Drug deposition monitoring method in respiratory atomization process

Abstract

The invention relates to the technical field of medicine monitoring, in particular to a method for monitoring medicine deposition in a breathing atomization process, which comprises the following steps: synchronously acquiring aerosol particle information and patient breathing parameter information in an atomization breathing process in real time by utilizing an aerosol monitor and a breathing flow rate sensor based on a preset synchronization mechanism; performing feature extraction on the aerosol particle information and the patient respiration parameter information to obtain drug deposition influence features and respiration driving features; performing graded impact simulation on the drug deposition influence characteristics to obtain particle deposition proportion characteristics of different airway areas; performing airflow dynamic simulation on the respiration driving characteristics to obtain respiration mode influence characteristics; and inputting the particle deposition proportion characteristics and the breathing mode influence characteristics as inputs into a pre-constructed drug deposition analysis model to obtain a real-time drug deposition amount. The medicine deposition amount can be accurately monitored.

Description

Technical Field

[0001] This invention relates to the technical field of drug monitoring, and more particularly to a method for monitoring drug deposition during respiratory nebulization. Background Technology

[0002] Nebulized drug delivery, as an important therapeutic method that delivers drugs to the respiratory tract and lungs in the form of an aerosol, is widely used in the treatment of respiratory diseases. Its therapeutic effect is closely related to the amount and distribution of drug at the target site. Accurate monitoring of drug deposition is of great significance for optimizing dosing regimens, improving therapeutic effects, and reducing adverse reactions.

[0003] Existing methods for monitoring drug deposition during nebulization mainly fall into two categories: in vivo and in vitro monitoring. In vivo monitoring methods, such as radionuclide imaging and biological sample analysis, can reflect drug deposition in the body to some extent, but they suffer from problems such as complex operation, high cost, and radioactive risks or invasiveness, making them difficult to apply in real-time and conveniently in clinical practice. In vitro monitoring methods are mostly used for drug development and equipment quality control. They are difficult to integrate with real-time patient respiratory parameters and cannot reflect the actual impact of the patient's breathing pattern on drug deposition during nebulization, resulting in insufficient accuracy in monitoring drug deposition and making it difficult to provide real-time and precise guidance for clinical treatment. Summary of the Invention

[0004] This invention provides a method for monitoring drug deposition during respiratory nebulization that can accurately monitor the amount of drug deposited, effectively solving the problems in the background art.

[0005] To achieve the above objectives, the present invention provides a method for monitoring drug deposition during inhalation nebulization, comprising: Based on a preset synchronization mechanism, aerosol monitoring instruments and respiratory flow sensors are used to acquire aerosol particle information and patient respiratory parameter information in real time during the nebulized breathing process. Feature extraction is performed on the aerosol particle information and the patient's respiratory parameter information to obtain drug deposition effect features and respiratory drive features; The impact characteristics of drug deposition were subjected to graded impaction simulation to obtain the particle deposition ratio characteristics in different airway regions. The respiratory drive characteristics were simulated using airflow dynamics to obtain the respiratory pattern influence characteristics; The particle deposition ratio characteristics and the respiratory pattern influence characteristics are used as inputs to a pre-constructed drug deposition analysis model to obtain the real-time drug deposition amount.

[0006] Furthermore, the drug deposition characteristics include particle size distribution and aerodynamic diameter; The respiratory drive characteristics include inspiratory flow rate, tidal volume, and respiratory rate.

[0007] Furthermore, the formula for calculating the aerodynamic diameter is as follows: ; in, Indicates the aerodynamic diameter, used to eliminate the interference of particle morphology and density on deposition behavior; Indicates the physical particle size; This indicates the actual density of the particles; The reference density is used as a unified comparison benchmark to eliminate the influence of differences in particle density on sedimentation characteristics. Indicates physical particle size Cunningham correction factor at time; This represents the Cunningham correction factor corresponding to the reference aerodynamic diameter; This represents the particle morphology correction factor.

[0008] Furthermore, the airway region includes at least the nasal cavity region, the pharyngeal region, the multi-level bronchial region, and the alveolar region.

[0009] Furthermore, a graded impaction simulation was performed on the drug deposition impact characteristics to obtain the particle deposition ratio characteristics in different airway regions, including: Construct a hierarchical airway model library that includes the nasal cavity region, pharyngeal region, multi-level bronchial region, and alveolar region; The particle size distribution and aerodynamic diameter in the characteristics affecting drug deposition are jointly grouped to form particle groups containing different particle size ranges, aerodynamic diameter ranges and corresponding proportions; For each graded airway model, based on the preset basic airflow field, the particle group is imported into the graded airway model in the form of discrete phase. The motion trajectory of the particles under the coupling of inertial force, air resistance and gravity is simulated. The number of particles impacting and depositing on the walls of each airway region is counted, and the proportion of the number of deposits to the total number of corresponding particle groups is calculated.

[0010] Furthermore, the graded airway models in the graded airway model library are classified according to human airway anatomical data and airway physiological differences, and each graded airway model defines structural parameters for a corresponding region.

[0011] Furthermore, a method for performing airflow dynamics simulation on the respiratory driving characteristics to obtain the respiratory pattern influence characteristics includes: Based on the graded airway model, the inspiratory flow rate, tidal volume and respiratory rate are used as boundary conditions, and a dynamic airflow field model for the entire respiratory cycle is constructed through computational fluid dynamics. Based on the dynamic airflow field model, the effects of inspiratory flow rate, tidal volume and respiratory rate on the airflow state in each airway region are analyzed. Based on the analytical results, a deposition efficiency correction coefficient matrix is ​​generated; For the respiratory drive characteristics acquired in real time, the deposition efficiency correction coefficients of the corresponding combination parameters are matched from the deposition efficiency correction coefficient matrix to form real-time respiratory pattern influence characteristics.

[0012] Furthermore, the row dimension of the deposition efficiency correction coefficient matrix is ​​the airway region in the hierarchical airway model, and the column dimension is the combined parameters of inspiratory flow rate, tidal volume and respiratory rate. The coefficient value of the row and column intersection is used to characterize the degree of influence of different breathing modes on the deposition efficiency of each airway region.

[0013] Furthermore, the synchronization mechanism includes: The respiratory flow sensor captures the initial moment of the patient's inspiration as the respiratory cycle anchor point, and transmits the respiratory cycle anchor point to the aerosol monitor in real time, triggering it to synchronously start sampling for that respiratory cycle. During each respiratory cycle, the sampling time difference between the aerosol monitor and the respiratory flow sensor is calculated in real time. When the sampling time difference exceeds the preset respiratory cycle fluctuation threshold, the sampling trigger time of the aerosol monitor is automatically adjusted to achieve dynamic calibration.

[0014] Furthermore, the aerosol monitor and the respiratory flow sensor are set to the same sampling frequency, which is matched to the nebulization process.

[0015] The technical solution of this invention achieves the following technical effects: By simultaneously collecting data using an aerosol monitor and a respiratory flow sensor, the invasiveness, radiation risks, and high costs of in vivo monitoring are avoided. Simultaneously, it overcomes the limitations of existing in vitro monitoring methods that cannot obtain real-time patient respiratory parameters, enabling a non-invasive, real-time monitoring process. By extracting aerosol particle characteristics and incorporating real-time patient respiratory parameters, it overcomes the isolation of existing methods that focus solely on particles or respiration, making the monitoring results more closely reflect the individual's actual nebulization process. Through graded impaction simulation, the deposition ratio of particles in different airway regions such as the pharynx, bronchi, and alveoli is clarified. Combined with airflow dynamics simulation, the impact of breathing patterns on deposition efficiency is quantified, solving the problem that existing in vitro monitoring cannot reflect the dynamic deposition patterns of drugs in the real human airways, thus achieving precise monitoring of drug deposition. Attached Figure Description

[0016] Figure 1 This is a logic flowchart of the drug deposition monitoring method during the respiratory nebulization process in this invention. Detailed Implementation

[0017] This application will now be described with reference to the accompanying drawings.

[0018] like Figure 1 As shown, the drug deposition monitoring method during the respiratory nebulization process of the present invention specifically includes the following steps: Step S1: Based on the preset synchronization mechanism, the aerosol monitor and respiratory flow sensor are used to obtain aerosol particle information and patient respiratory parameter information in real time during the nebulized breathing process. Step S2: Extract features from the aerosol particle information and the patient's respiratory parameter information to obtain drug deposition impact features and respiratory drive features; Step S3: Perform graded impact simulation on the drug deposition impact characteristics to obtain the particle deposition ratio characteristics in different airway regions; Step S4: Perform airflow dynamics simulation on the breathing drive characteristics to obtain the breathing pattern influence characteristics; the breathing pattern influence characteristics are used to characterize the degree of influence of the breathing pattern on the deposition efficiency. Step S5: Input the particle deposition ratio characteristics and the respiratory pattern influence characteristics into the pre-constructed drug deposition analysis model to obtain the real-time drug deposition amount.

[0019] In this embodiment, data is collected simultaneously using an aerosol monitor and a respiratory flow sensor, avoiding the invasiveness, radiation risks, and high costs of in vivo monitoring. It also overcomes the limitations of existing in vitro monitoring methods that cannot obtain real-time patient respiratory parameters, achieving a non-invasive, real-time monitoring process. By extracting aerosol particle characteristics and incorporating real-time patient respiratory parameters, it overcomes the isolation of existing methods that focus solely on particles or respiration, making the monitoring results more closely reflect the individual's actual nebulization process. Through graded impaction simulation, the deposition ratio of particles in different airway regions such as the pharynx, bronchi, and alveoli is clarified, and the impact of respiratory patterns on deposition efficiency is quantified by combining airflow dynamics simulation. This solves the problem that existing in vitro monitoring cannot reflect the dynamic deposition patterns of drugs in the real human airways, achieving precise monitoring of drug deposition amounts. Because of individual differences in breathing patterns and airway structure among different patients, existing standardized monitoring methods are difficult to adapt. This method, through the technical route of respiratory drive characteristics, airflow dynamics simulation, and respiratory pattern influence characteristics, transforms individual respiratory differences into quantifiable influence parameters. This is then combined with drug deposition influence characteristics, so that the drug deposition analysis model can be compatible with standardized particle deposition rules and adapted to individual respiratory characteristics. This achieves the fusion of standardized rules and individualized parameters, and solves the contradiction of existing methods that lose accuracy when standardized and lose regularity when individualized. Existing in vivo monitoring methods are difficult to popularize due to their complexity and high cost, while in vitro monitoring lacks clinical applicability. This method collects data in real time through non-invasive sensors and achieves automated analysis by combining it with a pre-built model. It retains the individualized targeting of in vivo monitoring and has the convenience of in vitro monitoring. At the same time, through graded impact simulation and airflow dynamics simulation, it achieves a balance between monitoring convenience and analytical depth that is difficult to achieve with existing methods.

[0020] In some embodiments of the present invention, drug deposition in the respiratory tract is the result of the combined effects of aerosol particle characteristics and respiratory parameters, and the correlation between the two changes dynamically with the respiratory phase. Static time synchronization is insufficient to match the critical window of drug deposition. Therefore, it is necessary to construct a synchronization mechanism that dynamically adapts to the respiratory rhythm to collect aerosol particle information and patient respiratory parameter information in real time. The specific implementation is as follows: An aerosol monitor with real-time detection function is selected and installed on the airflow channel near the output end of the nebulizer, ensuring that its detection window faces the direction of aerosol spraying, so as to capture the dynamic change information of particles during nebulization; at the same time, a respiratory flow sensor is connected to the nebulizer mask or mouthpiece worn by the patient, and the sensor sampling port needs to be close to the patient's breathing airflow path to ensure accurate capture of airflow changes during inhalation and exhalation. The aerosol monitor needs to be set with a sampling frequency that matches the atomization process to record information such as particle size distribution and number concentration; the respiratory flow sensor needs to be set with the same sampling frequency to collect parameters such as inspiratory flow rate, tidal volume, and respiratory rate. Tidal volume is obtained by integrating the flow rate, and respiratory rate is obtained by identifying periodic changes in airflow. The synchronization mechanism employs a synchronization control method combining respiratory cycle anchoring and dynamic error correction. First, a respiratory flow sensor captures the initial moment of the patient's inspiration as the respiratory cycle anchor point. Since the inspirational phase is a critical period for aerosol particles to enter the respiratory tract, using this as a benchmark improves synchronization accuracy. This respiratory cycle anchor point signal is transmitted in real-time to the aerosol monitor, triggering it to synchronously initiate sampling for that respiratory cycle. Second, within each respiratory cycle, the data acquisition system calculates the sampling time difference between the two devices in real-time. Combined with a preset respiratory cycle fluctuation threshold, when the time difference exceeds the threshold, the sampling trigger time of the aerosol monitor is automatically adjusted, achieving dynamic calibration. Finally, before data storage, the sampling data within each cycle undergoes time axis alignment verification, and minor time deviations are corrected using an interpolation algorithm. The synchronously acquired raw data is transmitted in real-time to the data processing terminal, where it is associated and stored using timestamps for easy retrieval in subsequent steps.

[0021] It should be noted that the sampling frequency of the above-mentioned equipment is set to ensure the density and timeliness of data collection, determine the number of times data is collected per unit time, and ensure that subtle changes in aerosol particle information and respiratory parameter information can be captured. This is to ensure that the raw data can fully reflect the dynamic characteristics of particles and respiration during nebulization. On the other hand, respiratory cycle anchoring is to calibrate the sampling time starting point based on the existing sampling frequency, using the patient's initial inhalation moment as the cycle anchor point. This allows each sampling to correspond to the key stage of the respiratory process, making the aerosol particle information and respiratory parameter information within the same respiratory cycle more accurately matched in the time dimension.

[0022] In this embodiment, by constructing a synchronization mechanism that dynamically adapts to the respiratory rhythm, the aerosol monitor is combined with the respiratory flow sensor. Based on setting a matching sampling frequency to ensure data density and timeliness, the initial moment of inhalation is used as the anchor point and errors are dynamically corrected. This achieves accurate synchronous acquisition of aerosol particle information and respiratory parameter information during key respiratory stages. It not only fully captures the core factors affecting drug deposition but also ensures a high correlation between the two in the time dimension. This provides high-quality raw data for subsequent accurate analysis of drug deposition. Moreover, the operation is non-invasive and suitable for various patient groups, effectively improving the accuracy of drug deposition monitoring during respiratory nebulization.

[0023] In a preferred embodiment of the present invention, the aerosol particle information and respiratory parameter information collected synchronously in step S1 are raw data, which contain a lot of redundant information. Directly using them for analysis will increase the computational complexity and make it difficult to accurately correlate with drug deposition patterns. The drug deposition effect is determined by the particle's own characteristics and respiratory state. Among them, particle size distribution and aerodynamic diameter are key characteristics that affect whether particles can reach the target site, while inspiratory flow rate, tidal volume, and respiratory rate are core respiratory parameters that determine the depth and efficiency of particles entering the respiratory tract with the airflow. Therefore, by extracting the above features, the raw data is transformed into feature parameters with clear physical meaning and directly related to the deposition mechanism.

[0024] Specifically, for aerosol particle information, a dynamic particle size analysis algorithm is used to process the raw data; for particle size distribution, the proportion of particles of different sizes at each sampling time is statistically analyzed to generate a continuous particle size distribution curve, and characteristic parameters of the curve such as peak particle size and particle size distribution width are extracted; for aerodynamic diameter, the physical particle size is converted into aerodynamic diameter by combining particle density and morphology parameters through an aerodynamic equivalent calculation model, such as a conversion formula based on Stokes' law, thereby eliminating the interference of particle morphology and density on deposition behavior; For patient respiratory parameter information, a respiratory waveform analysis algorithm is used to process the raw flow rate data; for inspiratory flow rate, the peak inspiratory flow rate and average inspiratory flow rate in each respiratory cycle are extracted; for tidal volume, the tidal volume value of each respiratory cycle is obtained by integrating the flow rate curve during the inspiratory phase; for respiratory rate, the time interval between adjacent starting points is calculated by identifying the starting point of a continuous respiratory cycle, i.e., the initial moment of inspiration, and then converted into the number of breaths per unit time.

[0025] More specifically, the formula for calculating the aerodynamic diameter is as follows: ; in, Indicates the aerodynamic diameter, used to eliminate the interference of particle morphology and density on deposition behavior; The physical particle size, expressed in μm, refers to the geometric diameter of the particle directly measured by an aerosol monitor and is a fundamental physical quantity for calculating aerodynamic diameter. This indicates the actual density of the particles, expressed in g / cm³. It represents the density of the drug aerosol particles themselves and varies depending on the drug composition. For example, there is a difference in density between glucocorticoid particles and antibiotic particles. The reference density is expressed in g / cm³. The density of a spherical standard particle is taken as 1 g / cm³ as a unified comparison benchmark to eliminate the influence of different particle density differences on sedimentation characteristics. Indicates physical particle size The Cunningham correction factor is dimensionless and is used to correct for the slip effect caused by the non-negligible mean free path of air molecules when small particles move in the air. Its value increases as the particle size decreases. This represents the Cunningham correction factor corresponding to the reference aerodynamic diameter. It is dimensionless and is approximately 1.16 when the reference particle size is 1 μm, ensuring that the correction benchmark is consistent for particles of different sizes. This represents the particle morphology correction factor, which is dimensionless and determined by the sphericity of the particles. The sphericity is calculated to be the ratio of the actual surface area of ​​a particle to the surface area of ​​a sphere of the same volume, where 0 < 0. ≤1, the formula is When the particles are perfect spheres =1, =1; when the particles are non-spherical <1, this factor is less than 1, and corrects for changes in air resistance caused by irregular shape.

[0026] In the above calculations, by introducing the ratio of physical particle size, actual density to reference density, and correlating the physical particle size and reference aerodynamic diameter with the Cunningham correction factor, combined with the morphology correction factor calculated from sphericity, the effects of small particle slippage and air resistance of non-spherical particles are accurately corrected. This unifies the comparison benchmark for different particle densities and morphologies, covers the entire particle size range, and the parameters are measurable and usable. This makes the calculations fit the actual deposition patterns and provides an accurate and universal aerodynamic diameter calculation method for monitoring drug deposition in respiratory nebulization, which is helpful for analyzing particle deposition characteristics.

[0027] In some embodiments of the present invention, the characteristics of drug deposition influence determine the movement and deposition patterns of particles within the airways. For example, the structures and airflow environments of different airway regions, such as the nasal cavity, pharynx, bronchi, and alveoli, vary greatly. Through graded impaction analysis, the deposition of particles in each airway region under different combinations of characteristics can be clearly identified. The graded impaction simulation in step S3 is specifically implemented as follows: Step S31: Referring to human airway anatomical data, construct a hierarchical airway geometric model from the nasal cavity to the alveoli, refining it into the nasal cavity region, pharyngeal region, bronchial region, and alveolar region; define key parameters for each region, such as the tortuous channel size in the nasal cavity region, the diameter variation in the pharyngeal region, the bifurcation angle and diameter reduction pattern in the bronchial region, and the vesicle structure and spacing in the alveolar region, to simulate the physical environment of the real airway; according to airway physiological differences, such as airway size between adults and children, and airway structure between healthy and diseased patients, construct a multi-version hierarchical airway model library to provide adapted models for different application scenarios such as pediatric nebulization therapy and COPD patient treatment; Step S32: Jointly group the particle size distribution and aerodynamic diameter; based on the aerodynamic diameter, divide the particles into large-diameter groups that are easy to deposit in the upper airway and small-diameter groups that are easy to deposit in the lower airway, etc.; combined with the particle size distribution, determine the proportion of particles in different particle size intervals in each group to form particle groups with characteristic quantity correlation; unify the units and formats of particle characteristic parameters, such as aerodynamic diameter in μm, and particle size distribution converted into a sequence of the proportion of particles in each particle size interval to ensure that the characteristic data input into the simulation is standardized and comparable; Step S33: For each graded airway model, use computational fluid dynamics to simulate the basic airflow field; set the airflow velocity, pressure and other parameters of the airflow state during calm breathing as physiological typical values, and generate data such as airflow velocity distribution and turbulence intensity in each airway region as the environmental basis for particle motion. Step S34: The particle groups processed with unified units and formats are imported into the airflow inlet of the graded airway model in discrete phase form; the coupling effect of inertial force, air resistance and gravity on particles in the airway is simulated; air resistance is calculated based on aerodynamic diameter, and inertial force and gravity are calculated by combining particle size and mass; the particle trajectory is tracked by discrete element method, focusing on simulating the impact process between particles and airway wall, and the number of impact-deposited particles is counted; in the simulation, according to the regional division of the graded airway model, the deposition number of different particle groups in each region is counted in real time, and the proportion of the deposition number to the total number of particles in that group is calculated to obtain the deposition ratio of each airway region.

[0028] Furthermore, by changing the graded airway model (e.g., switching to a pediatric airway model or a diseased airway model), adjusting the drug deposition impact characteristics, and altering the peak particle size distribution and aerodynamic diameter range, the simulation process was repeated to conduct multi-scenario iterations, covering diverse treatment needs. Simulation results from different graded airway models and different particle feature groups were collected and integrated into a database relating drug deposition impact characteristics, airway regions, and deposition ratios to characterize the correspondence between features and deposition ratios. Data mining algorithms, such as association rule mining, were used to analyze the potential patterns between particle size distribution, aerodynamic diameter combinations, and deposition ratios in each airway region, identifying key feature combinations, such as a specific aerodynamic diameter range combined with a specific particle size distribution, which can maximize alveolar deposition ratios, providing guidance for clinical applications.

[0029] In this embodiment, the hierarchical airway model library covers airway structures of different populations and health states. Combined with feature grouping simulation, it can accurately present the adaptability of drug deposition impact characteristics to airway regions, solve the problem of deposition pattern deviation caused by ignoring airway differences, and improve the treatment guidance value for special populations such as children and lung disease patients. By combining particle size distribution and aerodynamic diameter grouping simulation, it clearly reveals the synergistic effect of the two on airway deposition, breaks through the limitations of single feature analysis, and helps to optimize drug particle design.

[0030] In some embodiments of the present invention, respiratory drive characteristics directly determine the velocity, pressure, and residence time of airflow within the airway, thereby affecting the deposition efficiency of particles in different regions. For example, high inspiratory flow enhances inertial impaction in the upper airway, large tidal volume prolongs the residence time of particles in the alveolar region, and fluctuations in respiratory rate alter airflow stability. Existing simulations mostly use static airflow parameters, which cannot reflect the real-time impact of dynamic changes in individual breathing patterns on deposition. Therefore, it is necessary to perform airflow dynamics simulations based on the above characteristics to quantify the dynamic impact of breathing patterns on deposition efficiency. The specific implementation is as follows: Step S41: Based on the hierarchical airway geometric model in step S3, construct a full respiratory cycle airflow dynamics model using the inspiratory flow rate curve containing peak inspiratory velocity and rise rate, tidal volume, and respiratory rate as boundary conditions; output the airflow velocity field, pressure field, and turbulence intensity distribution in the airway for each respiratory stage through computational fluid dynamics simulation; the respiratory stages include the initial inspiratory phase, peak inspiratory phase, and initial expiratory phase. Step S42: Analyze the influence of airflow by subparameters. First, extract the correlation between the peak inspiratory velocity and the airflow velocity field, calculate the velocity gradient changes in the upper airway and alveolar regions under different flow rates, and quantify the enhancement / weakening effect of flow rate on particle inertial impaction and diffusion deposition. Then, simulate the filling depth of airflow in the bronchial tree according to the tidal volume. For example, with a large tidal volume, the airflow can reach more than level 10 bronchi, while with a small tidal volume, it only reaches level 5. Statistically calculate the proportion of airway area covered by airflow corresponding to different tidal volumes. Then, calculate the residence time of airflow in the airway for the respiratory rate, i.e., tidal volume / average flow rate. Combine the frequency fluctuations such as those of rapid shallow breathing in children to analyze the changes in airflow turbulence intensity and correct the deposition efficiency deviation caused by frequency abnormalities. Step S43: Based on the analysis results of the airflow influence of the above sub-parameters, generate a deposition efficiency correction coefficient matrix; the row dimension of the matrix is ​​the airway region, including the pharynx, bronchi, alveoli, etc., and the column dimension is the combination of respiratory parameters, including high flow rate + large tidal volume, low flow rate + small tidal volume, etc.; the coefficient value of the row and column intersection is obtained by comparing the particle deposition simulation results under dynamic airflow field with the deposition results under static reference airflow. Positive values ​​indicate that the respiratory mode enhances deposition, and negative values ​​indicate that the deposition is weakened. Step S44: For the respiratory drive features acquired in real time, match the deposition efficiency correction coefficients of the corresponding combination parameters from the deposition efficiency correction coefficient matrix to form real-time respiratory pattern influence features.

[0031] In this embodiment, the dynamic influence of the synergistic effect of inspiratory flow rate, tidal volume, and respiratory rate on the airflow field is accurately captured through full respiratory cycle simulation, making the deposition efficiency correction more consistent with the actual breathing process; the airflow influence mechanism is analyzed by parameters, clarifying the laws that high flow rate enhances upper airway deposition and large tidal volume improves alveolar coverage; simulation is performed based on the same hierarchical airway model to ensure the spatial matching between the respiratory pattern influence characteristics and the particle deposition ratio characteristics, thereby improving the accuracy of the final deposition amount calculation.

[0032] In some embodiments of the present invention, the actual amount of drug deposited in the respiratory tract is the result of the synergistic effect of particle deposition ratio characteristics and respiratory pattern influence characteristics. Relying solely on either characteristic cannot accurately reflect the individual's real-time drug deposition status. Therefore, it is necessary to integrate the two types of characteristics through a drug deposition analysis model to achieve a precise mapping from basic laws to individual realities, thereby obtaining the real-time drug deposition amount. The specific implementation is as follows: The drug deposition analysis model is trained based on historical clinical data and simulated data. The inputs are the particle deposition ratio characteristics obtained in step S3 and the respiratory pattern influence characteristics obtained in step S4. The output is the real-time drug deposition amount in each airway region. A fusion architecture combining mechanism and data is adopted: the mechanism layer is based on fluid dynamics and particle deposition theory to establish a mathematical correlation formula between particle deposition ratio and respiratory correction coefficient; the data layer optimizes the model parameters through machine learning algorithms such as gradient boosting trees, fits the clinically measured deposition amount data, and reduces the deviation between theoretical assumptions and reality. The deposition ratios of each airway region output in step S3 and the corresponding region correction coefficients output in step S4 are used. For example, if the deposition ratios of each airway region are 30% for the pharynx, 50% for the bronchi, and 20% for the alveoli, the corresponding region correction coefficients are 1.2 for the pharynx, 0.9 for the bronchi, and 1.1 for the alveoli. These are then input into the drug deposition analysis model according to the airway region association. The drug deposition analysis model first calculates the basic deposition amount using the mechanism layer formula, which is the deposition ratio × the total drug release amount. Then, it dynamically corrects the basic deposition amount of each region using the respiratory correction coefficient. For example, for the pharynx: 30% × total release amount × 1.2, the corrected regional deposition amount is obtained. The drug deposition analysis model outputs the real-time deposition amount and total deposition amount of each airway region, and associates the timestamp with the corresponding respiratory cycle to form a three-dimensional result of time, region and deposition amount; by comparing with some clinical indicators collected simultaneously in real time, the model parameters are dynamically fine-tuned to ensure output stability.

[0033] In this embodiment, the basic deposition patterns of particle characteristics are integrated with the dynamic correction effect of respiratory patterns to solve the problem of large deviations between theoretical and actual values ​​caused by single feature analysis, thereby reducing the error in deposition calculation. Based on real-time feature input and dynamic parameter adjustment, the deposition results can be updated with the respiratory cycle to adapt to the differences in respiratory and particle characteristics of different patients and achieve personalized and accurate monitoring.

[0034] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for monitoring drug deposition during inhalation nebulization, characterized in that, include: Based on a preset synchronization mechanism, aerosol monitoring instruments and respiratory flow sensors are used to acquire aerosol particle information and patient respiratory parameter information in real time during the nebulized breathing process. Feature extraction is performed on the aerosol particle information and the patient's respiratory parameter information to obtain drug deposition effect features and respiratory drive features; The impact characteristics of drug deposition were subjected to graded impaction simulation to obtain the particle deposition ratio characteristics in different airway regions. The respiratory drive characteristics were simulated using airflow dynamics to obtain the respiratory pattern influence characteristics; The particle deposition ratio characteristics and the respiratory pattern influence characteristics are used as inputs to a pre-constructed drug deposition analysis model to obtain the real-time drug deposition amount.

2. The method for monitoring drug deposition during respiratory nebulization according to claim 1, characterized in that, The drug deposition characteristics include particle size distribution and aerodynamic diameter; The respiratory drive characteristics include inspiratory flow rate, tidal volume, and respiratory rate.

3. The method for monitoring drug deposition during respiratory nebulization according to claim 2, characterized in that, The formula for calculating the aerodynamic diameter is: ； in, Indicates the aerodynamic diameter, used to eliminate the interference of particle morphology and density on deposition behavior; Indicates the physical particle size; This indicates the actual density of the particles; The reference density is used as a unified comparison benchmark to eliminate the influence of differences in particle density on sedimentation characteristics. Indicates physical particle size Cunningham correction factor at time; This represents the Cunningham correction factor corresponding to the reference aerodynamic diameter; This represents the particle morphology correction factor.

4. The method for monitoring drug deposition during respiratory nebulization according to claim 2, characterized in that, The airway region includes at least the nasal cavity, pharynx, multi-level bronchi, and alveoli.

5. The method for monitoring drug deposition during respiratory nebulization according to claim 4, characterized in that, The impact characteristics of drug deposition were subjected to graded impaction simulations to obtain the particle deposition ratio characteristics in different airway regions, including: Construct a hierarchical airway model library that includes the nasal cavity region, pharyngeal region, multi-level bronchial region, and alveolar region; The particle size distribution and aerodynamic diameter in the characteristics affecting drug deposition are jointly grouped to form particle groups containing different particle size ranges, aerodynamic diameter ranges and corresponding proportions; For each graded airway model, based on the preset basic airflow field, the particle group is imported into the graded airway model in the form of discrete phase. The motion trajectory of the particles under the coupling of inertial force, air resistance and gravity is simulated. The number of particles impacting and depositing on the walls of each airway region is counted, and the proportion of the number of deposits to the total number of corresponding particle groups is calculated.

6. The method for monitoring drug deposition during respiratory nebulization according to claim 5, characterized in that, The graded airway model library references human airway anatomical data and is classified according to airway physiological differences. Each graded airway model defines structural parameters for a corresponding region.

7. The method for monitoring drug deposition during respiratory nebulization according to claim 6, characterized in that, A method for performing airflow dynamics simulation on the respiratory drive characteristics to obtain the respiratory pattern influence characteristics includes: Based on the graded airway model, the inspiratory flow rate, tidal volume and respiratory rate are used as boundary conditions, and a dynamic airflow field model for the entire respiratory cycle is constructed through computational fluid dynamics. Based on the dynamic airflow field model, the effects of inspiratory flow rate, tidal volume and respiratory rate on the airflow state in each airway region are analyzed. Based on the analytical results, a deposition efficiency correction coefficient matrix is ​​generated; For the respiratory drive characteristics acquired in real time, the deposition efficiency correction coefficients of the corresponding combination parameters are matched from the deposition efficiency correction coefficient matrix to form real-time respiratory pattern influence characteristics.

8. The method for monitoring drug deposition during respiratory nebulization according to claim 7, characterized in that, The row dimension of the deposition efficiency correction coefficient matrix represents the airway regions in the hierarchical airway model, and the column dimension represents the combined parameters of inspiratory flow rate, tidal volume, and respiratory rate. The coefficient values ​​at the intersection of the row and column are used to characterize the degree of influence of different breathing patterns on the deposition efficiency of each airway region.

9. The method for monitoring drug deposition during respiratory nebulization according to claim 1, characterized in that, The synchronization mechanism includes: The respiratory flow sensor captures the initial moment of the patient's inspiration as the respiratory cycle anchor point, and transmits the respiratory cycle anchor point to the aerosol monitor in real time, triggering it to synchronously start sampling for that respiratory cycle. During each respiratory cycle, the sampling time difference between the aerosol monitor and the respiratory flow sensor is calculated in real time. When the sampling time difference exceeds the preset respiratory cycle fluctuation threshold, the sampling trigger time of the aerosol monitor is automatically adjusted to achieve dynamic calibration.

10. The method for monitoring drug deposition during respiratory nebulization according to claim 9, characterized in that, The aerosol monitor and the respiratory flow sensor are set to the same sampling frequency, which is matched to the nebulization process.

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