CN117476237B - Simulation evaluation system and method for old people operation - Google Patents
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Abstract
The invention discloses a simulation evaluation system and a simulation evaluation method for old people surgery, which belong to the technical field of intelligent medical treatment, wherein the system comprises: the human body simulation model construction module is used for acquiring human body data and constructing a human body simulation model according to the human body data; the simulation operation module is connected with the human body simulation model construction module and is used for performing operation simulation on the human body simulation model to obtain a simulation operation result; and the postoperative evaluation module is connected with the simulation operation module and is used for evaluating the feasibility of the operation according to the simulation operation result. The system realizes preoperative previewing through an analog simulation technology, can find out an optimal operation scheme through previewing, and the operation simulation is helpful for medical team to identify potential problems and avoid accidents and complications in operation before actual operation, so that the risk of old people in operation is reduced.
Description
Technical Field
The invention belongs to the technical field of intelligent medical treatment, and particularly relates to a simulation evaluation system and method for old people operation.
Background
With the age, the performance of each organ of the human body is gradually reduced, and the most prominent manifestation is the reduced immunity of the old and easy illness.
The earliest form of preoperative simulation could be traced back to the beginning of the 20 th century. At that time, doctors may simulate surgical procedures using simple models, specimens, and hand-made simulators. These methods rely primarily on the experience and intuition of the physician, lacking in highly accurate simulation. With the development of computer technology, preoperative simulation has entered the digital age. Advances in medical image processing, computer aided design, and virtual reality techniques have enabled physicians to use computer programs to simulate surgical procedures. These procedures can provide doctors with highly accurate visualization of anatomy and surgical procedures, helping them practice and plan surgery.
However, in the prior art, the physical simulation is performed through 3D printing, and the simulation technology cannot accurately simulate the human body, so that the operation simulation result is inaccurate.
Disclosure of Invention
In order to solve the technical problems, the invention provides a simulation evaluation system and a simulation evaluation method for old people operation, which are used for solving the problems existing in the prior art.
To achieve the above object, the present invention provides a simulation evaluation system for an operation for elderly people, comprising:
The human body simulation model construction module is used for acquiring human body data and constructing a human body simulation model according to the human body data;
The simulation operation module is connected with the human body simulation model construction module and is used for performing operation simulation on the human body simulation model to obtain a simulation operation result;
And the postoperative evaluation module is connected with the simulation operation module and is used for evaluating the feasibility of the operation according to the simulation operation result.
Preferably, the human body simulation model construction module comprises a human body data acquisition submodule and a digital twin submodule;
The human body data acquisition submodule is used for acquiring blood circulation data and acquiring geometric data of a human body based on a magnetic resonance imaging technology;
the digital twin sub-module is used for constructing a human body simulation model based on the blood circulation data and the geometric data of the human body.
Preferably, the digital twin submodule comprises a human body fluid model building unit, a human body structure model building unit and a mapping unit;
the human body fluid model building unit is used for building a human body fluid model according to blood circulation data;
the human body structure model building unit is used for building a human body structure model according to geometrical data of a human body;
The mapping unit is used for mapping the human body fluid model into a human body structure model to obtain a human body simulation model.
Preferably, the human body fluid model building unit includes:
a first geometric model building subunit, configured to build a first geometric model of a human body according to blood circulation data of the human body;
the first grid division subunit is used for carrying out partition processing on the first geometric model, determining the type of the grid unit and carrying out grid division on the calculation domain based on the type of the grid unit;
The first model generation subunit is used for establishing a blood non-Newtonian flow field for the divided calculation domain based on the lattice Boltzmann method, and correcting the blood non-Newtonian flow field by adding an additional item related to the grid shear rate in a particle distribution function in a balanced state to obtain a human body fluid model.
Preferably, the human body structure model building unit includes:
a second geometric model building unit for building a second geometric model of the pump based on geometric data of the human body;
The second grid dividing unit is used for importing grid division of the part to be analyzed through the first grid dividing module;
and the second model generation subunit is used for assigning values to the divided grids based on biomechanics to obtain a human body structure model.
Preferably, the simulation operation module comprises a scheme planning sub-module and a simulation operation sub-module;
the scheme planning submodule is used for planning an operation position and an operation step;
the simulation operation submodule is used for carrying out simulation operation according to the planned operation position and operation steps by a doctor to obtain a simulation operation result.
Preferably, the postoperative evaluation module comprises a feasibility sub-module and a postoperative recovery sub-module;
The feasibility submodule is used for calculating the success rate of the operation according to the simulation operation result;
the postoperative recovery submodule is used for calculating the postoperative recovery time according to the simulation operation result.
The invention also provides a simulation evaluation method for the old surgery, which comprises the following steps:
collecting human body data and constructing a human body simulation model according to the human body data;
Performing operation simulation on the human body simulation model to obtain a simulation operation result;
and performing operation feasibility assessment according to the simulation operation result.
Preferably, the method for constructing the human body simulation model comprises the following steps:
collecting blood circulation data and geometrical data of a human body based on a magnetic resonance imaging technology;
And constructing a human body simulation model based on the blood circulation data and the geometric data of the human body.
Preferably, the method for constructing the human body simulation model comprises the following steps:
Constructing a human body fluid model according to blood circulation data;
constructing a human body structure model according to the geometric data of the human body;
And mapping the human body fluid model into a human body structure model to obtain a human body simulation model.
Compared with the prior art, the invention has the following advantages and technical effects:
According to the simulation evaluation system and method for the old people operation, preoperative previewing is achieved through the simulation technology, an optimal operation scheme can be found through previewing, and operation simulation is helpful for medical teams to identify potential problems before actual operation and avoid accidents and complications in operation, so that risks of the old people in operation are reduced.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application. In the drawings:
Fig. 1 is a block diagram of a simulation evaluation system for an operation for the elderly according to an embodiment of the present invention.
Detailed Description
It should be noted that, without conflict, the embodiments of the present application and features of the embodiments may be combined with each other. The application will be described in detail below with reference to the drawings in connection with embodiments.
It should be noted that the steps illustrated in the flowcharts of the figures may be performed in a computer system such as a set of computer executable instructions, and that although a logical order is illustrated in the flowcharts, in some cases the steps illustrated or described may be performed in an order other than that illustrated herein.
As shown in fig. 1, the present invention proposes a simulation evaluation system for an operation for the elderly, comprising:
the human body simulation model construction module is used for acquiring human body data and constructing a human body simulation model according to the human body data;
further optimizing the scheme, the human body simulation model building module comprises a human body data acquisition submodule and a digital twin submodule;
The human body data acquisition submodule is used for acquiring blood circulation data and acquiring geometric data of a human body based on a magnetic resonance imaging technology;
Magnetic resonance imaging techniques utilize magnetic fields and radio waves to generate high resolution images of organs within the human body. Magnetic resonance imaging techniques have a better resolution for soft tissues, and the shape of blood vessels and viscera is often acquired by magnetic resonance imaging techniques.
Image segmentation is performed using image processing software or algorithms to separate structures (e.g., blood vessels and viscera) to be simulated from the medical image. The process involves complex image processing technology and artificial intelligence algorithm, and accurately identifies the positions and shapes of important structures such as blood vessels and viscera through identification and analysis of medical images, so as to provide an accurate model for subsequent simulation.
The specific steps of image segmentation are as follows:
pretreatment: before segmentation, the magnetic resonance image is preprocessed, including denoising, image enhancement, brightness and contrast adjustment and other operations, so that the image quality and accuracy are improved.
Feature extraction: the shape and edges are selected to describe different objects in the magnetic resonance image.
Image segmentation algorithm selection: and selecting a watershed algorithm to divide the image, wherein the watershed algorithm divides the magnetic resonance image into different areas based on gradient information of the magnetic resonance image, and is suitable for processing overlapped objects.
Image segmentation: the images are separated into different objects using a watershed algorithm. The result of this step is a signature in which each object has a unique identifier.
Post-treatment: the segmentation results are further processed to remove unwanted small regions, merge adjacent regions, or perform other corrective actions.
The digital twin sub-module is used for constructing a human body simulation model based on blood circulation data and geometric data of a human body. The module adopts an advanced data driving method, and establishes a mapping relation between a real human body and a virtual human body by performing deep learning and pattern recognition on blood circulation data and geometric data of the human body.
The digital twin submodule comprises a human body fluid model building unit, a human body structure model building unit and a mapping unit. These units work cooperatively to convert blood flow data and geometric data of the human body into a simulation model with a high degree of realism and accuracy.
The human body fluid model building unit is used for building a human body fluid model according to blood circulation data. The process adopts advanced technologies such as Computational Fluid Dynamics (CFD) and the like to accurately simulate the flow condition of blood in a human body, and establishes a real human body blood flow model.
The human body structure model building unit is used for building a human body structure model according to the geometric data of the human body. The process establishes a fine human body structure model comprising various components such as muscles, bones, organs and the like through three-dimensional reconstruction of geometrical data of a human body.
The mapping unit is used for mapping the human body fluid model into the human body structure model to obtain a human body simulation model. The process realizes the dynamic interaction of the real human body and the virtual human body, and maps the real blood flow condition into the virtual human body model, thereby obtaining a highly real and accurate human body simulation model.
The digital twin submodule also comprises a first geometric model building submodule, a first grid dividing submodule, a first model generating submodule and the like, and the submodules work cooperatively to realize fine modeling and correction of the human body fluid model.
The first geometric model building subunit is used for building a first geometric model of the human body according to blood circulation data of the human body. The process adopts advanced modeling technology, and establishes a geometrical model of the structure of the blood vessel, viscera and the like of the real human body according to blood circulation data.
The first grid dividing sub-unit is used for carrying out partition processing on the first geometric model, determining the type of the grid unit and carrying out grid division on the calculation domain based on the type of the grid unit. The process adopts an advanced grid generation technology, generates corresponding grids according to the geometric shapes of structures such as blood vessels, viscera and the like, and provides accurate discretization for subsequent numerical calculation.
The first model generation subunit is used for establishing a blood non-Newtonian flow field for the divided calculation domain based on the lattice Boltzmann method, and correcting the blood non-Newtonian flow field by adding an additional item related to the grid shear rate in a particle distribution function in an equilibrium state to obtain a human body fluid model. The process realizes the accurate simulation of the human blood flow and establishes a real human blood flow model.
The particle distribution function in the corrected equilibrium state is:
Where B represents a local shear rate dependent variable, ω i is a weight coefficient, ρ and u are macroscopic density and velocity, respectively, e i is a discrete velocity, i is a discrete velocity direction, D is a strain rate tensor, and c s is a lattice sound velocity.
The digital twin submodule also comprises a second geometric model building unit, a second grid dividing unit, a second model generating submodule and the like, and the submodules work cooperatively to realize fine modeling and assignment of the human body structure model.
The second geometric model building unit is used for building a second geometric model of the pump based on geometric data of the human body. The process establishes a fine human body structure model comprising various components such as muscles, bones, organs and the like through three-dimensional reconstruction of geometrical data of a human body.
The second meshing unit is used for importing meshing of the part to be analyzed through the first meshing module. This process generates a corresponding mesh from the geometry of the human body structural model, providing accurate discretization for subsequent numerical calculations.
The second model generation subunit is used for assigning values to the divided grids based on biomechanics to obtain a human body structure model. The process realizes the accurate assignment of the human body structure model and establishes a real human body structure model.
The simulation operation module is connected with the human body simulation model construction module and is used for performing operation simulation on the human body simulation model to obtain a simulation operation result. The module adopts an advanced virtual reality technology, and a doctor can perform simulated operation according to the condition of a patient to obtain a simulated operation result, so that the feasibility and effect of the operation are better evaluated.
The simulation operation module comprises a scheme planning sub-module and a simulation operation sub-module. The scheme planning submodule is used for planning operation positions and operation steps, and a doctor can formulate a customized operation scheme according to the specific situation of a patient. The simulation operation submodule is used for carrying out simulation operation according to the planned operation position and operation steps by a doctor to obtain a simulation operation result, so that the effect and risk of the operation are better estimated.
The postoperative evaluation module is connected with the simulation operation module and is used for performing operation feasibility evaluation according to the simulation operation result. The module adopts advanced machine learning technology, and evaluates the feasibility and effect of the operation through data analysis and pattern recognition of the simulation operation result, thereby providing valuable reference comments and suggestions for doctors.
The specific steps of the surgical feasibility assessment are as follows:
First, data related to the procedure needs to be collected, including medical image data of the patient (e.g., CT, MRI, X-ray images), procedure history, video recordings of the procedure, etc. Cases of successful surgery and failed surgery are included in these data so that the neural network can learn the characteristics in different situations.
Data preprocessing is a process of data cleansing and normalization. This includes image denoising, alignment, segmentation, integration of different types of data into a consistent format, etc., to ensure that the neural network can process the data efficiently.
Relevant features relating to the feasibility of the procedure are extracted from the medical image data. Such as by Convolutional Neural Networks (CNNs) to detect structures, organs, vessels, etc. in the image.
Surgical feasibility assessment requires marking data regarding the success and failure of surgery. These markers may include binary labels (success/failure) or other related indicators such as surgical time, bleeding volume, etc.
Surgical feasibility assessment was performed by Convolutional Neural Network (CNN).
The convolutional neural network is trained using the prepared data set. During training, the neural network will learn to extract features from the input data and predict the feasibility of the procedure.
The trained neural network is evaluated and validated, including using a separate test dataset to test the performance of the model, including accuracy, recall, precision, and the like. And the robustness of the model is assessed by cross-validation.
After verification is passed, the medical professional uses this feasibility assessment model to assess the surgical feasibility of the patient.
The postoperative evaluation module comprises a feasibility sub-module and a postoperative recovery sub-module. The feasibility submodule is used for calculating the success rate of the operation according to the simulated operation result and providing operation risk assessment and optimization scheme suggestion for doctors. The postoperative recovery submodule is used for calculating the postoperative recovery time according to the simulated operation result and providing valuable reference basis for doctors to make a recovery plan.
The embodiment also provides a simulation evaluation method for the operation of the elderly, which comprises the following steps:
collecting human body data and constructing a human body simulation model according to the human body data;
Performing operation simulation on the human body simulation model to obtain a simulation operation result;
and performing operation feasibility assessment according to the simulation operation result.
Further optimizing the scheme, the method for constructing the human body simulation model comprises the following steps:
collecting blood circulation data and geometrical data of a human body based on a magnetic resonance imaging technology;
And constructing a human body simulation model based on the blood circulation data and the geometric data of the human body.
Further optimizing the scheme, the method for constructing the human body simulation model comprises the following steps:
Constructing a human body fluid model according to blood circulation data;
constructing a human body structure model according to the geometric data of the human body;
mapping the human body fluid model into the human body structure model to obtain a human body simulation model.
In summary, the significant advantages of the present invention over the prior art are summarized as follows:
risk reduction: surgical simulation allows medical teams to have potential problems and risks prior to the actual surgery. This helps to avoid accidents and complications during surgery, reducing the risk to the patient.
Improving the surgical skill: through simulated surgery, surgeons and medical teams can practice and improve surgical skills, which is helpful for improving the professional level, reducing errors and improving the success rate of surgery.
Improvement collaboration: the operation simulation can help the medical team to run in the simulation environment, and the cooperation and communication capacity is improved.
Customizing a surgical scheme: simulation allows a physician to tailor a custom surgical plan to each patient's specific situation. This helps to optimise the effect of the procedure.
The operation time is reduced: by simulating the procedure in advance, the physician can better plan each step, reducing the procedure time. This helps to reduce the risk of surgery, reduce anesthesia time, shorten bed occupation time, and improve hospital resource utilization.
The present application is not limited to the above-mentioned embodiments, and any changes or substitutions that can be easily understood by those skilled in the art within the technical scope of the present application are intended to be included in the scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.
Claims (6)
1. A simulated evaluation system for surgery on elderly persons, comprising:
The human body simulation model construction module is used for acquiring human body data and constructing a human body simulation model according to the human body data;
The human body simulation model construction module comprises a human body data acquisition submodule and a digital twin submodule;
The human body data acquisition submodule is used for acquiring blood circulation data and acquiring geometric data of a human body based on a magnetic resonance imaging technology;
the digital twin submodule is used for constructing a human body simulation model based on the blood circulation data and the geometric data of a human body;
the method also comprises the following specific steps of segmenting the magnetic resonance image before constructing the human body simulation model:
preprocessing the magnetic resonance image, wherein the preprocessing comprises denoising, image enhancement, brightness adjustment and contrast adjustment;
Selecting the shape and edges as features to describe different objects in the magnetic resonance image, and using a watershed algorithm to divide the magnetic resonance image into different objects;
removing unnecessary small areas in the segmentation result and merging adjacent areas;
the digital twin submodule comprises a human body fluid model building unit, a human body structure model building unit and a mapping unit;
the human body fluid model building unit is used for building a human body fluid model according to blood circulation data;
the human body structure model building unit is used for building a human body structure model according to geometrical data of a human body;
The mapping unit is used for mapping the human body fluid model into a human body structure model to obtain a human body simulation model;
the human body fluid model building unit includes:
a first geometric model building subunit, configured to build a first geometric model of a human body according to blood circulation data of the human body;
the first grid division subunit is used for carrying out partition processing on the first geometric model, determining the type of the grid unit and carrying out grid division on the calculation domain based on the type of the grid unit;
The first model generation subunit is used for establishing a blood non-Newton flow field for the divided calculation domain based on a lattice Boltzmann method, and correcting the blood non-Newton flow field by adding an additional item related to grid shear rate in a particle distribution function in a balanced state to obtain a human body fluid model;
The human body structure model building unit includes:
a second geometric model building unit for building a second geometric model of the pump based on geometric data of the human body;
The second grid dividing unit is used for importing grid division of the part to be analyzed through the first grid dividing module;
the second model generation subunit is used for assigning values to the divided grids based on biomechanics to obtain a human body structure model;
The simulation operation module is connected with the human body simulation model construction module and is used for performing operation simulation on the human body simulation model to obtain a simulation operation result;
the postoperative evaluation module is connected with the simulation operation module and is used for evaluating the feasibility of the operation according to the simulation operation result;
the specific steps of the surgical feasibility assessment are as follows:
Firstly, collecting data of successful surgery and failed surgery, wherein the data comprise medical image data of a patient, a surgery history record and a video record of a surgery process;
Preprocessing the data, wherein the preprocessing comprises image denoising, alignment, segmentation and integration of different types of data into a consistent format;
extracting relevant characteristics related to surgical feasibility from the preprocessed image, and marking success and failure to obtain a training data set;
training the convolutional neural network using the training dataset;
Evaluating and verifying the trained neural network, including using an independent test data set to test the performance of the model, wherein performance indexes include accuracy, recall and precision, and evaluating the robustness of the model through cross-verification;
after verification is passed, the feasibility of the surgery of the patient is evaluated using a feasibility evaluation model.
2. A simulated evaluation system for surgery on elderly people as claimed in claim 1, wherein,
The simulation operation module comprises a scheme planning sub-module and a simulation operation sub-module;
the scheme planning submodule is used for planning an operation position and an operation step;
the simulation operation submodule is used for carrying out simulation operation according to the planned operation position and operation steps by a doctor to obtain a simulation operation result.
3. A simulated evaluation system for surgery on elderly people as claimed in claim 1, wherein,
The postoperative evaluation module comprises a feasibility sub-module and a postoperative recovery sub-module;
The feasibility submodule is used for calculating the success rate of the operation according to the simulation operation result;
the postoperative recovery submodule is used for calculating the postoperative recovery time according to the simulation operation result.
4. A simulated evaluation method for surgery of elderly people, characterized in that it is based on the simulated evaluation system for surgery of elderly people according to claim 1, said simulated evaluation method comprising the steps of:
collecting human body data and constructing a human body simulation model according to the human body data;
Performing operation simulation on the human body simulation model to obtain a simulation operation result;
and performing operation feasibility assessment according to the simulation operation result.
5. A simulated evaluation method for surgery on elderly people as claimed in claim 4, wherein,
The method for constructing the human body simulation model comprises the following steps:
collecting blood circulation data and geometrical data of a human body based on a magnetic resonance imaging technology;
And constructing a human body simulation model based on the blood circulation data and the geometric data of the human body.
6. A simulated evaluation method for surgery on elderly people as claimed in claim 4, wherein,
The method for constructing the human body simulation model comprises the following steps:
Constructing a human body fluid model according to blood circulation data;
constructing a human body structure model according to the geometric data of the human body;
And mapping the human body fluid model into a human body structure model to obtain a human body simulation model.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102117378A (en) * | 2009-12-31 | 2011-07-06 | 苏州瑞派宁科技有限公司 | Hepatic tumor comprehensive surgical planning analogy method and system thereof based on three-dimensional multimode images |
| CN113611422A (en) * | 2021-07-14 | 2021-11-05 | 佛山市逸合生物科技有限公司 | Simulation method, device and equipment based on biological hydrodynamics |
| CN115329598A (en) * | 2022-09-01 | 2022-11-11 | 重庆电子工程职业学院 | Data processing platform based on digital twins |
| CN116092687A (en) * | 2023-03-24 | 2023-05-09 | 山东建筑大学 | Aneurysm growth model construction system based on computational fluid dynamics |
| CN116864115A (en) * | 2023-08-04 | 2023-10-10 | 浙江大学 | Method and system for evaluating curative effect after thoracic aortic endoluminal repair |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN102117378A (en) * | 2009-12-31 | 2011-07-06 | 苏州瑞派宁科技有限公司 | Hepatic tumor comprehensive surgical planning analogy method and system thereof based on three-dimensional multimode images |
| CN113611422A (en) * | 2021-07-14 | 2021-11-05 | 佛山市逸合生物科技有限公司 | Simulation method, device and equipment based on biological hydrodynamics |
| CN115329598A (en) * | 2022-09-01 | 2022-11-11 | 重庆电子工程职业学院 | Data processing platform based on digital twins |
| CN116092687A (en) * | 2023-03-24 | 2023-05-09 | 山东建筑大学 | Aneurysm growth model construction system based on computational fluid dynamics |
| CN116864115A (en) * | 2023-08-04 | 2023-10-10 | 浙江大学 | Method and system for evaluating curative effect after thoracic aortic endoluminal repair |
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