CN120531472B - Temperature self-adaptive adjusting system for high-frequency heating steam ablation equipment - Google Patents
Temperature self-adaptive adjusting system for high-frequency heating steam ablation equipmentInfo
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Abstract
The invention relates to the technical field of medical instrument engineering, in particular to a temperature self-adaptive regulating system for high-frequency heating steam ablation equipment, which comprises a high-frequency generator, a steam generation module, an ablation electrode, an impedance detection unit, an analysis module, a temperature monitoring module, a time monitoring module, a control module and a leakage protection module, wherein the analysis module is used for giving treatment parameters according to patient disease data, the temperature monitoring module is used for collecting temperature data in a steam transmission pipeline in real time, the time monitoring module is used for recording time information in the treatment process of a patient, the control module is used for regulating the power of the steam generator based on the treatment parameters and the temperature data, the control module is used for controlling the working time of the steam generator based on the treatment parameters and the time information, and the leakage protection module is used for monitoring the line leakage condition of the steam generator in real time.
Description
Technical Field
The invention relates to the technical field of medical instrument engineering, in particular to a temperature self-adaptive adjusting system for high-frequency heating steam ablation equipment.
Background
In the medical field, accurate treatment is a key to improving the therapeutic effect and quality of life of patients. Traditional treatment regimens are generally based on physician experience and standardized treatment guidelines, however, such methods do not always provide optimal treatment results due to individual differences among patients. With the development of information technology, electronic medical record systems (EMR) are widely used, and convenience is provided for the collection, storage and analysis of medical data. How to effectively use these data to generate personalized treatment schemes for specific patients has become an important direction in modern medical research. Steam ablation equipment is used as an emerging medical technology, and is increasingly applied to the fields of tumors, liver diseases and the like by the characteristics of minimally invasive and high efficiency. The device ablates the pathological tissues through high-temperature steam, thereby achieving the purpose of treatment. However, the operating parameters of the steam ablation apparatus (e.g., steam temperature, flow rate, treatment time, etc.) have a significant impact on the effectiveness of the treatment, and the treatment parameters required for different patients vary from patient to patient due to differences in their condition. Therefore, how to determine optimal treatment parameters according to the specific condition of a patient becomes a challenge in clinical practice.
Disclosure of Invention
In view of the above, the present invention addresses the shortcomings of the prior art by providing a temperature adaptive adjustment system for a high-frequency heating steam ablation device, which aims to solve at least one of the problems set forth in the background art.
The invention provides a temperature self-adaptive regulating system for high-frequency heating steam ablation equipment, which comprises a high-frequency generator, a steam generation module, an ablation electrode and an impedance detection unit, wherein the ablation electrode acquires an impedance signal of the steam generation module, and the impedance detection unit monitors voltages or currents at two ends of the ablation electrode in real time;
The system also comprises an analysis module, a control module and a control module, wherein the analysis module is used for giving treatment parameters according to the disease condition data of the patient;
the temperature monitoring module is used for collecting temperature data in the steam transmission pipeline in real time;
the time monitoring module is used for recording time information in the treatment process of the patient;
The control module is used for adjusting the power of the steam generator based on the treatment parameters and the temperature data, controlling the working time length of the steam generator based on the treatment parameters and the time information, receiving the impedance signals, and calculating the resonant frequency based on an impedance-frequency characteristic curve;
The leakage protection module is used for monitoring the line leakage condition of the steam generator in real time, and the leakage protection module is electrically connected with the control module, the analysis module, the temperature monitoring module and the time monitoring module.
In some embodiments, the analysis module obtains a treatment information report of a related patient through an electronic medical record system EMR, constructs a historical treatment case library based on the treatment information report of the related patient, searches the historical treatment case library according to the patient's condition data, finds a similar treatment information report, gives a preset treatment parameter according to the similar treatment information report, and determines the treatment parameter based on the relationship between the patient's condition data and the similar treatment information report.
In some embodiments, the treatment information report includes lesion tissue type data, lesion location data, lesion extent data, medical history information data, and clinical symptom data;
The preset treatment parameters comprise a steam temperature parameter, a steam flow parameter and a steam treatment time parameter.
In some embodiments, the temperature monitoring module collects temperature data in the vapor transmission pipeline in real time through a temperature sensor, and the temperature monitoring module transmits the temperature data in the vapor transmission pipeline collected in real time to the control module through a signal transmission line;
Setting a first adjustment coefficient, and when the temperature actually monitored by the temperature monitoring module is greater than the steam temperature in the treatment parameters, reducing the steam temperature parameters in the treatment parameters by the control module according to the first adjustment coefficient;
Setting a second adjustment coefficient, and when the temperature actually monitored by the temperature monitoring module is smaller than the steam temperature in the treatment parameters, increasing the steam treatment time parameter in the treatment parameters according to the second adjustment coefficient by the control module.
In some embodiments, the control module reduces the power of the steam generator when the control module detects that the temperature data transmitted in real time by the temperature monitoring module exceeds the steam temperature in the treatment parameters;
the high-frequency generator switches the frequency in real time, the frequency range is 200kHz-1MHz, the resonance point is detected, and the lowest impedance point is set as an initial value;
The temperature monitoring module is used for controlling the steam generating module based on the heat resistance of the tissue to be treated and through a PID algorithm, maintaining the steam temperature error to +/-2 ℃, setting flow according to the ablation area of the tissue to be treated, and feeding back and adjusting the opening of the electromagnetic valve through the flow sensor;
The impedance detection unit collects voltages or currents at two ends of the ablation electrode at a rate of 1kHz-10kHz, calculates dynamic impedance (Z=U/I), and eliminates motion artifact interference by adopting moving average filtering or FFT;
when the impedance change rate (delta Z/delta t) exceeds a preset value, judging the tissue to be treated to be denatured, starting frequency tracking, and triggering frequency correction by comparing the voltage/current phase difference if the phase difference is more than 5 degrees;
According to the impedance deviation Adjusting frequency, scaling factorDetermining a response speed;
iteratively approximating the target frequency with an impedance minimization as a target
。
In some embodiments, the time monitoring module uses a clock chip as a time reference, counts the time in the treatment process by a timing circuit to obtain the time information, and transmits the time information to the control module by a circuit.
In some embodiments, a third adjustment factor is set, and the control module reduces the power of the steam generator according to the third adjustment factor.
In some embodiments, the leakage protection module is provided with a leakage threshold value, and the leakage protection module monitors the line leakage condition of the steam generator in real time through a leakage sensor;
And when the leakage sensor monitors that the leakage current reaches a second leakage threshold, a second-level early warning is sent out and a power-off measure is taken by the control module.
In some embodiments, searching the historical treatment case library for a similar treatment information report based on the patient's condition data comprises:
the analysis module calculates the similarity between the disease data of the patient and each case in the historical treatment case library through an Euclidean distance algorithm;
The analysis module is further configured to determine whether each case in the historical treatment case library is a similar treatment information report for the patient based on a relationship between a preset similarity and an actual similarity;
When the actual similarity is less than the preset similarity, determining that the case is not a similar treatment information report of the patient;
When the actual similarity is greater than or equal to the preset similarity, determining that the case is a similar treatment information report of the patient.
In some embodiments, the analysis module calculates a distance between the patient's condition data and the case data in the historical treatment case library by a euclidean distance algorithm based on the obtained relationships between the case data in the historical treatment case library and the patient's condition data;
respectively distributing a weight to the lesion tissue type data, the lesion position data, the lesion degree data, the medical history information data and the clinical symptom data by the analysis module, wherein the lesion tissue type data, the lesion position data, the lesion degree data, the medical history information data and the clinical symptom data are feature vectors;
setting the pathological tissue type data as X1, the pathological position data as X2, the pathological degree data as X3, the medical history information data as X4 and the clinical symptom data as X5;
The pathological tissue type data of the patient is X1 new, the pathological position data of the patient is X2 new, the pathological degree data of the patient is X3 new, the medical history information data of the patient is X4 new, and the clinical symptom data of the patient is X5 new;
the pathological change tissue type data of the historical cases in the historical treatment case library is X1 hist, the pathological change position data of the historical cases in the historical treatment case library is X2 hist, the pathological change degree data of the historical cases in the historical treatment case library is X3 hist, the medical history information data of the historical cases in the historical treatment case library is X4 hist, and the clinical symptom data of the historical cases in the historical treatment case library is X5 hist;
the calculation formula of the Euclidean distance is as follows:
Wherein a and b are feature vectors of the patient and the historical case, respectively, Is the weight of the i-th feature,AndThe values of vectors a and b on the ith feature, respectively, and n is the total number of features.
Compared with the prior art, the temperature self-adaptive adjusting system has the beneficial effects that the temperature self-adaptive adjusting system can automatically give treatment parameters according to the disease condition data of a patient, and the temperature, the flow and the treatment time of steam are acquired and adjusted in real time, so that the treatment efficiency and the treatment accuracy are greatly improved. Meanwhile, the treatment information report of the relevant patient is obtained through the EMR system, a historical treatment case library is constructed, reference is provided for the treatment personnel, and the treatment effect is further improved. The system is provided with the earth leakage protection module, can real-time supervision steam generator's circuit electric leakage condition, when detecting that electric leakage current reaches the settlement threshold value, can send the early warning and take the outage measure, has effectively prevented the emergence of electric shock accident. The system calculates the similarity between the disease data of the patient and each case in the historic treatment case library through the Euclidean distance algorithm, and provides accurate treatment parameters for the treatment personnel. Meanwhile, the therapist can also respectively allocate weights to lesion tissue type data, lesion position data, lesion degree data, medical history information data and clinical symptom data according to actual conditions, so that the treatment is more in line with the actual conditions of patients. The system can collect temperature data in the steam transmission pipeline and time information in the treatment process in real time, and timely adjust the power and the working time of the steam generator, so that the safety and the effectiveness of the treatment process are ensured. When the system detects that the temperature or the time exceeds the preset range, the power or the working time length of the steam generator can be automatically adjusted without manual intervention, so that the workload of medical staff is greatly reduced. The control module of the system adjusts the power of the steam generator based on the treatment parameters and the temperature data, and controls the working time of the steam generator based on the treatment parameters and the time information, so that the operation is simple and convenient. Meanwhile, the electric leakage protection module of the system is electrically connected with the control module, the analysis module, the temperature monitoring module and the time monitoring module, so that maintenance and overhaul are facilitated.
The foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Other features and aspects of the present disclosure will become apparent from the following detailed description of exemplary embodiments, which proceeds with reference to the accompanying drawings.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
Fig. 1 is a block diagram of a temperature adaptive adjustment system for a high-frequency heating steam ablation apparatus according to an embodiment of the present invention;
Fig. 2 is a flowchart of a temperature adaptive adjustment system for a high-frequency heating steam ablation apparatus according to an embodiment of the present invention;
FIG. 3 is a block diagram showing steps of a temperature adaptive adjustment system for a high-frequency heating steam ablation apparatus according to an embodiment of the present invention;
Fig. 4 is a block diagram of a temperature adaptive adjustment system for a high-frequency heating steam ablation apparatus according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
In the description of the present application, it should be understood that the terms "center," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like indicate orientations or positional relationships based on the orientation or positional relationships shown in the drawings, merely to facilitate describing the present application and simplify the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and thus should not be construed as limiting the present application.
The terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature. In the description of the present application, unless otherwise indicated, the meaning of "a plurality" is two or more.
In the description of the present application, unless explicitly stated or limited otherwise, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected, mechanically connected, electrically connected, directly connected, indirectly connected via an intermediate medium, or in communication between two elements. The specific meaning of the above terms in the present application will be understood in specific cases by those of ordinary skill in the art.
Referring to fig. 1-4, a temperature adaptive adjustment system for a high-frequency heating steam ablation device according to an embodiment of the application comprises a high-frequency generator, a steam generation module, an ablation electrode and an impedance detection unit, wherein the ablation electrode acquires an impedance signal of the steam generation module, and the impedance detection unit monitors voltages or currents at two ends of the ablation electrode in real time;
The system also comprises an analysis module, a control module and a control module, wherein the analysis module is used for giving treatment parameters according to the disease condition data of the patient;
the temperature monitoring module is used for collecting temperature data in the steam transmission pipeline in real time;
the time monitoring module is used for recording time information in the treatment process of the patient;
The control module is used for adjusting the power of the steam generator based on the treatment parameters and the temperature data, controlling the working time length of the steam generator based on the treatment parameters and the time information, receiving the impedance signals, and calculating the resonant frequency based on an impedance-frequency characteristic curve;
The leakage protection module is used for monitoring the line leakage condition of the steam generator in real time, and the leakage protection module is electrically connected with the control module, the analysis module, the temperature monitoring module and the time monitoring module.
It should be appreciated that the vapor transmission conduit is typically formed from a high temperature, corrosion resistant material, such as stainless steel or a special alloy material, to ensure that deformation or damage does not occur in the high temperature, high pressure environment. The interior of the pipe may be coated with a special coating to reduce heat loss and condensation during the steam transfer. The pipeline is designed into a multi-layer structure, and comprises an inner layer for transmitting steam, an intermediate layer for heat preservation and insulation, and an outer layer for protecting the pipeline from external environment. The primary function of the vapor delivery conduit is to deliver the generated vapor from the vapor generator to the treatment site. The interior of the pipe is smooth to reduce drag and turbulence during steam flow.
The steam generator operates on the principle that heat is released by heating means (e.g. electric heating elements, gas burners, etc.) and transferred to the surrounding water, causing the water temperature to rise gradually and eventually to steam. In the heating process, water is first preheated to a certain temperature and then further heated to a boiling state, generating steam. The steam produced can then be transported through a pipeline to a desired location for use.
Referring to fig. 2, specifically, as a core decision unit of the system, the analysis module is responsible for integrating and processing data from the temperature monitoring module, the time monitoring module, and possibly other sensors (such as flow sensors, pressure sensors, etc.), and based on these data and a preset algorithm or model, it analyzes the condition or treatment requirement of the patient and gives corresponding treatment parameters. The temperature monitoring module monitors the temperature in the steam transmission pipeline in real time, and ensures that the temperature in the treatment process is kept in a safe and effective range. And transmitting the temperature data to an analysis module and a control module in real time so as to carry out real-time adjustment. The time monitoring module accurately records the duration of the treatment process, including the warm-up time, the treatment time, and possibly the pause time. The time data is transmitted to the analysis module and the control module to assist in adjusting the treatment parameters and to ensure timeliness of the treatment process. The control module precisely controls the steam generator according to the treatment parameters given by the analysis module and the real-time data provided by the temperature monitoring module and the time monitoring module. The power, duration of operation and possibly other parameters of the steam generator are adjusted to ensure the accuracy and effectiveness of the treatment process. The leakage protection module monitors the leakage condition of the steam generator and related circuits in real time, and ensures the electrical safety. Upon detection of an electrical leakage, the alarm system is triggered immediately and emergency measures are taken, such as cutting off the power supply, starting an emergency procedure, etc. The temperature monitoring module and the time monitoring module transmit the acquired data to the analysis module and the control module through a high-speed communication interface (such as USB, bluetooth, wi-Fi and the like).
In some embodiments, the analysis module obtains the treatment information report of the relevant patient through the electronic medical record system EMR, constructs a historical treatment case library based on the treatment information report of the relevant patient, searches the historical treatment case library according to the patient's condition data, finds a similar treatment information report, gives a preset treatment parameter according to the similar treatment information report, and determines the treatment parameter based on the relationship between the patient's condition data and the similar treatment information report.
It should be appreciated that the analysis module is deeply integrated with an electronic medical record system (EMR) of a hospital or medical institution, and data intercommunication and sharing are achieved through an API interface. The integration method ensures the real-time performance and accuracy of the data, and the analysis module can immediately acquire the treatment information report of the patient, wherein the treatment information report comprises lesion tissue type data, lesion position data, lesion degree data, medical history information data and clinical symptom data. After the original data is obtained, the analysis module can firstly clean the data to remove noise, repetition or inconsistent data. The data may then be subjected to normalization processes, such as unified units, format conversion, term normalization, etc., to ensure consistency and comparability of the data. The normalized data is stored in a structured database for subsequent analysis and retrieval. The analysis module displays the preset treatment parameters to the therapist in the form of a chart, a table or a text. These preset parameters may include recommended drugs and their dosages, selection of treatment regimens, expected therapeutic effects, and possible risks, etc. The therapist can adjust the preset parameters according to the actual conditions. This may be based on a variety of factors such as the patient's specific response, the judgment of the physician, the equipment conditions of the hospital, etc. During the adjustment process, the therapist can refer to the similar case details provided by the analysis module, and the sources and the bases of the preset parameters. The adjusted treatment parameters better meet the actual conditions and requirements of patients. The adjusted treatment parameters are validated and entered into the system for guiding the actual treatment process. At this point, the therapist may develop detailed treatment plans, including drug prescriptions, treatment plan schedules, follow-up plans, and the like. At the same time, the system records these validated treatment parameters for future tracking and evaluation.
Specifically, by searching the historical treatment case library, the analysis module can find historical cases similar to the current patient condition and give preset treatment parameters based on the cases. This enables the treatment regimen to more closely conform to the actual condition of the patient, improving the accuracy of the treatment. The preset treatment parameters provide an important reference point for the therapist to better understand how the treatment parameters should be adjusted to optimize the treatment effect. The therapist can finely adjust the preset parameters according to the actual conditions, so that more personalized treatment is realized. With preset treatment parameters as a starting point, the therapist can more quickly make a preliminary treatment plan without having to consider all possible treatment options from scratch. This greatly shortens the time for the formulation of the treatment regimen and improves the treatment efficiency. In emergency or complex situations, the therapist may be faced with a significant amount of decision pressure. The preset treatment parameters provide them with a clear decision starting point, helping to alleviate decision burden and decision fatigue. The preset treatment parameters are generated based on the data in the historical treatment case library, and the reliability is high. By relying on objective historical data and preset parameters given by algorithms, the therapist can reduce the impact of personal subjective bias on the treatment regimen. Over time, the historical treatment case library will continually accumulate new cases and treatment experience. This valuable knowledge and experience can be inherited and shared by the analysis module, providing better treatment options for future patients. The analysis module may also facilitate collaboration and knowledge sharing between different departments. By integrating treatment experience and data from different departments, a more comprehensive and comprehensive treatment regimen can be formed.
In some embodiments, the treatment information report includes lesion tissue type data, lesion location data, lesion extent data, medical history information data, and clinical symptom data;
The preset treatment parameters comprise steam temperature parameters, steam flow parameters and steam treatment time parameters.
In some specific embodiments, the temperature monitoring module collects temperature data in the steam transmission pipeline in real time through the temperature sensor, and the temperature monitoring module transmits the temperature data in the steam transmission pipeline collected in real time to the control module through the signal transmission line;
setting a first adjustment coefficient, and when the temperature actually monitored by the temperature monitoring module is greater than the steam temperature in the treatment parameters, reducing the steam temperature parameters in the treatment parameters by the control module according to the first adjustment coefficient;
setting a second adjustment coefficient, and when the temperature actually monitored by the temperature monitoring module is smaller than the steam temperature in the treatment parameters, increasing the steam treatment time parameter in the treatment parameters by the control module according to the second adjustment coefficient.
Specifically, as shown in fig. 3, when the temperature actually monitored by the temperature monitoring module is greater than the steam temperature in the treatment parameters, the control module decreases the steam temperature in the treatment parameters according to the first adjustment coefficient. This means that if the actual monitored temperature is higher than the preset treatment temperature, the system will automatically reduce the power of the steam generator or adjust other relevant parameters to ensure that the temperature during treatment does not exceed the safe range. This adjustment helps to prevent tissue overheating damage, ensuring safety of treatment. When the temperature actually monitored by the temperature monitoring module is smaller than the steam temperature in the treatment parameters, the control module increases the steam treatment time parameter in the treatment parameters according to the second adjustment coefficient. This means that if the actual monitored temperature is below the preset treatment temperature, the system will automatically extend the operation time of the steam generator or adjust other relevant parameters to ensure that the temperature during treatment will achieve the desired treatment effect. This adjustment helps to improve the therapeutic effect and ensures adequate ablation of the diseased tissue.
In some embodiments, the control module reduces the power of the steam generator when the control module detects that the temperature data transmitted in real time by the temperature monitoring module exceeds the steam temperature in the treatment parameters;
the high-frequency generator switches the frequency in real time, the frequency range is 200kHz-1MHz, the resonance point is detected, and the lowest impedance point is set as an initial value;
The temperature monitoring module is used for controlling the steam generating module based on the heat resistance of the tissue to be treated and through a PID algorithm, maintaining the steam temperature error to +/-2 ℃, setting flow according to the ablation area of the tissue to be treated, and feeding back and adjusting the opening of the electromagnetic valve through the flow sensor;
The impedance detection unit collects voltages or currents at two ends of the ablation electrode at a rate of 1kHz-10kHz, calculates dynamic impedance (Z=U/I), and eliminates motion artifact interference by adopting moving average filtering or FFT;
when the impedance change rate (delta Z/delta t) exceeds a preset value, judging the tissue to be treated to be denatured, starting frequency tracking, and triggering frequency correction by comparing the voltage/current phase difference if the phase difference is more than 5 degrees;
According to the impedance deviation Adjusting frequency, scaling factorDetermining a response speed;
Iteratively approximating the target frequency with the impedance minimization as a target:
。
it should be understood that the system is comprised of a high frequency generator, a steam generation module, an ablation electrode, an impedance detection unit, and an automatic frequency tracking control module. The core is that the resonance impedance change (impedance detection unit) is monitored in real time, the high-frequency output frequency (automatic frequency tracking control module) is dynamically adjusted, the optimal energy transmission efficiency is ensured to be kept all the time under different tissue states (such as drying and carbonization), and meanwhile, the high-temperature steam is utilized for assisting ablation, so that the tissue carbonization risk is reduced.
Impedance feedback, namely acquiring an impedance signal of the steam module by the ablation electrode and transmitting the impedance signal to the control module;
the control module calculates the optimal resonance frequency based on the impedance-frequency characteristic curve (preset or self-adaptive algorithm);
Dynamic adjustment, namely, the real-time switching frequency (the range is generally 200kHz-1 MHz) of the high-frequency generator is matched with the current tissue load, so that the output power is ensured to be stable.
Steam synergy, the steam module (heated to 100-150 ℃) synchronously acts on the target tissue, softens the structure and enhances high-frequency energy penetration, and reduces impedance mutation.
An initialization stage, which is to preset initial frequency and steam parameters (temperature and flow rate);
The ablation stage, namely synchronously releasing high-frequency energy and steam, updating impedance detection every millisecond, and triggering frequency fine adjustment;
And (3) stopping the process after reaching a preset impedance threshold or time. The method has the advantages of avoiding the problem of mismatch caused by tissue denaturation in the traditional high-frequency ablation, improving the operation efficiency and safety, and being particularly suitable for tissues with water content changes such as livers, tumors and the like.
Technical principle and flow of presetting initial frequency and steam parameters (1) initial frequency presetting
Depending on tissue type, the system built-in database stores typical impedance-frequency characteristics of different tissues (e.g. liver, muscle, tumor) and automatically matches the initial frequency according to the surgical site (e.g. liver is commonly used at 300kHz, adipose tissue may be selected at 500 kHz).
Self-adaptive calibration, namely, after the electrode is started, a low-power sweep signal (such as 200kHz-800 kHz) is applied when the electrode contacts with tissue, a resonance point (impedance minimum point) is detected, and the frequency is set as an initial value.
(2) Steam parameter presetting
Temperature setting, namely controlling a heating module through a PID algorithm based on tissue heat resistance (such as 100-120 ℃ for tumor ablation and higher temperature for hemostasis), and maintaining a steam temperature error of +/-2 ℃.
And controlling the flow rate, namely setting the flow (for example, 5-20 mL/min) according to the ablation area (preoperative image planning), and feeding back and adjusting the opening of the electromagnetic valve by a flow sensor.
Technical principle and detailed flow of trigger frequency fine tuning (1) real-time impedance detection
Sampling frequency the impedance detection unit collects the voltage/current across the electrodes at a rate of 1kHz-10kHz, calculating the dynamic impedance (z=u/I).
Noise processing, namely removing motion artifact interference by adopting moving average filtering or FFT.
(2) Frequency adjustment triggering condition
And triggering a threshold value, namely judging tissue denaturation when the impedance change rate (delta Z/delta t) exceeds a preset value (such as 5 omega/ms), and starting frequency tracking.
Phase detection, namely triggering frequency correction by comparing the voltage/current phase difference (reflecting resonance offset), if the phase difference is more than 5 degrees.
(3) Fine tuning algorithm
PID closed loop control based on impedance deviationAdjusting frequency, scaling factorDetermining response speed, PID algorithm:
。
Gradient descent method, which aims at minimizing impedance and iteratively approaches optimal frequency
。
The method for presetting the impedance threshold comprises the following steps:
(1) Based on tissue characteristics
Experimental data reference impedance values were determined by ex vivo experiments at the time of complete ablation of different tissues (e.g. liver tissue rising from initial 50 Ω to 200 Ω was considered to be ablated complete).
Clinical experience value database integration expert experience (e.g. tumor ablation threshold set 3 times the initial impedance).
(2) Dynamic threshold calculation
Normalization process threshold value=X k (coefficient k is determined by tissue type, e.g., k=2.5-4.0).
Machine learning prediction, wherein a training model (such as SVM) predicts an ablation endpoint according to a real-time impedance curve, and a threshold value is adaptively adjusted.
(3) Safety redundancy design
Dual decision, i.e. monitoring the absolute value of impedance (such as >250Ω) and ablation time (such as 60 seconds) simultaneously, and if any condition is met, terminating energy output.
In some embodiments, the time monitoring module uses the clock chip as a time reference, counts the time in the treatment process by the timing circuit to obtain time information, and transmits the time information to the control module by the time monitoring module through a circuit.
In some embodiments, a third adjustment factor is set, and the control module reduces the power of the steam generator based on the third adjustment factor.
In some specific embodiments, the leakage protection module is provided with a leakage threshold value, and the leakage protection module monitors the line leakage condition of the steam generator in real time through a leakage sensor;
and when the leakage sensor detects that the leakage current reaches the first leakage threshold value, a first-level early warning is sent out, and when the leakage sensor detects that the leakage current reaches the second leakage threshold value, a second-level early warning is sent out and a power-off measure is taken through the control module.
It should be understood that the lesion tissue type data clearly records the pathological type of the patient's lesion tissue, such as benign tumor, malignant tumor (including cancer type), inflammatory lesions, necrotic tissue, etc. For malignant tumors, it may also include genetic mutation status of cancer cells, receptor expression status (e.g., hormone receptor, HER2, etc.), and tumor marker levels, which are critical for targeted therapy and prognostic evaluation.
Lesion location data accurately describes the specific location of a lesion tissue within a patient, such as organ name (liver, lung, kidney, etc.), tissue type (e.g., left or right lobe of liver, upper or lower lobe of lung), and specific location (e.g., a segment of liver or a segment of lung). The lesion location is further determined in combination with image data provided by medical images (e.g., CT, MRI, ultrasound, etc.), providing accurate positioning information for treatment.
The lesion extent data classifies the lesion extent according to the nature and extent of spread of the lesion tissue. For tumors, this typically includes stage of the tumor (e.g., stage I, stage II, stage III, stage IV), reflecting the size, depth of infiltration, lymph node metastasis and distant metastasis of the tumor. For non-neoplastic lesions, it may also be desirable to describe their severity or activity. The influence degree of pathological tissues on the functions of organs or systems where pathological tissues are located, such as the liver function damage degree, the kidney function decline degree and the like, is important to consider the overall health condition of a patient when making a treatment scheme.
The medical history information data details past medical history of the patient, including names of the previous diseases, treatment conditions, and prognosis. Of particular note is the history of the disease associated with the current lesion, such as whether there is a history or family history of similar lesions. Drugs that have been used by patients in the past are listed, including prescription drugs, over-the-counter drugs, herbal medicines, etc., as well as the purpose of administration, dosage, duration, and drug response. This is of great importance for avoiding drug interactions and allergic reactions. The patient's history of allergies to any substance, particularly drugs and foods, including allergens, allergic symptoms and severity is recorded. In formulating a treatment regimen, it is necessary to avoid the use of drugs or substances that may cause allergic reactions.
Clinical symptom data details the symptoms currently exhibited by a patient, including the nature of the symptoms (e.g., pain, swelling, cough, etc.), the location (e.g., chest, abdomen, back, etc.), the duration (e.g., sustained, intermittent), the frequency of episodes (e.g., occasional, frequent), and the trend of the symptoms (e.g., gradual exacerbation, gradual alleviation). Recording abnormal findings in physical examination, such as bumps, tenderness, rebound pain, muscular tension, etc., which are of great value for locating lesions and assessing severity of the condition.
The steam temperature parameter range of the preset treatment parameters is based on the historical treatment case library and the current patient condition data (including the lesion tissue type, the position, the degree and the overall health condition of the patient), and the analysis module can use an advanced algorithm to give a steam temperature preset range suitable for the current patient. This range is not only to consider maximization of the therapeutic effect, but also to ensure safety and tolerability of the patient. For example, for certain temperature sensitive diseased tissue, the preset temperature may be relatively low to protect surrounding normal tissue, while for cases where a stronger heating effect is required to kill cancer cells, the preset temperature may approach but not exceed a safety limit.
The steam flow parameter range of the preset treatment parameters is preset to be suitable according to the illness state and the treatment requirement of the patient. The size of the steam flow directly affects the effectiveness of the treatment and the comfort of the patient. Excessive flow may cause discomfort to the patient or increase the risk of complications, while too small flow may not achieve the desired therapeutic effect. Therefore, the analysis module can comprehensively consider various factors and provide a steam flow preset range which can ensure the treatment effect and furthest reduce discomfort of patients.
The steam treatment time parameter range of the preset treatment parameters is combined with the experience data in the patient condition and the historical cases to preset the time range required by treatment. The length of treatment depends on a number of factors, such as the size, location, extent of the diseased tissue, and the patient's response to the treatment. By accurately presetting the treatment time range, the treatment personnel can be helped to plan the treatment process better, and the patient is ensured to receive treatment in a safe and effective time range.
The steam temperature parameter range of the treatment parameters is that on the basis of the reference preset range, the treatment personnel adjusts the actual steam temperature parameters according to the actual conditions of the patients. This includes data that takes into account individual differences in the patient (e.g., sensitivity to temperature, skin conditions, etc.), reactions during treatment (e.g., whether discomfort or pain is present), and real-time monitoring (e.g., temperature data provided by a temperature monitoring module). Through continuous adjustment and optimization, the actual steam temperature parameter can be ensured to achieve the optimal treatment effect, and the safety and comfort of a patient can be ensured.
The steam flow parameter range of the treatment parameters is an actual steam flow parameter determined according to the preset range and the actual condition of the patient. During actual treatment, the therapist will fine tune the steam flow based on patient response and real-time monitoring data (e.g., flow data provided by the flow sensor) to ensure treatment effectiveness and patient comfort.
The range of the steam treatment time parameters of the treatment parameters, the steam treatment time parameters actually used in the treatment process, may be adjusted due to the patient's reaction or the progress of the treatment. The therapist can pay close attention to the change of the illness state and the treatment effect of the patient, and the treatment time is adjusted timely according to the actual situation. For example, if the patient experiences discomfort or rapid improvement in the condition during treatment, the treatment time may be appropriately shortened, whereas if the condition is stable and longer treatment is required to consolidate the effect, the treatment time may be prolonged.
The temperature monitoring module adopts a high-precision temperature sensor, and can continuously and accurately measure the temperature change in the steam transmission pipeline. These sensors generally have the characteristics of quick response, high sensitivity and good stability, and can ensure the accuracy and reliability of measurement data. In order to fully and accurately reflect the temperature distribution within the vapor transmission pipeline, temperature sensors are typically arranged and sampled at a plurality of key points of the pipeline. Temperature information at different positions can be obtained, and abundant data support is provided for subsequent data analysis and processing. The analog signals collected by the temperature sensor need to be converted into digital signals through a signal conversion circuit for subsequent processing and analysis. Meanwhile, in order to compensate for signal attenuation and interference in the transmission process, the signal needs to be amplified. The converted digital signals are transmitted to the control module through a special signal transmission line. These transmission lines generally have good insulating properties and anti-interference capabilities, which ensure stability and accuracy of the signals during transmission. And after the control module receives the temperature data, the current temperature value can be immediately displayed on the display screen in real time, and meanwhile, the data is recorded in the built-in storage device. Thus, the therapist can know the temperature change condition in the steam transmission pipeline at any time and inquire and call the historical data according to the requirement.
The time monitoring module adopts a high-precision clock chip as a time reference source. The clock chips have extremely high stability and accuracy and can provide stable and reliable time signals. They typically employ advanced semiconductor technology and sophisticated manufacturing processes to ensure accuracy and consistency of time metering. To ensure accuracy and reliability of the time monitoring module, the clock chip may be calibrated periodically in synchronization with an external time source (e.g., GPS clock, network time server, etc.). Thus, error accumulation caused by clock drift or external interference can be eliminated, and accuracy and consistency of time measurement are ensured. The time signal provided by the clock chip is typically timed and displayed in seconds. However, the time units may also be converted into minutes, hours, or other larger time units for presentation and processing, depending on the needs of a particular application scenario.
The timing circuit is composed of electronic elements such as a counter, a register and the like. The counter is used for counting pulse signals provided by the clock chip, so that a time metering function is realized. The register is used for storing the counting result and the related state information for subsequent processing and analysis. The time monitoring module is typically provided with timing start and stop control functions. The therapist can manually start or stop timing operation according to the actual requirement of treatment, or automatically control the start and stop of timing through a program. Thus, accurate control and management of the treatment time can be achieved. To prevent problems such as counter overflow or data loss due to long time counting, the time monitoring module is usually provided with a time overflow detection and alarm processing mechanism. When the counter reaches a preset maximum value, the module automatically sends out an alarm signal and stops timing operation, and meanwhile relevant information is recorded in a log file for subsequent inquiry and analysis.
The leakage monitoring module adopts a special leakage sensor to monitor the leakage condition of the steam generator in real time. These sensors typically operate on the current transformer principle, and are capable of detecting small leakage current variations. When leakage occurs, the sensor immediately generates corresponding electric signal change. The leakage sensor is typically mounted in a critical location of the steam generator, such as near the heating element, at an electrical connection, or in a region where leakage is likely to occur. Through reasonable mounted position and overall arrangement, can ensure that the sensor can in time, accurately detect the electric leakage condition. The leakage sensor has the characteristics of high sensitivity and quick response. They can detect weak leakage signals and react quickly, ensuring that problems can be found and handled in time at the early stages of leakage. The electrical signal output by the leakage sensor is typically weak and contains noise interference. Thus, amplification and filtering of the signal is required to improve the strength and purity of the signal. The amplifier can amplify weak electric signals to enough amplitude for subsequent processing and analysis, and the filter can remove noise interference components in the signals and improve the signal-to-noise ratio of the signals. The amplified and filtered signal is sent to a comparator for threshold judgment and comparison. The preset threshold is set according to safety standards and clinical experience, and when the signal amplitude exceeds the threshold, it means that the leakage phenomenon occurs. The comparator outputs a corresponding logic signal to indicate whether the leakage occurs. In order to prevent false alarms caused by false positives and interference, the leakage monitoring module is typically provided with a delay confirmation mechanism. After the comparator outputs the leakage signal, the module waits for a period of time (e.g., a few seconds) to see if the signal is continuously present. If the signal is continuously present and exceeds the delay time, the leakage phenomenon is confirmed, otherwise, the signal is regarded as an interference signal and the alarm is ignored.
In some embodiments, searching the historical treatment case library for a report of similar treatment information based on patient condition data includes:
The analysis module calculates the similarity between the disease data of the patient and each case in the historical treatment case library through the Euclidean distance algorithm;
The analysis module is further configured to determine whether each case in the historical treatment case library is a similar treatment information report for the patient based on a relationship between the preset similarity and the actual similarity;
When the actual similarity is smaller than the preset similarity, determining that the case is not a similar treatment information report of the patient;
when the actual similarity is greater than or equal to the preset similarity, then the case is determined to be a similar treatment information report for the patient.
In some embodiments, the analysis module calculates the distance between the patient's condition data and the case data in the historical treatment case library by a Euclidean distance algorithm based on the obtained relationships between the case data in the historical treatment case library and the patient's condition data;
Respectively distributing a weight to lesion tissue type data, lesion position data, lesion degree data, medical history information data and clinical symptom data by an analysis module, wherein the lesion tissue type data, the lesion position data, the lesion degree data, the medical history information data and the clinical symptom data are characteristic vectors;
Setting the data of pathological change tissue type as X1, pathological change position as X2, pathological change degree as X3, medical history information as X4 and clinical symptom as X5;
The disease tissue type data of the patient is X1 new, the disease position data of the patient is X2 new, the disease degree data of the patient is X3 new, the medical history information data of the patient is X4 new, and the clinical symptom data of the patient is X5 new;
The disease tissue type data of the historical cases in the historical treatment case library is X1 hist, the disease position data of the historical cases in the historical treatment case library is X2 hist, the disease degree data of the historical cases in the historical treatment case library is X3 hist, the disease history information data of the historical cases in the historical treatment case library is X4 hist, and the clinical symptom data of the historical cases in the historical treatment case library is X5 hist;
the calculation formula of the Euclidean distance is as follows:
Wherein a and b are feature vectors of the patient and the historical case, respectively, Is the weight of the i-th feature,AndThe values of vectors a and b on the ith feature, respectively, and n is the total number of features.
According to the formula: calculating distance The similarity score of (2) is in the range of 0 to 1, and the similarity score is positively correlated with the similarity.
It should be appreciated that the new patient's condition data is extracted from the electronic medical record system (EMR), including lesion tissue type, lesion location, lesion extent, medical history information, clinical symptoms, etc. Corresponding case data are extracted from the historical treatment case library, which data should contain the same feature dimensions as the new patient. And carrying out standardized processing on the data to ensure the consistent dimension of different characteristics. For example, if the value ranges of certain features vary widely, normalization may be performed to scale the data to the same range (e.g., between 0 and 1). The missing values are processed and if there is missing data, the padding can be performed using mean, median or interpolation. A set of features for computing similarity is determined. For example, lesion tissue type data, lesion location data, lesion extent data, medical history information data, and clinical symptom data may be selected as feature vectors. Each feature is assigned a weight reflecting its importance in the similarity calculation. If certain features are more important than others, higher weights may be given. Each case in the new patient's data and the historical case library is represented as a feature vector, respectively. Each vector contains a numerical representation of a selected feature, namely, the data of the lesion tissue type is set as X1, the data of the lesion position is set as X2, the data of the lesion degree is set as X3, the data of the medical history information is set as X4, and the data of the clinical symptoms is set as X5;
The disease tissue type data of the patient is X1 new, the disease position data of the patient is X2 new, the disease degree data of the patient is X3 new, the medical history information data of the patient is X4 new, and the clinical symptom data of the patient is X5 new;
the disease tissue type data of the historical cases in the historical treatment case library is X1 hist, the disease position data of the historical cases in the historical treatment case library is X2 hist, the disease degree data of the historical cases in the historical treatment case library is X3 hist, the disease history information data of the historical cases in the historical treatment case library is X4 hist, and the clinical symptom data of the historical cases in the historical treatment case library is X5 hist.
For each case in the new patient and the historical case library, substituting the characteristic vector and the corresponding weight, and calculating the Euclidean distance between the characteristic vector and the corresponding weight. The smaller the calculated euclidean distance, the more similar the new patient is to the historical case. Therefore, the historical cases can be ranked according to the distance, and the first cases with the smallest distance are selected as the most similar cases.
The euclidean distance enables comprehensive consideration of the characteristic dimensions of the new patient data and the historical cases. In the medical field, patient condition-related information is manifold, including physiological indicators (e.g., blood pressure, blood sugar, heart rate, etc.), medical history information (e.g., past diseases, family medical history, etc.), symptoms (e.g., pain level, fever, etc.), and examination results (e.g., imaging examination, laboratory examination, etc.). The Euclidean distance integrates the information of different dimensions into a mathematical formula to calculate an overall similarity value, rather than considering a certain factor alone, so that the difference between a new patient and a historical case can be reflected more comprehensively and accurately. For example, when comparing the conditions of two patients, if they are considered similar from a single blood pressure value only, the Euclidean distance can give a more objective and comprehensive similarity assessment considering the differences in other dimensions such as blood glucose, medical history, etc. This helps the doctor to more accurately find out the history cases that are truly similar to the new patient, providing a more reliable basis for developing personalized treatment regimens. The euclidean distance is calculated as a distance value between the new patient data and the historical case data in the multidimensional space, and the similarity between the new patient data and the historical case data can be accurately quantified. The smaller the distance value, the more similar the new patient is to the historical case, and the larger the distance value, the lower the similarity is. This precise quantification provides an intuitive, comparable standard for doctors to order and screen historical cases according to the magnitude of similarity, quickly locating the cases most likely to fit new patient treatment regimens. For example, in the face of a plurality of historical cases that may be related to the condition of a new patient, a doctor may rank the cases from high to low in similarity to the new patient by calculating euclidean distance, and preferably refers to cases with high similarity to make a treatment strategy, thereby improving the efficiency and accuracy of treatment decisions.
In medical data, there is often a complex nonlinear relationship between the various factors. The euclidean distance can not only take into account the differences of individual features, but can also capture interactions and combined effects between different features. By calculating Euclidean distance between new patient data and historical case data, complex relations hidden behind the data can be revealed, and doctors can be helped to find new disease modes and treatment rules. For example, in the treatment of certain diseases, it may be found that interactions of factors of different dimensions such as age, sex, medical history, etc. can have a significant impact on the therapeutic efficacy. The euclidean distance can help doctors identify potential associations between these factors, thereby more comprehensively considering the comprehensive actions of various factors when making treatment schemes and improving the pertinence and effectiveness of treatment. With the continuous accumulation and updating of medical data, euclidean distance calculations can help doctors find some similarity patterns that were not previously noticed. Through comparative analysis of a large number of historical cases and new patient data, it is possible to find patient populations with similar disease manifestations but different combinations of features, or to find rules of influence of certain specific factors on the disease in different disease states. For example, in the study of a disease, a group of patients with similar symptoms but different etiologies were found by euclidean distance calculation, and they had a common characteristic pattern in terms of gene expression, metabolic level, and the like. The discovery can provide new ideas and methods for diagnosing and treating diseases and promote the deep development of medical research. The condition of a patient is a dynamic process, and as treatment progresses, the physiological index and symptoms of the patient may change. The Euclidean distance can calculate the similarity between the data of the new patient and the data of the historical cases in real time, and timely reflect the change condition of the illness state. The doctor can dynamically adjust the treatment scheme according to the similarity calculation results of different time points so as to better adapt to the individual difference and the disease development of the patient. For example, during chemotherapy, the physical condition of the patient and the response of tumor cells may change continuously. Through regularly calculating Euclidean distance, doctor can know the difference between the current state of illness of patient and the history case of early stage or earlier stage of treatment, in time adjusts kind, dosage and the frequency of use of chemotherapeutic medicine, improves treatment effect and reduces adverse reaction.
Each patient's response to treatment is unique and even if the conditions are similar, different patients may respond differently to the same treatment regimen due to individual differences. The euclidean distance calculation can help doctors monitor individual response differences of patients in the treatment process, and personalized treatment adjustment suggestions are provided for the patients according to similarity changes with the historical cases. For example, some patients may recover faster after surgical treatment, while some patients may experience complications or recover slower. By comparing these post-operative data of the patient with the euclidean distance of the historical cases, the doctor can find out factors that cause the discrepancy, such as physical quality of the patient, details of the operation in the operation, etc., thereby making a more accurate rehabilitation plan and subsequent treatment regimen for each patient.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
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