture estimation model is a MediaPipe model, and the number of the multiple target key points is less than the number of the multiple key points; The data analysis module is specifically configured to adjust the target posture of the subject at a current moment, and after the adjustment, detect whether there is an abnormality in the second pressure data at a subsequent moment; if there is no abnormality in the second pressure data at the subsequent moment, determine that the cause of the abnormality in the second pressure data at the current moment is related to the target posture at the current moment; if there is still an abnormality in the second pressure data at the subsequent moment, determine that the cause of the abnormality in the second pressure data at the current moment is related to the peritoneal dialysis fluid infusion volume or ultrafiltration volume of the subject; and determine a subsequent operation to be performed based on the cause of the abnormality, where different causes of the abnormality correspond to different subsequent operations; An auxiliary decision-making module is used to generate a personalized treatment plan corresponding to the subject to be tested based on the second pressure data of the subject to be tested in multiple pressure monitoring processes, the clinical treatment data of the subject to be tested, and the living habit data of the subject to be tested; the clinical treatment data includes the initial treatment plan and / or predicted treatment results.

2. The peritoneal pressure monitoring device for peritoneal dialysis according to claim 1, characterized in that: The initial posture includes any one of a sitting posture, a standing posture and a lying posture, the target key points include a hip key point, a shoulder key point and a knee key point, and the posture recognition module is specifically used to Calculating a first vector based on the position information of the hip key point and the position information of the shoulder key point; and calculating a second vector based on the position information of the hip key point and the position information of the knee key point; calculating an angle between the first vector and the second vector, and determining a degree of waist bending of the subject to be measured according to the angle; The target posture is determined according to the degree of waist bending.

3. The peritoneal cavity pressure monitoring device for peritoneal dialysis according to any one of claims 1 to 2, characterized in that: The neural network is trained based on a plurality of training images and the annotation posture corresponding to each training image, and the annotation postures corresponding to the training images are not completely the same.

4. The peritoneal cavity pressure monitoring device for peritoneal dialysis according to any one of claims 1 to 2, characterized in that: In the current pressure monitoring process, the target posture of the object to be measured is not exactly the same at each moment; The data analysis module is further configured to extract second pressure data belonging to the same target posture at different times from the second pressure data, and determine a change trend of the abdominal cavity pressure under the corresponding target posture based on the second pressure data at different times for each target posture; the change trend of the abdominal cavity pressure under each target posture is different; Furthermore, the changing trends of the abdominal cavity pressure under the target postures are compared, and the optimal posture of the subject to be measured in the next pressure monitoring process is determined from the target postures.

5. The peritoneal cavity pressure monitoring device for peritoneal dialysis according to any one of claims 1 to 2, characterized in that: Also includes: an alarm module, configured to compare the real-time second pressure data with a preset pressure threshold range, and when the second pressure data at any moment exceeds the pressure threshold range, determine that the second pressure data at any moment is abnormal, and output an alarm message; The alarm information includes the reason why the second pressure data at any moment is abnormal and decision-making suggestion information determined based on the reason.

6. A peritoneal pressure monitoring system for peritoneal dialysis, characterized in that: comprising an image acquisition device and the peritoneal pressure monitoring device for peritoneal dialysis according to any one of claims 1 to 5; The image acquisition device is used to acquire the image to be measured of the object to be measured in real time, and transmit the image to be measured to the peritoneal pressure monitoring device for peritoneal dialysis.

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