CN115101162A - Thrombolysis efficiency prediction method and system based on biological fluid mechanics - Google Patents
Thrombolysis efficiency prediction method and system based on biological fluid mechanics Download PDFInfo
- Publication number
- CN115101162A CN115101162A CN202210689493.9A CN202210689493A CN115101162A CN 115101162 A CN115101162 A CN 115101162A CN 202210689493 A CN202210689493 A CN 202210689493A CN 115101162 A CN115101162 A CN 115101162A
- Authority
- CN
- China
- Prior art keywords
- thrombolysis
- data
- measured
- prediction
- thrombus
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
- G16H20/10—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0012—Biomedical image inspection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10072—Tomographic images
- G06T2207/10088—Magnetic resonance imaging [MRI]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
- G06T2207/30101—Blood vessel; Artery; Vein; Vascular
Landscapes
- Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Medical Informatics (AREA)
- Primary Health Care (AREA)
- Radiology & Medical Imaging (AREA)
- Medicinal Chemistry (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Chemical & Material Sciences (AREA)
- Public Health (AREA)
- Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
- Epidemiology (AREA)
- Quality & Reliability (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Measuring Pulse, Heart Rate, Blood Pressure Or Blood Flow (AREA)
- Investigating Or Analysing Biological Materials (AREA)
Abstract
The invention provides a thrombolysis efficiency prediction method based on biological fluid mechanics, which comprises the steps of obtaining thrombus data and thrombolysis data; and (3) importing the thrombus data and the thrombolysis data into a prediction model constructed by an N-S equation and a convection diffusion reaction equation for prediction to obtain the thrombolysis rate and the thrombolysis time. The invention also provides a thrombolysis efficiency prediction system based on the biological hydrodynamics. The implementation of the invention can improve the prediction accuracy and save time and labor.
Description
Technical Field
The invention relates to the technical field of computer simulation, in particular to a thrombolysis efficiency prediction method and system based on biological fluid mechanics.
Background
Biomedical simulation engineering techniques are being applied to clinical treatment in various departments step by step. Simulation prediction is different from meta-analysis in the current medical research, and attempts are made to predict the treatment effect from the specific microscopic substance effect level from the aspects of basic biology and basic physics, substance flow, substance transfer and substance action.
In clinical diagnosis and treatment, venous thromboembolism patients with different results only depend on the thinking of conservative treatment, so that the healing process of the patients is greatly delayed, and unnecessary pain and treatment cost are caused. Therefore, it is necessary to predict different thrombolytic efficiencies early, which contributes to the improvement of the evaluation of the therapeutic effect of thromboembolism.
Currently, the thrombolysis efficiency is often predicted by means of correlation comparison of stored patient databases, subjective evaluation and judgment of doctors, reference of thrombus treatment guidelines and the like, but the methods have the following problems: (1) the underlying principle of the prediction method stays in a patient database utilizing macroscopical data and does not relate to the mechanism of thrombus growth ablation under microcosmic; (2) a large number of existing cases are required, and there are correlations between cases, which results in inaccurate prediction and time and labor consumption.
Therefore, in order to solve the above problems, it is necessary to combine the bio-fluid mechanics simulation technology to construct a thrombolytic efficiency prediction method, help intravascular and surgical physicians to make a thrombus treatment plan and predict the treatment effect.
Disclosure of Invention
The technical problem to be solved by the embodiments of the present invention is to provide a method and a system for predicting thrombolysis efficiency based on biological fluid mechanics, which can improve prediction accuracy, save time and labor, and facilitate the formulation of a thrombus treatment scheme and the prediction of treatment effect.
In order to solve the technical problem, an embodiment of the present invention provides a method for predicting thrombolysis efficiency based on bio-hydrodynamics, including the following steps:
acquiring thrombus data and thrombolysis data; wherein the thrombus data comprises blood viscosity, clotting substances, muscle stress, blood flow rate, and vascular structure; the thrombolysis data comprises medicine types, medicine doses, injection modes, injection sites, reaction parameters and reaction time;
and importing the thrombus data and the thrombolysis data into a prediction model constructed by an N-S equation and a convection diffusion reaction equation for prediction to obtain the thrombolysis rate and the thrombolysis time.
Wherein the blood viscosity is measured by a blood viscosity analyzer; the blood coagulation substance is measured by a blood component detector; the muscle stress is measured by a muscle tension tester; the blood flow velocity is measured by a laser Doppler blood flow meter; the vascular structure is measured by a magnetic resonance imager.
Wherein the convection diffusion reaction equation is realized by the following formula;
wherein, C i Is the concentration of the relevant substance, D i Is the diffusion coefficient of the material, u is the flow field, R i Is the reaction variation term of different substances.
Wherein the method further comprises:
and evaluating a corresponding thrombolysis efficiency grade based on the thrombolysis rate and the thrombolysis time.
The embodiment of the invention also provides a thrombolysis efficiency prediction system based on the biological hydrodynamics, which comprises:
a data acquisition unit for acquiring thrombus data and thrombolysis data; wherein the thrombus data comprises blood viscosity, clotting substances, muscle stress, blood flow rate, and vascular structure; the thrombolysis data comprises medicine types, medicine doses, injection modes, injection sites, reaction parameters and reaction time;
and the prediction unit is used for importing the thrombus data and the thrombolysis data into a prediction model constructed by an N-S equation and a convection diffusion reaction equation for prediction to obtain the thrombolysis rate and the thrombolysis time.
Wherein the blood viscosity is measured by a blood viscosity analyzer; the blood coagulation substance is measured by a blood component detector; the muscle stress is measured by a muscle tension tester; the blood flow velocity is measured by a laser Doppler blood flow meter; the vascular structure is measured by a magnetic resonance imager.
Wherein the convection diffusion reaction equation is realized by the following formula;
wherein, C i Is the concentration of the relevant substance, D i Is the diffusion coefficient of the material, u is the flow field, R i Is the reaction variation term of different substances.
Wherein, still include:
and the evaluation unit is used for evaluating a corresponding thrombolysis efficiency grade based on the thrombolysis rate and the thrombolysis time.
The embodiment of the invention has the following beneficial effects:
the invention leads the thrombus data and the thrombolysis data into the prediction model constructed by the N-S equation and the convection diffusion reaction equation for prediction, and rapidly obtains the thrombolysis rate and the thrombolysis time, thereby improving the prediction accuracy, saving time and labor, and being beneficial to formulating a thrombus treatment scheme and predicting the treatment effect.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is within the scope of the present invention for those skilled in the art to obtain other drawings based on the drawings without inventive exercise.
Fig. 1 is a flowchart of a method for predicting thrombolysis efficiency based on biological fluid mechanics according to an embodiment of the present invention;
FIG. 2 is a diagram illustrating the input states of thrombus data and thrombolysis data in a method for predicting thrombolysis efficiency based on bio-fluid mechanics according to an embodiment of the present invention;
fig. 3 is a structural diagram of logic processing of a prediction model in a thrombolysis efficiency prediction method based on biological fluid mechanics according to an embodiment of the present invention;
FIG. 4 is a graph showing the results of a prediction model in a method for predicting thrombolysis efficiency based on biofluid mechanics according to an embodiment of the present invention;
FIG. 5 is a diagram illustrating the specific calculation results of FIG. 4;
fig. 6 is a schematic structural diagram of a system for predicting thrombolysis efficiency based on bio-fluid mechanics according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings.
As shown in fig. 1, in an embodiment of the present invention, a method for predicting thrombolysis efficiency based on bio-fluid mechanics is provided, the method includes the following steps:
step S1, thrombus data and thrombolysis data are obtained; wherein the thrombus data comprises blood viscosity, clotting substances, muscle stress, blood flow rate, and vascular structure; the thrombolysis data comprises the type of the medicine, the dosage of the medicine, the injection mode, the injection site, reaction parameters and reaction time;
and step S2, importing the thrombus data and the thrombolysis data into a prediction model constructed by an N-S equation and a convection diffusion reaction equation for prediction to obtain thrombolysis rate and thrombolysis time.
Specifically, in step S1, as shown in fig. 2, the data is acquired in two blocks, including thrombus data and thrombolysis data.
In the thrombus data, the blood viscosity was measured by a blood viscosity analyzer; the blood coagulation substance is measured by a blood component detector; the muscle stress is measured by a muscle tension tester; the blood flow velocity is measured by a laser Doppler blood flow meter; the vascular structure is measured by a magnetic resonance imager.
In the thrombolysis data, the thrombolysis data is derived from a treatment protocol setting.
In step S2, first, a prediction model is constructed based on the N — S equation and the convection diffusion reaction equation, that is, a specified index can be obtained through research steps of fluid mechanics index calculation, biological reaction setting, and the like. In order to save calculation time, the prediction model calculates the flow field and the force field, and then calculates the substance reaction and mass transfer conditions, and the specific processing logic is shown in fig. 3.
Wherein, the convection diffusion reaction equation is realized by the following formula;
wherein, C i Is the concentration of the relevant substance, D i Is the diffusion coefficient of the material, u is the flow field, R i Is the reaction variation term of different substances.
And secondly, importing the thrombus data and the thrombolysis data into the prediction model for prediction to obtain the thrombolysis rate and the thrombolysis time.
Finally, as shown in fig. 4, the fast time for stable thrombolysis, the maximum degree of thrombolysis, and the like can be directly presented, and even the thrombolysis efficiency grade is evaluated by combining the thrombolysis rate and the thrombolysis time, that is, the corresponding thrombolysis efficiency grade is evaluated based on the thrombolysis rate and the thrombolysis time. Of course, the specific presented index can be presented at every moment, as shown in the model calculation result of fig. 5.
As shown in fig. 6, in an embodiment of the present invention, a system for predicting thrombolysis efficiency based on bio-fluid mechanics is provided, including:
a data acquisition unit 110 for acquiring thrombus data and thrombolysis data; wherein the thrombus data comprises blood viscosity, clotting substances, muscle stress, blood flow rate, and vascular structure; the thrombolysis data comprises medicine types, medicine doses, injection modes, injection sites, reaction parameters and reaction time;
and the prediction unit 120 is configured to introduce the thrombus data and the thrombolysis data into a prediction model constructed by an N-S equation and a convection diffusion reaction equation to perform prediction, so as to obtain a thrombolysis rate and thrombolysis time.
Wherein the blood viscosity is measured by a blood viscosity analyzer; the blood coagulation substance is measured by a blood component detector; the muscle stress is measured by a muscle tension tester; the blood flow velocity is measured by a laser Doppler blood flow meter; the vascular structure is measured by a magnetic resonance imager.
Wherein the convection diffusion reaction equation is realized by the following formula;
wherein, C i Is the concentration of the relevant substance, D i Is the diffusion coefficient of the material, u is the flow field, R i Is the reaction variation term of different substances.
Wherein, still include:
and the evaluation unit is used for evaluating a corresponding thrombolysis efficiency grade based on the thrombolysis rate and the thrombolysis time.
The embodiment of the invention has the following beneficial effects:
the invention leads the thrombus data and the thrombolysis data into the prediction model constructed by the N-S equation and the convection diffusion reaction equation for prediction, and rapidly obtains the thrombolysis rate and the thrombolysis time, thereby improving the prediction accuracy, saving time and labor, and being beneficial to formulating a thrombus treatment scheme and predicting the treatment effect.
It should be noted that, in the above system embodiment, each included unit is only divided according to functional logic, but is not limited to the above division as long as the corresponding function can be implemented; in addition, specific names of the functional units are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present invention.
It will be understood by those skilled in the art that all or part of the steps in the method for implementing the above embodiments may be implemented by using a program to instruct related hardware, and the program may be stored in a computer readable storage medium, such as ROM/RAM, magnetic disk, optical disk, etc.
While the invention has been described in connection with what is presently considered to be the most practical and preferred embodiment, it is to be understood that the invention is not to be limited to the disclosed embodiment, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.
Claims (8)
1. A thrombolytic efficiency prediction method based on biological fluid mechanics is characterized by comprising the following steps:
acquiring thrombus data and thrombolysis data; wherein the thrombus data comprises blood viscosity, clotting substances, muscle stress, blood flow rate, and vascular structure; the thrombolysis data comprises the type of the medicine, the dosage of the medicine, the injection mode, the injection site, reaction parameters and reaction time;
and importing the thrombus data and the thrombolysis data into a prediction model constructed by an N-S equation and a convection diffusion reaction equation for prediction to obtain the thrombolysis rate and the thrombolysis time.
2. A method of predicting thrombolytic efficiency according to claim 1, wherein the blood viscosity is measured by a blood viscosity analyzer; the blood coagulation substance is measured by a blood component detector; the muscle stress is measured by a muscle tension tester; the blood flow velocity is measured by a laser Doppler blood flow meter; the vascular structure is measured by a magnetic resonance imager.
3. The method of claim 1, wherein the convective diffusion reaction equation is implemented by the following equation;
wherein, C i Is the concentration of the relevant substance, D i Is the diffusion coefficient of the material, u is the flow field, R i Is the reaction variation term of different substances.
4. The method of predicting thrombolytic efficiency based on biofluid mechanics according to claim 1, wherein the method further comprises:
and evaluating a corresponding thrombolysis efficiency grade based on the thrombolysis rate and the thrombolysis time.
5. A system for predicting thrombolytic efficiency based on biofluid mechanics, comprising:
a data acquisition unit for acquiring thrombus data and thrombolysis data; wherein the thrombus data comprises blood viscosity, clotting substances, muscle stress, blood flow rate, and vascular structure; the thrombolysis data comprises medicine types, medicine doses, injection modes, injection sites, reaction parameters and reaction time;
and the prediction unit is used for importing the thrombus data and the thrombolysis data into a prediction model constructed by an N-S equation and a convection diffusion reaction equation for prediction to obtain the thrombolysis rate and the thrombolysis time.
6. The bio-hydrodynamic based thrombolysis efficiency prediction system according to claim 5, wherein the blood viscosity is measured by a blood viscosity analyzer; the blood coagulation substance is measured by a blood component detector; the muscle stress is measured by a muscle tension tester; the blood flow velocity is measured by a laser Doppler blood flow meter; the vascular structure is measured by a magnetic resonance imager.
7. The bio-hydrodynamic based thrombolysis efficiency prediction system according to claim 5, wherein said convective diffusion reaction equation is implemented by the following formula;
wherein, C i Is the concentration of the relevant substance, D i Is the diffusion coefficient of the material, u is the flow field, R i Is the reaction variation term of different substances.
8. The bio-hydrodynamic based thrombolysis efficiency prediction system of claim 5, further comprising:
and the evaluation unit is used for evaluating a corresponding thrombolysis efficiency grade based on the thrombolysis rate and the thrombolysis time.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210689493.9A CN115101162A (en) | 2022-06-16 | 2022-06-16 | Thrombolysis efficiency prediction method and system based on biological fluid mechanics |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210689493.9A CN115101162A (en) | 2022-06-16 | 2022-06-16 | Thrombolysis efficiency prediction method and system based on biological fluid mechanics |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115101162A true CN115101162A (en) | 2022-09-23 |
Family
ID=83291768
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210689493.9A Pending CN115101162A (en) | 2022-06-16 | 2022-06-16 | Thrombolysis efficiency prediction method and system based on biological fluid mechanics |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115101162A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115969464B (en) * | 2022-12-26 | 2024-05-10 | 昆明理工大学 | Method and system for predicting thrombolysis effect of piezoelectric impedance based on regression of support vector machine |
-
2022
- 2022-06-16 CN CN202210689493.9A patent/CN115101162A/en active Pending
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115969464B (en) * | 2022-12-26 | 2024-05-10 | 昆明理工大学 | Method and system for predicting thrombolysis effect of piezoelectric impedance based on regression of support vector machine |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| RU2630122C2 (en) | Method for continuous forecasting of disease severity, death and hospitalization duration | |
| Tan et al. | Non-invasive continuous blood pressure measurement based on mean impact value method, BP neural network, and genetic algorithm | |
| CN101743552B (en) | DSS for acute dynamic diseases | |
| AU2018204120A1 (en) | Drug monitoring and regulation systems and methods | |
| CN112786204A (en) | Machine learning diabetes onset risk prediction method and application | |
| CN112669960A (en) | Liver fibrosis prediction model construction method based on machine learning method, prediction system, device and storage medium | |
| CN112786203A (en) | Machine learning diabetic retinopathy morbidity risk prediction method and application | |
| CN115101162A (en) | Thrombolysis efficiency prediction method and system based on biological fluid mechanics | |
| Nayak et al. | The relationship between salivary insulin-like growth factor I and quantitative cervical maturational stages of skeletal maturity | |
| Tikhonova et al. | Trends in the development of digital technologies in medicine | |
| Ezimah et al. | Absolute platelet count in adult patients with musculoskeletal pain: Current perspectives | |
| CN117373584A (en) | Dynamic prediction method and system for acute kidney injury | |
| De Falco et al. | Accurate estimate of blood glucose through interstitial glucose by genetic programming | |
| Dong et al. | Fast algorithm based on parallel computing for sample entropy calculation | |
| CN102024090A (en) | Hepatitis B indicator data processing device, detection equipment and detection system | |
| Hoyer et al. | Interactions between short-term and long-term cardiovascular control mechanisms | |
| CN108542354A (en) | A kind of the determination method, apparatus and gastroenteritic power instrument of stomach and intestine information | |
| Chen et al. | Analyzing complexity and fractality of glucose dynamics in a pregnant woman with type 2 diabetes under treatment | |
| RU2522279C1 (en) | Method for assessing body adaptation | |
| RU2303955C2 (en) | Method of predicting effectiveness of methods of cure of diseases | |
| Kelly et al. | Method for estimating confidence levels for measurements by blood glucose monitoring systems | |
| Phan et al. | Responsiveness of PROMIS instruments for trigger digit after corticosteroid injection or A1 pulley release | |
| Schoenenberger et al. | Variation in the management of deep vein thrombosis: implications for the potential impact of a critical pathway | |
| Walker | Quality assurance for blood glucose monitoring: the balance of feasibility and standards | |
| Wegner | Data, data, data… |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |