A three-energy electrosurgical complication risk prediction method and system

Abstract

The present application relates to the technical field of medical devices, and proposes a three-energy electrosurgical procedure complication risk prediction method and system, the method comprising: fusing and normalizing operation parameters and clinical diagnosis and treatment data to obtain standardized multi-source parameters; performing time-frequency domain feature extraction on three groups of energy operation parameters to obtain energy feature vectors; performing correlation pairing and interaction influence analysis on the energy feature vectors to obtain interaction feature vectors; based on the interaction feature vectors, performing operation stability evaluation on the three-energy output parameters to obtain single-energy risk bases; performing three-energy cumulative action decomposition on the standardized multi-source parameters to obtain the tissue energy deposition amount of the patient, and coupling and comparing the tissue energy deposition amount with the tissue tolerance parameter to obtain a comprehensive energy load; according to the comprehensive energy load, the single-energy risk base and the interaction feature vector, determining the complication risk type and the risk level; the present application can improve the efficiency of a three-energy electrosurgical procedure complication risk prediction.

Description

Technical Field

[0001] This invention relates to the field of medical device technology, and in particular to a method and system for predicting the risk of complications in three-energy electrosurgical procedures. Background Technology

[0002] Current risk prediction methods for complications of electrosurgery can only collect and analyze single energy output data, and cannot simultaneously integrate the operating parameters of three types of energy: high-frequency electricity, radiofrequency ablation, and low-temperature plasma ablation. They also do not include clinical diagnostic data such as patients' preoperative physiological indicators and real-time vital signs during the operation. Multi-source data suffers from problems such as temporal misalignment and inconsistent dimensions, making it difficult to fully extract the dynamic changes of energy in the time and frequency domains. They lack the ability to quantitatively analyze the interaction modulation and synchronous fluctuation correlation between multiple energy groups, and cannot distinguish between the risk of single energy operation fluctuations and the compound risks brought about by the superposition and coupling of multiple energies. The tissue energy deposition calculation does not consider the numerical deviation caused by mutual energy perturbation, and the final risk assessment results have obvious bias and error.

[0003] Current prediction methods lack standardized, comprehensive energy load quantification models, failing to simultaneously output the risk type and precise risk level corresponding to complications. The entire process relies on manual segmentation and analysis of various parameters, resulting in cumbersome data processing and low overall efficiency in risk assessment and judgment. This makes it difficult to provide rapid and reliable quantitative evidence for real-time intraoperative risk warnings. Therefore, improving the completeness, accuracy, and efficiency of risk prediction for complications in three-energy electrosurgical procedures has become an urgent problem to be solved. Summary of the Invention

[0004] This invention provides a method and system for predicting the risk of complications in three-energy electrosurgery, in order to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides a method for predicting the risk of complications in three-energy electrosurgical procedures, comprising:

[0006] D1. The operating parameters of the three-energy electrosurgical procedure are fused and normalized with the patient's clinical diagnosis and treatment data to obtain the standardized multi-source parameters of the three-energy electrosurgical procedure.

[0007] D2. Extract time-frequency domain features from the three sets of energy operation parameters in the standardized multi-source parameters to obtain the energy feature vector of the three-energy electrosurgery.

[0008] D3. The energy feature vectors are associated and paired to obtain three sets of energy parameter pairs for the three-energy electrosurgical procedure, and the interaction influence analysis of the three sets of energy parameter pairs is performed to obtain the interaction feature vector of the three-energy electrosurgical procedure.

[0009] D4. Based on the interaction feature vector, evaluate the operational stability of the three-energy output parameters in the standardized multi-source parameters to obtain the single-energy risk basis of the three-energy electrosurgery.

[0010] D5. Based on the interaction feature vector and the single-energy risk basis, the standardized multi-source parameters are decomposed into three-energy cumulative effects to obtain the tissue energy deposition amount of the patient. The tissue energy deposition amount is then coupled and compared with the tissue tolerance parameters in the clinical diagnosis and treatment data to obtain the comprehensive energy load of the three-energy electrosurgery.

[0011] D6. Based on the comprehensive energy load, the single-energy risk base and the interaction feature vector, determine the complication risk type and risk level of the three-energy electrosurgery.

[0012] In a preferred embodiment, the process of fusing and normalizing the operating parameters of the three-energy electrosurgery with the patient's clinical data to obtain standardized multi-source parameters for the three-energy electrosurgery includes:

[0013] The operating parameters of the three-energy electrosurgical procedure are obtained, and the operating parameters are time-aligned to obtain the time-series operating parameters of the three-energy electrosurgical procedure. The time-series operating parameters include high-frequency electrical energy output parameters, radiofrequency ablation energy output parameters, and low-temperature plasma ablation energy output parameters.

[0014] The patient's preoperative physiological indicators and intraoperative vital sign monitoring data were extracted in a structured manner to obtain the patient's structured clinical indicator sequence;

[0015] The time-series operating parameters and the structured clinical indicator sequence are time-base synchronized to obtain the synchronous multi-source parameter data of the three-energy electrosurgery.

[0016] The synchronous multi-source parameter data is subjected to dimension normalization mapping to obtain the standardized multi-source parameters of the three-energy electrosurgery.

[0017] In a preferred embodiment, the step of extracting time-frequency domain features from the three sets of energy operating parameters in the standardized multi-source parameters to obtain the energy feature vector of the three-energy electrosurgery includes:

[0018] The high-frequency electrical energy parameter, radiofrequency ablation energy parameter, and low-temperature plasma ablation energy parameter in the standardized multi-source parameters are separated to obtain the independent energy parameters of the three-energy electrosurgery.

[0019] The independent energy parameters are analyzed to obtain the time-domain and frequency-domain characteristic parameters of the three-energy electrosurgery.

[0020] The time-domain feature parameters and the frequency-domain feature parameters belonging to the same energy type are paired, and the paired time-domain feature parameters and the frequency-domain feature parameters are spliced ​​and fused to obtain the energy feature vector of the three-energy electrosurgery.

[0021] In a preferred embodiment, the energy feature vector is associated and paired to obtain three sets of energy parameter pairs for the three-energy electrosurgical procedure, and the interaction influence analysis of the three sets of energy parameter pairs is performed to obtain the interaction feature vector of the three-energy electrosurgical procedure, including:

[0022] The feature components in the energy feature vector are combined and paired to obtain three sets of energy parameter pairs for the energy electrosurgery. The three sets of energy parameter pairs include high-frequency electrical energy-radiofrequency ablation energy parameter pairs, high-frequency electrical energy-low-temperature plasma ablation energy parameter pairs, and radiofrequency ablation energy-low-temperature plasma ablation energy parameter pairs.

[0023] Synchronous fluctuation analysis and amplitude modulation relationship analysis were performed on the energy output parameters of the three sets of energy parameter pairs respectively to obtain the fluctuation correlation characteristics and modulation correlation characteristics of the three sets of energy parameter pairs;

[0024] The fluctuation correlation feature and the modulation correlation feature are cross-fused to obtain the interaction influence coefficients of the three sets of energy parameter pairs;

[0025] The interaction influence coefficients are combined in a fixed-dimensional order to obtain the interaction feature vector of the three-energy electrosurgery.

[0026] In a preferred embodiment, the step of performing synchronous fluctuation analysis and amplitude modulation relationship analysis on the energy output parameters of the three sets of energy parameter pairs respectively to obtain the fluctuation correlation characteristics and modulation correlation characteristics of the three sets of energy parameter pairs includes:

[0027] The energy output parameters in the three sets of energy parameter pairs are divided into time-series sampling windows to obtain the synchronous analysis data segments of the three sets of energy parameter pairs;

[0028] Based on the synchronous analysis data segment, the parameters of the energy parameter pairs in the three sets of energy parameter pairs are registered in the time domain to obtain the synchronous fluctuation sequence of the energy parameter pairs;

[0029] The amplitude of the synchronous fluctuation sequence is compared to obtain the amplitude difference of the energy parameter pair, and the trend consistency of the amplitude difference is measured to obtain the fluctuation correlation characteristics of the energy parameter pair.

[0030] Based on the synchronous analysis data segment, the amplitude envelope change rate analysis is performed on the parameters of the energy parameter pair to obtain the amplitude modulation parameter sequence of the energy parameter pair;

[0031] The amplitude modulation ratio sequence is extracted from the amplitude modulation parameter sequence, and the directional consistency analysis is performed on the extracted modulation ratio sequence to obtain the modulation correlation characteristics of the energy parameter pair.

[0032] In a preferred embodiment, the step of evaluating the operational stability of the three-energy output parameters in the standardized multi-source parameters based on the interaction feature vector to obtain the single-energy risk basis of the three-energy electrosurgery includes:

[0033] Based on the interaction feature vector, the interaction influence coefficient of the high-frequency electrical energy-radio frequency ablation energy parameter pair and the interaction influence coefficient of the high-frequency electrical energy-low-temperature plasma ablation energy parameter pair are combined into the high-frequency electrical energy disturbance factor of the high-frequency electrical energy parameter.

[0034] Based on the interaction feature vector, the interaction influence coefficient of the high-frequency electrical energy-radio frequency ablation energy parameter pair and the interaction influence coefficient of the radio frequency ablation energy-low-temperature plasma ablation energy parameter pair are combined into the radio frequency ablation energy disturbance factor of the radio frequency ablation energy parameter.

[0035] Based on the interaction feature vector, the interaction influence coefficients of the high-frequency electrical energy-low-temperature plasma ablation energy parameter pair and the radio frequency ablation energy-low-temperature plasma ablation energy parameter pair are combined into the plasma ablation energy disturbance factor of the plasma ablation energy parameter.

[0036] Based on the high-frequency electrical energy perturbation factor, the radiofrequency ablation energy perturbation factor, and the plasma ablation energy perturbation factor, the independent energy parameters of the three-energy electrosurgical procedure are perturbed and stripped to obtain the deperturbed parameter sequence of the independent energy parameters;

[0037] The amplitude fluctuation statistics and frequency fluctuation statistics of the undisturbed parameter sequence are performed to obtain the amplitude fluctuation statistics and frequency fluctuation statistics of the undisturbed parameter sequence.

[0038] The amplitude fluctuation statistics and the frequency fluctuation statistics are mapped to risk base values ​​corresponding to energy types, and the risk base values ​​are combined in order of energy type to obtain the single-energy risk base of the three-energy electrosurgery.

[0039] In a preferred embodiment, the standardized multi-source parameters are decomposed into three-energy cumulative effects based on the interaction feature vector and the single-energy risk basis to obtain the patient's tissue energy deposition amount. This tissue energy deposition amount is then coupled and compared with tissue tolerance parameters from the clinical data to obtain the comprehensive energy load of the three-energy electrosurgery, including:

[0040] The high-frequency electrical energy output parameter sequence, radiofrequency ablation energy output parameter sequence, and low-temperature plasma ablation energy output parameter sequence in the standardized multi-source parameters are subjected to time-period integration processing to obtain the initial cumulative effect of the high-frequency electrical energy output parameter sequence, radiofrequency ablation energy output parameter sequence, and low-temperature plasma ablation energy output parameter sequence.

[0041] Based on the interaction feature vector, the initial cumulative effect is corrected by interaction, and the corrected cumulative effect of the high-frequency electrical energy output parameter sequence, the radiofrequency ablation energy output parameter sequence, and the low-temperature plasma ablation energy output parameter sequence is obtained.

[0042] Based on the single-energy risk basis, the modified cumulative effect is weighted by stability to obtain the weighted cumulative effect of the high-frequency electrical energy output parameter sequence, the radiofrequency ablation energy output parameter sequence, and the low-temperature plasma ablation energy output parameter sequence;

[0043] The weighted cumulative effects are summed to obtain the tissue energy deposition amount of the patient.

[0044] The overall energy load of the three-energy electrosurgery is calculated based on the amount of energy deposited in the tissue and the tissue tolerance parameters in the clinical data.

[0045] In a preferred embodiment, the formula for calculating the overall energy load is as follows:

[0046] ;

[0047] in, For the aforementioned comprehensive energy load, The amount of energy deposited in the tissue. The tissue tolerance parameter, For the single-energy risk basis, This is the feature vector of the interaction.

[0048] In a preferred embodiment, determining the complication risk type and risk level of the three-energy electrosurgery based on the comprehensive energy load, the single-energy risk base, and the interaction feature vector includes:

[0049] The overall energy load level of the three-energy electrosurgical procedure is obtained by segmenting the comprehensive energy load into segments and mapping the load level.

[0050] The risk contribution of the single-energy risk base was analyzed to obtain the risk contribution ratio of high-frequency electrical energy, radiofrequency ablation energy and low-temperature plasma ablation energy in the three-energy electrosurgery.

[0051] The interaction feature vector is analyzed for coupling strength to obtain the interaction coupling strength index of the three-energy electrosurgery.

[0052] Based on the overall energy load level, the risk contribution ratio of high-frequency electrical energy, the risk contribution ratio of radiofrequency ablation energy, the risk contribution ratio of low-temperature plasma ablation energy, and the interaction coupling strength index, the risk type of the three-energy electrosurgical procedure is determined, and the complication risk type of the three-energy electrosurgical procedure is obtained.

[0053] Based on the complication risk type, a comprehensive risk level mapping is performed on the overall energy load level and the interaction coupling strength index to obtain the complication risk level of the three-energy electrosurgery.

[0054] To address the aforementioned problems, the present invention also provides a three-energy electrosurgical complication risk prediction system, the system comprising:

[0055] The multi-source data fusion module is used to fuse and normalize the operating parameters of the three-energy electrosurgical procedure with the patient's clinical diagnosis and treatment data to obtain the standardized multi-source parameters of the three-energy electrosurgical procedure.

[0056] The energy time-frequency feature extraction module is used to extract time-frequency domain features from the three sets of energy operation parameters in the standardized multi-source parameters to obtain the energy feature vector of the three-energy electrosurgery.

[0057] The energy interaction feature analysis module is used to associate and pair the energy feature vectors to obtain three sets of energy parameter pairs for the three-energy electrosurgical procedure, and to perform interaction influence analysis on the three sets of energy parameter pairs to obtain the interaction feature vector of the three-energy electrosurgical procedure.

[0058] The single-energy risk baseline assessment module is used to evaluate the operational stability of the three-energy output parameters in the standardized multi-source parameters based on the interaction feature vector, so as to obtain the single-energy risk baseline of the three-energy electrosurgery.

[0059] The energy load coupling calculation module is used to perform three-energy cumulative effect decomposition on the standardized multi-source parameters based on the interaction feature vector and the single-energy risk basis to obtain the tissue energy deposition amount of the patient, and to couple and compare the tissue energy deposition amount with the tissue tolerance parameter in the clinical diagnosis and treatment data to obtain the comprehensive energy load of the three-energy electrosurgery.

[0060] The concurrency risk assessment module is used to determine the complication risk type and risk level of the three-energy electrosurgery based on the comprehensive energy load, the single-energy risk base and the interaction feature vector.

[0061] Compared with the prior art, the present invention has the following beneficial effects:

[0062] 1. This invention integrates and normalizes multi-source surgical parameters and clinical diagnostic data to unify the dimensions and time-series benchmarks of different types of data. It relies on the joint extraction of time and frequency domains to fully capture the dynamic characteristics of three types of energy output. Then, by pairing and analyzing the interactive correlation features of each energy, it accurately removes the disturbances caused by the superposition of multiple energies and quantifies the independent risk basis of each single energy. This invention can fully restore the dual risk factors of the individual effects and mutual coupling of energy during surgery, and greatly improve the comprehensiveness and accuracy of energy risk feature mining in electrosurgery.

[0063] 2. This invention completes the weighted calculation of cumulative energy based on interactive features and a single-energy risk basis, and constructs a standardized comprehensive energy load calculation model by combining tissue tolerance parameters. Relying on load grading, energy risk contribution ratio and coupling strength index, it simultaneously completes the determination of complication risk type and quantitative output of level, realizing automated quantitative calculation of surgical complication risk throughout the process, effectively shortening the risk assessment time, and providing quantitative and intuitive risk data support for real-time intraoperative intervention and preoperative surgical plan optimization. Attached Figure Description

[0064] Figure 1 A flowchart illustrating a method for predicting the risk of complications in three-energy electrosurgery, as provided in an embodiment of the present invention;

[0065] Figure 2 A functional block diagram of a three-energy electrosurgical complication risk prediction system provided in an embodiment of the present invention;

[0066] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0067] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0068] This application provides a method for predicting the risk of complications in three-energy electrosurgical procedures. The executing entity of this method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, the method for predicting the risk of complications in three-energy electrosurgical procedures can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster. The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0069] Reference Figure 1 The diagram shown is a flowchart illustrating a method for predicting the risk of complications in three-energy electrosurgical procedures according to an embodiment of the present invention. In this embodiment, the method for predicting the risk of complications in three-energy electrosurgical procedures includes:

[0070] D1. The operating parameters of the three-energy electrosurgical procedure are fused and normalized with the patient's clinical diagnosis and treatment data to obtain the standardized multi-source parameters of the three-energy electrosurgical procedure.

[0071] In this embodiment of the invention, the fusion and normalization of the operating parameters of the three-energy electrosurgical procedure with the patient's clinical diagnosis and treatment data to obtain standardized multi-source parameters for the three-energy electrosurgical procedure includes:

[0072] The operating parameters of the three-energy electrosurgical procedure are obtained, and the operating parameters are time-aligned to obtain the time-series operating parameters of the three-energy electrosurgical procedure. The time-series operating parameters include high-frequency electrical energy output parameters, radiofrequency ablation energy output parameters, and low-temperature plasma ablation energy output parameters.

[0073] The patient's preoperative physiological indicators and intraoperative vital sign monitoring data were extracted in a structured manner to obtain the patient's structured clinical indicator sequence;

[0074] The time-series operating parameters and the structured clinical indicator sequence are time-base synchronized to obtain the synchronous multi-source parameter data of the three-energy electrosurgery.

[0075] The synchronous multi-source parameter data is subjected to dimension normalization mapping to obtain the standardized multi-source parameters of the three-energy electrosurgery.

[0076] The system continuously collects the operational parameters of the three-energy electrosurgery throughout the entire surgical procedure. The recording time nodes corresponding to all operational parameters are uniformly arranged according to the order of the surgical operation. Operational parameters with time node deviations are adjusted to a unified time arrangement logic. After all adjustments are completed, the time sequence operational parameters of the three-energy electrosurgery are directly generated. These time sequence operational parameters fully include the high-frequency electrical energy output parameters, radiofrequency ablation energy output parameters, and low-temperature plasma ablation energy output parameters generated throughout the entire surgical procedure. The three types of energy-related output content are completely arranged within the time sequence operational parameters according to the order of the surgical procedure.

[0077] The system retrieves all preoperative physiological records and intraoperative vital sign monitoring records continuously collected by the equipment during the surgery. It then breaks down the records into line by line, assigning each piece of diagnostic and treatment information to a fixed storage location. The system then standardizes the arrangement of all the split diagnostic and treatment information, generating a structured clinical indicator sequence for the patient. All clinically relevant information within this sequence has a standardized and uniform structure, eliminating any disorganized and fragmented diagnostic and treatment data.

[0078] The system reads all time-series nodes within the generated three-energy electrosurgical procedure's time-series parameters and simultaneously reads all recorded time nodes within the generated patient's structured clinical indicator sequence. It matches each time node in both data sets one-to-one, adjusting any records that cannot be matched in time. This ensures that each segment of surgical energy records within the time-series parameters matches the corresponding diagnostic and treatment records within the structured clinical indicator sequence for the same surgical period. After all time-matching operations are completed, synchronous multi-source parameter data for the three-energy electrosurgical procedure is generated. Within this data, all energy-related content and patient clinical diagnostic and treatment-related content are bound to the same time reference.

[0079] The system reads all records with different measurement standards from the synchronous multi-source parameter data of the generated three-energy electrosurgery. For each type of record with measurement standard differences, a unified numerical conversion rule is defined. The numerical conversion operation is performed on all records in the synchronous multi-source parameter data one by one according to the defined conversion rule to eliminate the measurement standard differences between all records. After all numerical conversion operations are completed, a standardized multi-source parameter for three-energy electrosurgery is generated. All energy operation records in this parameter are based on a completely unified measurement standard with the patient's clinical diagnosis and treatment records, so there are no data reading obstacles caused by inconsistent measurement standards.

[0080] The beneficial effects include unifying the time reference and measurement standards of the various energy operation data generated by the three-energy surgical equipment with the patient's clinical diagnosis and treatment data, eliminating the problems of time sequence misalignment and measurement differences in data from different sources, and allowing subsequent feature extraction, interactive analysis, and risk assessment processes to directly read the standardized multi-source parameters that have been normalized. This avoids the problems of missing features and biased risk assessment caused by chaotic multi-source data formats, and completes the automatic data normalization process throughout the entire process without the need for manual adjustment of various data layouts and measurement formats, thereby continuously improving the basic data processing quality of the entire risk prediction process.

[0081] D2. Extract time-frequency domain features from the three sets of energy operation parameters in the standardized multi-source parameters to obtain the energy feature vector of the three-energy electrosurgery.

[0082] In this embodiment of the invention, the step of extracting time-frequency domain features from the three sets of energy operating parameters in the standardized multi-source parameters to obtain the energy feature vector of the three-energy electrosurgery includes:

[0083] The high-frequency electrical energy parameter, radiofrequency ablation energy parameter, and low-temperature plasma ablation energy parameter in the standardized multi-source parameters are separated to obtain the independent energy parameters of the three-energy electrosurgery.

[0084] The independent energy parameters are analyzed to obtain the time-domain and frequency-domain characteristic parameters of the three-energy electrosurgery.

[0085] The time-domain feature parameters and the frequency-domain feature parameters belonging to the same energy type are paired, and the paired time-domain feature parameters and the frequency-domain feature parameters are spliced ​​and fused to obtain the energy feature vector of the three-energy electrosurgery.

[0086] The entire storage content of the standardized multi-source parameters is traversed, and the data is classified according to the energy type identifier corresponding to the data. All data content with high-frequency electrical energy identifier is extracted from the overall data as high-frequency electrical energy parameters, all data content with radiofrequency ablation energy identifier is extracted as radiofrequency ablation energy parameters, and all data content with low-temperature plasma ablation energy identifier is extracted as low-temperature plasma ablation energy parameters. The three types of separately extracted parameters are uniformly collected and integrated to generate independent energy parameters for three-energy electrosurgery. The three types of energy-related data within the independent energy parameters are independently classified, and there is no situation where different types of energy data are mixed in storage.

[0087] Each segment of continuous record content within the independent energy parameters of three-energy electrosurgery is read sequentially. The data content showing the changing patterns over time is extracted based on the chronological order of the data records. After extraction, the data is summarized to form the time-domain feature parameters of three-energy electrosurgery. Then, the periodic change patterns of all continuous records within the independent energy parameters are decomposed to extract the change content corresponding to the data fluctuation period. All period-related decomposed content is summarized to form the frequency-domain feature parameters of three-energy electrosurgery. The time-domain feature parameters and frequency-domain feature parameters respectively contain the data information of two different types of change patterns in the independent energy parameters.

[0088] The system identifies the energy type attribution identifier for each set of data within the time-domain feature parameters and simultaneously identifies the energy type attribution identifier for each set of data within the frequency-domain feature parameters. It establishes a fixed binding association between time-domain feature parameter segments and frequency-domain feature parameter segments with completely consistent identifiers to complete feature pairing. Following a fixed front-to-back arrangement, it sequentially arranges and merges the paired time-domain feature parameter content and frequency-domain feature parameter content. After the splicing operation of the paired data corresponding to all energy types is completed, it generates the energy feature vector of three-energy electrosurgery. The energy feature vector fully contains the time-domain and frequency-domain combined feature information corresponding to each of the three energy types, and the feature information of each type of energy is kept in a distinguishable arrangement.

[0089] The beneficial effects are that it accurately separates the three types of energy data within the standardized multi-source parameters to achieve independent data division, and simultaneously and completely mines the two types of change characteristics of energy data over time and periodic fluctuations. By pairing and splicing energy features of the same type, a complete and unified energy feature vector is formed, which provides complete and missing underlying feature materials for subsequent multi-energy interaction and correlation analysis, avoids the loss of energy change information caused by single-dimensional feature collection, and ensures the data integrity of subsequent interaction impact analysis and risk basis assessment.

[0090] D3. The energy feature vectors are associated and paired to obtain three sets of energy parameter pairs for the three-energy electrosurgical procedure, and the interaction influence analysis of the three sets of energy parameter pairs is performed to obtain the interaction feature vector of the three-energy electrosurgical procedure.

[0091] In this embodiment of the invention, the process of associating and pairing the energy feature vectors to obtain three sets of energy parameter pairs for the three-energy electrosurgical procedure, and performing interaction influence analysis on the three sets of energy parameter pairs to obtain the interaction feature vector of the three-energy electrosurgical procedure, includes:

[0092] The feature components in the energy feature vector are combined and paired to obtain three sets of energy parameter pairs for the energy electrosurgery. The three sets of energy parameter pairs include high-frequency electrical energy-radiofrequency ablation energy parameter pairs, high-frequency electrical energy-low-temperature plasma ablation energy parameter pairs, and radiofrequency ablation energy-low-temperature plasma ablation energy parameter pairs.

[0093] Synchronous fluctuation analysis and amplitude modulation relationship analysis were performed on the energy output parameters of the three sets of energy parameter pairs respectively to obtain the fluctuation correlation characteristics and modulation correlation characteristics of the three sets of energy parameter pairs;

[0094] The fluctuation correlation feature and the modulation correlation feature are cross-fused to obtain the interaction influence coefficients of the three sets of energy parameter pairs;

[0095] The interaction influence coefficients are combined in a fixed-dimensional order to obtain the interaction feature vector of the three-energy electrosurgery.

[0096] The process involves performing synchronous fluctuation analysis and amplitude modulation relationship analysis on the energy output parameters of the three sets of energy parameter pairs, respectively, to obtain the fluctuation correlation characteristics and modulation correlation characteristics of the three sets of energy parameter pairs, including:

[0097] The energy output parameters in the three sets of energy parameter pairs are divided into time-series sampling windows to obtain the synchronous analysis data segments of the three sets of energy parameter pairs;

[0098] Based on the synchronous analysis data segment, the parameters of the energy parameter pairs in the three sets of energy parameter pairs are registered in the time domain to obtain the synchronous fluctuation sequence of the energy parameter pairs;

[0099] The amplitude of the synchronous fluctuation sequence is compared to obtain the amplitude difference of the energy parameter pair, and the trend consistency of the amplitude difference is measured to obtain the fluctuation correlation characteristics of the energy parameter pair.

[0100] Based on the synchronous analysis data segment, the amplitude envelope change rate analysis is performed on the parameters of the energy parameter pair to obtain the amplitude modulation parameter sequence of the energy parameter pair;

[0101] The amplitude modulation ratio sequence is extracted from the amplitude modulation parameter sequence, and the directional consistency analysis is performed on the extracted modulation ratio sequence to obtain the modulation correlation characteristics of the energy parameter pair.

[0102] The feature components belonging to different energy categories within the energy feature vector are extracted sequentially. The feature components belonging to high-frequency electrical energy and radiofrequency ablation energy are bound together to form high-frequency electrical energy-radiofrequency ablation energy parameter pairs. The feature components belonging to high-frequency electrical energy and low-temperature plasma ablation energy are bound together to form high-frequency electrical energy-low-temperature plasma ablation energy parameter pairs. The feature components belonging to radiofrequency ablation energy and low-temperature plasma ablation energy are bound together to form radiofrequency ablation energy-low-temperature plasma ablation energy parameter pairs. The three sets of parameter pairs that have been bound together are summarized and collected to obtain the three sets of energy parameter pairs for three-energy electrosurgery. The three sets of energy parameter pairs completely cover all the pairwise combinations of the three energy categories.

[0103] Select any one of the three energy parameter pairs and divide it into multiple continuous recording segments of equal length according to a unified time truncation rule. Each segment is used as a set of synchronous analysis data segments. After all the division operations are completed, a dedicated synchronous analysis data segment is generated for each energy parameter pair. Each synchronous analysis data segment contains the complete output records of the two types of energy for that parameter pair during the same time period.

[0104] Retrieve all records within the dedicated synchronous analysis data segment for the corresponding energy parameter pair, align each record of the two types of energy output parameters according to a unified time scale, ensuring that each record of the two types of energy output parameters corresponds to a completely consistent surgical time node. After the alignment operation is completed, generate a synchronous fluctuation sequence for the energy parameter pair. Within the synchronous fluctuation sequence, the two types of energy data maintain a completely synchronized time arrangement logic, and there are no records with misaligned time nodes.

[0105] The two types of energy fluctuation values ​​corresponding to the same time node within the synchronous fluctuation sequence are read segment by segment. The actual difference between the two types of energy fluctuation values ​​at the same time node is compared in turn. The difference information of all time nodes is summarized to form the fluctuation amplitude difference of the energy parameter pair. The change trend of the fluctuation amplitude difference is observed in sequence along the time progression. The change trend of the difference in all time periods is statistically analyzed. The recording interval length is kept uniform. The statistical results of all intervals are fully collected to form the fluctuation correlation feature of the energy parameter pair. The fluctuation correlation feature fully records the difference in amplitude of the two sets of energy fluctuations and the matching of the change trend.

[0106] Retrieve all energy output records within the dedicated synchronous analysis data segment for the corresponding energy parameter pair, continuously track the upper and lower floating boundaries of the two types of energy output values ​​along the time sequence, continuously calculate the numerical change amplitude of the floating boundary between adjacent recording nodes, arrange the boundary change amplitudes obtained from all time periods in an orderly manner, and generate the amplitude modulation parameter sequence for the energy parameter pair. The amplitude modulation parameter sequence completely preserves all the change information of the amplitude boundaries of the two types of energy output as the surgical process progresses.

[0107] The amplitude modulation parameter sequence is segmented, and the boundary change amplitude values ​​of the two types of energy within the same time window are extracted. The amplitude modulation ratio corresponding to each window is calculated using a fixed numerical comparison rule. After all windows are converted, all ratio values ​​are arranged in chronological order to form an amplitude modulation ratio sequence. The increase and decrease trends of the ratio sequence are observed sequentially along the progress of the operation. The recording intervals in which the increase and decrease trends of the ratios remain consistent throughout the entire continuous time period are statistically analyzed. The statistical results of all intervals are summarized to form the modulation correlation characteristics of the energy parameter pair. The modulation correlation characteristics completely record the matching of the magnitude and direction of change of the ratio of the amplitude modulation amplitudes of the two sets of energy.

[0108] The fluctuation correlation feature and modulation correlation feature corresponding to the same set of energy parameters are retrieved. All recorded information within the fluctuation correlation feature and the modulation correlation feature are superimposed and stored according to time nodes. The recorded content of the same time node within the two types of features is bound and integrated. The weighting rule is set to 0.6 for the fluctuation correlation feature and 0.4 for the modulation correlation feature. The weighted sum is used to obtain the scalar interaction coefficient of a single set of parameters. The interaction influence coefficient simultaneously carries the fluctuation coordination law and amplitude modulation coordination law of the set of energy parameters.

[0109] Following a fixed sequence of high-frequency electrical energy-radiofrequency ablation energy parameter pairs, high-frequency electrical energy-low-temperature plasma ablation energy parameter pairs, and radiofrequency ablation energy-low-temperature plasma ablation energy parameter pairs, the interaction coefficients corresponding to each of the three energy parameter pairs are extracted sequentially. The three sets of interaction coefficients are then continuously arranged and integrated into a unified and complete data sequence. After the arrangement and integration operation is completed, an interaction feature vector of three-energy electrosurgery is generated. The interaction feature vector strictly follows a fixed dimensional order to include all the interaction coefficients corresponding to each pair of energy combinations. The interaction information corresponding to the three sets of energy combinations is clearly and orderly divided.

[0110] The beneficial effects include complete coverage of all pairwise combinations of the three types of energy, precise decomposition of the synchronous fluctuation and amplitude modulation coordination patterns of each energy combination, complete quantification of the degree of interaction between the two energy groups through cross-fusion of the two types of features, integration of all combination interaction information into a standardized interaction feature vector according to a fixed dimension order, complete restoration of the coupling details when multiple energies are output synchronously during surgery, providing a comprehensive and detailed interaction quantification basis for subsequent stripping of multi-energy interaction disturbances and calculation of single-energy risk base, and eliminating risk assessment bias caused by the lack of multi-energy interaction information.

[0111] D4. Based on the interaction feature vector, evaluate the operational stability of the three-energy output parameters in the standardized multi-source parameters to obtain the single-energy risk basis of the three-energy electrosurgery.

[0112] In this embodiment of the invention, the step of evaluating the operational stability of the three-energy output parameters in the standardized multi-source parameters based on the interaction feature vector to obtain the single-energy risk basis of the three-energy electrosurgery includes:

[0113] Based on the interaction feature vector, the interaction influence coefficient of the high-frequency electrical energy-radio frequency ablation energy parameter pair and the interaction influence coefficient of the high-frequency electrical energy-low-temperature plasma ablation energy parameter pair are combined into the high-frequency electrical energy disturbance factor of the high-frequency electrical energy parameter.

[0114] Based on the interaction feature vector, the interaction influence coefficient of the high-frequency electrical energy-radio frequency ablation energy parameter pair and the interaction influence coefficient of the radio frequency ablation energy-low-temperature plasma ablation energy parameter pair are combined into the radio frequency ablation energy disturbance factor of the radio frequency ablation energy parameter.

[0115] Based on the interaction feature vector, the interaction influence coefficients of the high-frequency electrical energy-low-temperature plasma ablation energy parameter pair and the radio frequency ablation energy-low-temperature plasma ablation energy parameter pair are combined into the plasma ablation energy disturbance factor of the plasma ablation energy parameter.

[0116] Based on the high-frequency electrical energy perturbation factor, the radiofrequency ablation energy perturbation factor, and the plasma ablation energy perturbation factor, the independent energy parameters of the three-energy electrosurgical procedure are perturbed and stripped to obtain the deperturbed parameter sequence of the independent energy parameters;

[0117] The amplitude fluctuation statistics and frequency fluctuation statistics of the undisturbed parameter sequence are performed to obtain the amplitude fluctuation statistics and frequency fluctuation statistics of the undisturbed parameter sequence.

[0118] The amplitude fluctuation statistics and the frequency fluctuation statistics are mapped to risk base values ​​corresponding to energy types, and the risk base values ​​are combined in order of energy type to obtain the single-energy risk base of the three-energy electrosurgery.

[0119] The interaction feature vector stores two sets of paired interaction coefficients related to high-frequency electrical energy. All time-series records of the interaction coefficients between high-frequency electrical energy and radiofrequency ablation energy parameters are extracted. Then, all time-series records of the interaction coefficients between high-frequency electrical energy and low-temperature plasma ablation energy parameters are extracted. Using the surgical time axis as a unified benchmark, the two sets of coefficients are superimposed and integrated on a time-by-time basis to generate a high-frequency electrical energy perturbation factor corresponding to the high-frequency electrical energy parameters. This perturbation factor fully includes the superimposed interference information of high-frequency electrical energy generated by the synchronous operation of radiofrequency ablation and low-temperature plasma energy throughout the entire process.

[0120] Two sets of paired interaction coefficients related to the radiofrequency ablation energy within the interaction feature vector are extracted. The complete time-series record of the interaction coefficients between the high-frequency electrical energy and radiofrequency ablation energy parameters is retrieved, and then the complete time-series record of the interaction coefficients between the radiofrequency ablation energy and low-temperature plasma ablation energy parameters is retrieved. The contents of the two sets of coefficients are fused and summarized using each operation period of the surgery as the matching benchmark. After the fusion and summary are completed, the radiofrequency ablation energy perturbation factor corresponding to the radiofrequency ablation energy parameters is generated. This perturbation factor fully carries all the interference information continuously applied to the radiofrequency ablation energy during the synchronous output of the two types of energy, high-frequency electrical and low-temperature plasma.

[0121] Two sets of paired interaction coefficients related to low-temperature plasma ablation energy were extracted from the interaction feature vector. The time-series records of the interaction coefficients between high-frequency electrical energy and low-temperature plasma ablation energy parameters and between radiofrequency ablation energy and low-temperature plasma ablation energy parameters were retrieved respectively. The records of the two sets of coefficients were aligned with the surgical progress sequence and uniformly collected. After collection, the plasma ablation energy perturbation factor corresponding to the plasma ablation energy parameters was generated. This perturbation factor completely records all coupling interference information that continuously acts on the low-temperature plasma ablation energy when the two types of energy, high-frequency electrical and radiofrequency ablation, work synchronously.

[0122] The independent energy parameters of the three-energy electrosurgical procedure were separated into three categories: high-frequency electricity, radiofrequency ablation, and low-temperature plasma ablation. Each category of independent parameters was matched with a corresponding exclusive perturbation factor. The external energy coupling interference data carried by the perturbation factor was removed one by one along the entire time sequence of the surgery. The interference removal operation was performed independently for each of the three categories of independent parameters. After all external interference content was removed from each category of parameters, a corresponding de-perturbation parameter sequence was generated. Each de-perturbation parameter sequence only retained the original output record of the corresponding energy that was not affected by the other two energy categories.

[0123] The entire time-series records of the perturbation parameter sequence corresponding to each type of energy are completely traversed. The fluctuation range of the energy output value at each recording node is continuously collected along the surgical time sequence. The fluctuation range records of all time periods are summarized to form the amplitude fluctuation statistics of the perturbation parameter sequence. Simultaneously, the interval pattern of the periodic recurrence of values ​​within the sequence is continuously captured. The frequency fluctuation statistics of the perturbation parameter sequence are completely summarized to form the frequency fluctuation statistics of the perturbation parameter sequence. The amplitude fluctuation statistics and frequency fluctuation statistics respectively retain the two types of self-fluctuation information of the output amplitude and output period of a single type of energy after removing external coupling interference.

[0124] The amplitude fluctuation statistics and frequency fluctuation statistics associated with each type of energy are uniformly converted to generate the exclusive risk base value for that energy. After the conversion is completed, the risk base values ​​of the three types of energy are collected in a fixed arrangement order of high-frequency electrical energy, radiofrequency ablation energy, and low-temperature plasma ablation energy. After all the base values ​​are integrated in an orderly manner, a single-energy risk base for three-energy electrosurgery is generated. The single-energy risk base stores the independent risk benchmark information corresponding to the operating state of each type of energy after the external coupling interference is removed.

[0125] The beneficial effects are as follows: two sets of exclusive interaction coefficients are matched for each of the three types of energy to construct differentiated disturbance factors, distinguish the external interference sources that different energies can withstand, rely on the disturbance factors to remove the numerical interference caused by multi-energy coupling, accurately restore the original operating data of various energies, simultaneously collect amplitude and frequency fluctuation dimension information to quantify the instability of a single energy, orderly integrate the independent risk benchmarks of various energies to form a standardized single-energy risk base, clearly distinguish the two types of risk causes: single-energy operation anomalies and multi-energy coupling superposition, avoid the distortion of single-energy risk assessment caused by multi-energy mutual interference, and provide accurate independent risk quantification basis for subsequent comprehensive energy load calculation.

[0126] D5. Based on the interaction feature vector and the single-energy risk basis, the standardized multi-source parameters are decomposed into three-energy cumulative effects to obtain the tissue energy deposition amount of the patient. The tissue energy deposition amount is then coupled and compared with the tissue tolerance parameters in the clinical diagnosis and treatment data to obtain the comprehensive energy load of the three-energy electrosurgery.

[0127] In this embodiment of the invention, the standardized multi-source parameters are decomposed into three-energy cumulative effects based on the interaction feature vector and the single-energy risk basis to obtain the patient's tissue energy deposition amount. This tissue energy deposition amount is then coupled and compared with tissue tolerance parameters in the clinical data to obtain the comprehensive energy load of the three-energy electrosurgery, including:

[0128] The high-frequency electrical energy output parameter sequence, radiofrequency ablation energy output parameter sequence, and low-temperature plasma ablation energy output parameter sequence in the standardized multi-source parameters are subjected to time-period integration processing to obtain the initial cumulative effect of the high-frequency electrical energy output parameter sequence, radiofrequency ablation energy output parameter sequence, and low-temperature plasma ablation energy output parameter sequence.

[0129] Based on the interaction feature vector, the initial cumulative effect is corrected by interaction, and the corrected cumulative effect of the high-frequency electrical energy output parameter sequence, the radiofrequency ablation energy output parameter sequence, and the low-temperature plasma ablation energy output parameter sequence is obtained.

[0130] Based on the single-energy risk basis, the modified cumulative effect is weighted by stability to obtain the weighted cumulative effect of the high-frequency electrical energy output parameter sequence, the radiofrequency ablation energy output parameter sequence, and the low-temperature plasma ablation energy output parameter sequence;

[0131] The weighted cumulative effects are summed to obtain the tissue energy deposition amount of the patient.

[0132] The overall energy load of the three-energy electrosurgery is calculated based on the amount of energy deposited in the tissue and the tissue tolerance parameters in the clinical data.

[0133] The formula for calculating the overall energy load is as follows:

[0134] ;

[0135] in, For the aforementioned comprehensive energy load, The amount of energy deposited in the tissue. The tissue tolerance parameter, For the single-energy risk basis, This is the feature vector of the interaction.

[0136] Complete high-frequency electrical energy output parameter sequences, radiofrequency ablation energy output parameter sequences, and low-temperature plasma ablation energy output parameter sequences are extracted from standardized multi-source parameters. Multiple continuous calculation periods of equal length are divided according to the time progression of the entire operation. For each type of energy output parameter sequence, all energy output values ​​within each calculation period are accumulated and summarized segment by segment. The accumulation range covers the entire operation from start to finish. After the three types of energy have completed the full accumulation calculation, the initial cumulative effect of the high-frequency electrical energy output parameter sequence, radiofrequency ablation energy output parameter sequence, and low-temperature plasma ablation energy output parameter sequence is generated.

[0137] All interaction coefficients corresponding to the pairwise combinations of the three energy groups within the interaction feature vector are retrieved, and the initial cumulative effect of the corresponding energy type is matched. The initial cumulative effect of each type of energy is adjusted in the same direction according to the degree of influence of the interaction between the energies to offset the calculation deviation caused by the multi-energy coupling effect to the cumulative effect of a single type of energy. After the adjustment is completed, the corrected cumulative effect of the high-frequency electrical energy output parameter sequence, the radiofrequency ablation energy output parameter sequence, and the low-temperature plasma ablation energy output parameter sequence is generated.

[0138] The risk base values ​​of the three energy types within the single-energy risk base are retrieved, and the corrected cumulative effect is matched for each energy type. The weighting of the corrected cumulative effect is adjusted according to the operational stability of each energy type. The lower the operational stability of the energy, the higher the weighting of the cumulative effect. After the weighting of all three energy types is adjusted, the weighted cumulative effect of the high-frequency electrical energy output parameter sequence, the radiofrequency ablation energy output parameter sequence, and the low-temperature plasma ablation energy output parameter sequence is generated.

[0139] The weighted cumulative effects of the three types of energy—high-frequency electrical energy, radiofrequency ablation energy, and low-temperature plasma ablation energy—are summed up to integrate the total energy effects of the three types of energy acting on the patient's target tissue. After summarizing, the amount of energy deposited in the patient's tissue is obtained.

[0140] Tissue tolerance parameters corresponding to the surgical site of the patient are extracted from clinical diagnosis and treatment data. The amount of tissue energy deposition of the patient is compared with the tissue tolerance parameters. At the same time, the risk factors carried by the single-energy risk base and the interaction feature vector are combined for numerical correction. After the calculation is completed, the comprehensive energy load of three-energy electrosurgery is obtained.

[0141] The tissue energy deposition amount is obtained by summing the weighted cumulative effects of three types of energy: high-frequency electrical energy, radiofrequency ablation energy, and low-temperature plasma ablation energy. The tissue tolerance parameter is taken from the tolerance records of the corresponding surgical lesion tissue in the patient's preoperative physiological index data and intraoperative vital sign monitoring data collected by standardized multi-source parameters. The single-energy risk base value is obtained by integrating the three types of energy after removing external coupling interference according to the fixed arrangement order of high-frequency electrical energy, radiofrequency ablation energy, and low-temperature plasma ablation energy. The interaction feature vector is generated by integrating the interaction influence coefficients of the three sets of energy parameters arranged in a fixed dimension order.

[0142] Before calculation, the average values ​​of the three-dimensional risk basis vector and the three-dimensional interaction feature vector are calculated to ensure that the parameter dimensions are consistent. First, the numerical conversion between the tissue energy deposition amount and the tissue tolerance parameter is completed to obtain the baseline value of the basic tissue load. Then, the risk correction coefficient is obtained by superimposing the single-energy self-operational instability risk information carried by the single-energy risk basis and the multi-energy coupling interference information carried by the interaction feature vector. The baseline value of the basic tissue load is multiplied by the risk correction coefficient to obtain the comprehensive energy load. The four core influencing factors of the actual total energy received by the tissue, the upper limit of human tissue tolerance, single-energy self-operational risk, and multi-energy mutual interference are fully and synchronously incorporated. The output is a unified quantitative result that can synchronously reflect the tissue energy carrying capacity and the degree of abnormality of surgical energy operation.

[0143] When the tissue energy deposition increases synchronously while the other three types of data remain unchanged, the overall energy load continues to increase synchronously. When the tissue tolerance parameter increases synchronously while the other three types of data remain unchanged, the overall energy load continues to decrease synchronously. When the single energy risk base increases synchronously while the other three types of data remain unchanged, the overall energy load continues to increase synchronously. When the interaction feature vector increases synchronously while the other three types of data remain unchanged, the overall energy load continues to increase synchronously. The numerical changes of the four types of data do not cancel each other out. All data are processed for dimensional alignment using a unified standardized mapping transformation rule, and there is no trend judgment bias caused by differences in measurement standards.

[0144] The beneficial effects are as follows: the initial total effect of a single type of energy is accurately calculated by accumulating it segment by segment over equal time periods; the calculation deviation of the cumulative amount caused by multi-energy coupling is corrected by relying on the interaction coefficient; the weighted adjustment of the cumulative effect is completed by combining the stability of the single energy itself; and the summation is used to obtain the tissue energy deposition amount that closely matches the actual tissue effect. Then, the comprehensive energy load is calculated by comparing it with the tissue tolerance parameters of the corresponding part of the patient. This fully covers the entire process of energy accumulation from independent calculation of a single type to correction of coupling effect and comparison of tissue tolerance. The final comprehensive energy load can truly reflect the actual energy pressure borne by the tissue at the surgical site of the patient, and provides an accurate and reliable quantitative calculation basis for the subsequent determination of complication risk type and risk level classification.

[0145] D6. Based on the comprehensive energy load, the single-energy risk base and the interaction feature vector, determine the complication risk type and risk level of the three-energy electrosurgery.

[0146] In this embodiment of the invention, determining the complication risk type and risk level of the three-energy electrosurgery based on the comprehensive energy load, the single-energy risk base, and the interaction feature vector includes:

[0147] The overall energy load level of the three-energy electrosurgical procedure is obtained by segmenting the comprehensive energy load into segments and mapping the load level.

[0148] The risk contribution of the single-energy risk base was analyzed to obtain the risk contribution ratio of high-frequency electrical energy, radiofrequency ablation energy and low-temperature plasma ablation energy in the three-energy electrosurgery.

[0149] The interaction feature vector is analyzed for coupling strength to obtain the interaction coupling strength index of the three-energy electrosurgery.

[0150] Based on the overall energy load level, the risk contribution ratio of high-frequency electrical energy, the risk contribution ratio of radiofrequency ablation energy, the risk contribution ratio of low-temperature plasma ablation energy, and the interaction coupling strength index, the risk type of the three-energy electrosurgical procedure is determined, and the complication risk type of the three-energy electrosurgical procedure is obtained.

[0151] Based on the complication risk type, a comprehensive risk level mapping is performed on the overall energy load level and the interaction coupling strength index to obtain the complication risk level of the three-energy electrosurgery.

[0152] The pre-defined multi-segment continuous load interval division criteria are retrieved and divided into three graded thresholds. The thresholds are obtained by statistical fitting of clinical complication cases. When L∈[1,1.6], it is low load; when L∈(1.6,2.3], it is medium load; and when L∈(2.3,3], it is high load. The actual recorded content corresponding to the comprehensive energy load is matched to the corresponding interval. The hierarchical mapping operation is completed according to the hierarchical identifier corresponding to the interval. After all matching and mapping operations are completed, the overall energy load level of the three-energy electrosurgery is obtained. The overall energy load level intuitively reflects the overall interval level of the energy load borne by the tissue throughout the operation.

[0153] The risk base value of a single energy is divided into three independent risk base values ​​belonging to three energy categories. The risk base values ​​corresponding to the three energy categories are then aggregated to obtain a total baseline value. The independent risk base value corresponding to each energy category is used to perform a percentage conversion operation with the total baseline value. After the conversion, the risk contribution percentages of high-frequency electrical energy, radiofrequency ablation energy, and low-temperature plasma ablation energy in three-energy electrosurgery are generated in sequence. The three percentage data reflect the proportion of the instability of the single energy itself in the overall independent risk. If the percentage of a single energy category exceeds 60%, the energy is determined to be the dominant source of single risk.

[0154] The interaction influence coefficients of the three energy pairs within the interaction feature vector are decomposed, and all interaction influence coefficients are summarized to obtain the overall interaction baseline value. The proportion of the interaction influence coefficient of each energy pair in the overall interaction baseline value is calculated. Combined with the temporal change amplitude of the coefficient of each pair, a unified quantitative conversion is completed. After the conversion, the interaction coupling strength index of three-energy electrosurgery is generated. The interaction coupling strength index fully quantifies the overall effect of mutual interference and superposition when the three types of energy run synchronously in pairs. The coupling strength index takes the value of 0 to 1. The higher the value, the stronger the mutual interference of multiple energies.

[0155] Simultaneously retrieve the generated overall energy load level, high-frequency electrical energy risk contribution ratio, radiofrequency ablation energy risk contribution ratio, low-temperature plasma ablation energy risk contribution ratio, and interactive coupling strength index. Compare with the preset multi-dimensional risk matching judgment criteria, match the risk cause classification labels corresponding to different index combinations, and obtain the complication risk type of three-energy electrosurgery after matching. The complication risk type can clearly distinguish whether the risk comes from a single energy operation abnormality, the superposition of multiple energy interactive coupling, or the combined effect of two types of factors.

[0156] The identified complication risk types are retrieved and uniformly classified into four levels: no risk, mild risk, moderate risk, and severe risk. This can be directly used for real-time intraoperative early warning output. The specific level mapping matching standard for this risk type is retrieved, and the overall energy load level and the interaction coupling strength index are substituted into the matching standard to complete the level correspondence conversion. After the conversion operation is completed, the complication risk level of the three-energy electrosurgery is obtained. The complication risk level uniformly quantifies the overall risk of surgical complications.

[0157] The beneficial effects include extracting multiple risk quantification indicators from three different dimensions: overall load, single-energy independent risk, and multi-energy coupled interaction. Based on the combination of multi-dimensional indicators, it accurately distinguishes the complication risk types corresponding to different causes. Then, it completes the standardized risk level classification by combining risk type-specific mapping rules. It fully covers the two core output contents of risk cause classification and risk degree quantification. The multi-dimensional indicators constrain each other to avoid risk judgment bias caused by single-dimensional data. The output risk types and risk levels can be directly used for real-time intraoperative early warning and preoperative surgical energy plan optimization and adjustment.

[0158] like Figure 2 The diagram shown is a functional block diagram of a three-energy electrosurgical complication risk prediction system provided in an embodiment of the present invention.

[0159] The three-energy electrosurgical complication risk prediction system 100 described in this invention can be installed in an electronic device. Depending on the functions implemented, the three-energy electrosurgical complication risk prediction system 100 may include a multi-source data fusion module 101, an energy time-frequency feature extraction module 102, an energy interaction feature analysis module 103, a single-energy risk baseline assessment module 104, an energy load coupling calculation module 105, and a concurrent risk determination module 106. The modules described in this invention can also be referred to as units, which are a series of computer program segments that can be executed by the processor of an electronic device and perform a fixed function, stored in the memory of the electronic device.

[0160] In this embodiment, the functions of each module / unit are as follows:

[0161] The multi-source data fusion module 101 is used to fuse and normalize the operating parameters of the three-energy electrosurgery with the patient's clinical diagnosis and treatment data to obtain the standardized multi-source parameters of the three-energy electrosurgery.

[0162] The energy time-frequency feature extraction module 102 is used to extract time-frequency domain features from the three sets of energy operation parameters in the standardized multi-source parameters to obtain the energy feature vector of the three-energy electrosurgery.

[0163] The energy interaction feature analysis module 103 is used to associate and pair the energy feature vectors to obtain three sets of energy parameter pairs for the three-energy electrosurgical procedure, and to perform interaction influence analysis on the three sets of energy parameter pairs to obtain the interaction feature vector of the three-energy electrosurgical procedure.

[0164] The single-energy risk base assessment module 104 is used to evaluate the operational stability of the three-energy output parameters in the standardized multi-source parameters based on the interaction feature vector, so as to obtain the single-energy risk base of the three-energy electrosurgery.

[0165] The energy load coupling calculation module 105 is used to perform three-energy cumulative effect decomposition on the standardized multi-source parameters based on the interaction feature vector and the single-energy risk basis to obtain the tissue energy deposition amount of the patient, and to couple and compare the tissue energy deposition amount with the tissue tolerance parameter in the clinical diagnosis and treatment data to obtain the comprehensive energy load of the three-energy electrosurgery.

[0166] The concurrent risk assessment module 106 is used to determine the complication risk type and risk level of the three-energy electrosurgery based on the comprehensive energy load, the single-energy risk base and the interaction feature vector.

[0167] In the several embodiments provided by this invention, it should be understood that the disclosed methods and systems can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0168] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0169] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0170] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0171] This application embodiment can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0172] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for predicting the risk of complications in three-energy electrosurgery, characterized in that, The method includes: D1. The operating parameters of the three-energy electrosurgical procedure are fused and normalized with the patient's clinical diagnosis and treatment data to obtain the standardized multi-source parameters of the three-energy electrosurgical procedure. D2. Extract time-frequency domain features from the three sets of energy operation parameters in the standardized multi-source parameters to obtain the energy feature vector of the three-energy electrosurgery. D3. The energy feature vectors are associated and paired to obtain three sets of energy parameter pairs for the three-energy electrosurgical procedure, and the interaction influence analysis of the three sets of energy parameter pairs is performed to obtain the interaction feature vector of the three-energy electrosurgical procedure. D4. Based on the interaction feature vector, evaluate the operational stability of the three-energy output parameters in the standardized multi-source parameters to obtain the single-energy risk basis of the three-energy electrosurgery. D5. Based on the interaction feature vector and the single-energy risk basis, the standardized multi-source parameters are decomposed into three-energy cumulative effects to obtain the tissue energy deposition amount of the patient. The tissue energy deposition amount is then coupled and compared with the tissue tolerance parameters in the clinical diagnosis and treatment data to obtain the comprehensive energy load of the three-energy electrosurgery. D6. Based on the comprehensive energy load, the single-energy risk base and the interaction feature vector, determine the complication risk type and risk level of the three-energy electrosurgery.

2. The method for predicting the risk of complications in three-energy electrosurgical procedures as described in claim 1, characterized in that, The operating parameters of the three-energy electrosurgical procedure are fused and normalized with the patient's clinical diagnosis and treatment data to obtain standardized multi-source parameters for the three-energy electrosurgical procedure, including: The operating parameters of the three-energy electrosurgical procedure are obtained, and the operating parameters are time-aligned to obtain the time-series operating parameters of the three-energy electrosurgical procedure. The time-series operating parameters include high-frequency electrical energy output parameters, radiofrequency ablation energy output parameters, and low-temperature plasma ablation energy output parameters. The patient's preoperative physiological indicators and intraoperative vital sign monitoring data were extracted in a structured manner to obtain the patient's structured clinical indicator sequence; The timing operation parameters and the structured clinical indicator sequence are time-base synchronized to obtain the synchronous multi-source parameter data of the three-energy electrosurgery. The synchronous multi-source parameter data is subjected to dimensional normalization mapping to obtain the standardized multi-source parameters of the three-energy electrosurgery.

3. The method for predicting the risk of complications in three-energy electrosurgical procedures as described in claim 1, characterized in that, The step of extracting time-frequency domain features from the three sets of energy operation parameters in the standardized multi-source parameters to obtain the energy feature vector of the three-energy electrosurgery includes: The high-frequency electrical energy parameter, radiofrequency ablation energy parameter, and low-temperature plasma ablation energy parameter in the standardized multi-source parameters are separated to obtain the independent energy parameters of the three-energy electrosurgery. The independent energy parameters are analyzed to obtain the time-domain and frequency-domain characteristic parameters of the three-energy electrosurgery. The time-domain feature parameters and the frequency-domain feature parameters belonging to the same energy type are paired, and the paired time-domain feature parameters and the frequency-domain feature parameters are spliced ​​and fused to obtain the energy feature vector of the three-energy electrosurgery.

4. The method for predicting the risk of complications in three-energy electrosurgical procedures as described in claim 1, characterized in that, The energy feature vectors are associated and paired to obtain three sets of energy parameter pairs for the three-energy electrosurgical procedure. Interaction influence analysis is then performed on these three sets of energy parameter pairs to obtain the interaction feature vector of the three-energy electrosurgical procedure, including: The feature components in the energy feature vector are combined and paired to obtain three sets of energy parameter pairs for the energy electrosurgery. The three sets of energy parameter pairs include high-frequency electrical energy-radiofrequency ablation energy parameter pairs, high-frequency electrical energy-low-temperature plasma ablation energy parameter pairs, and radiofrequency ablation energy-low-temperature plasma ablation energy parameter pairs. Synchronous fluctuation analysis and amplitude modulation relationship analysis were performed on the energy output parameters of the three sets of energy parameter pairs respectively to obtain the fluctuation correlation characteristics and modulation correlation characteristics of the three sets of energy parameter pairs; The fluctuation correlation feature and the modulation correlation feature are cross-fused to obtain the interaction influence coefficients of the three sets of energy parameter pairs; The interaction influence coefficients are combined in a fixed-dimensional order to obtain the interaction feature vector of the three-energy electrosurgery.

5. The method for predicting the risk of complications in three-energy electrosurgical procedures as described in claim 4, characterized in that, The process involves performing synchronous fluctuation analysis and amplitude modulation relationship analysis on the energy output parameters of the three sets of energy parameter pairs, respectively, to obtain the fluctuation correlation characteristics and modulation correlation characteristics of the three sets of energy parameter pairs, including: The energy output parameters in the three sets of energy parameter pairs are divided into time-series sampling windows to obtain the synchronous analysis data segments of the three sets of energy parameter pairs; Based on the synchronous analysis data segment, the parameters of the energy parameter pairs in the three sets of energy parameter pairs are registered in the time domain to obtain the synchronous fluctuation sequence of the energy parameter pairs; The amplitude of the synchronous fluctuation sequence is compared to obtain the amplitude difference of the energy parameter pair, and the trend consistency of the amplitude difference is measured to obtain the fluctuation correlation characteristics of the energy parameter pair. Based on the synchronous analysis data segment, the amplitude envelope change rate analysis is performed on the parameters of the energy parameter pair to obtain the amplitude modulation parameter sequence of the energy parameter pair; The amplitude modulation ratio sequence is extracted from the amplitude modulation parameter sequence, and the directional consistency analysis is performed on the extracted modulation ratio sequence to obtain the modulation correlation characteristics of the energy parameter pair.

6. The method for predicting the risk of complications in three-energy electrosurgery as described in claim 1, characterized in that, The step of evaluating the operational stability of the three-energy output parameters in the standardized multi-source parameters based on the interaction feature vector to obtain the single-energy risk basis of the three-energy electrosurgery includes: Based on the interaction feature vector, the interaction influence coefficient of the high-frequency electrical energy-radio frequency ablation energy parameter pair and the interaction influence coefficient of the high-frequency electrical energy-low-temperature plasma ablation energy parameter pair are combined into the high-frequency electrical energy disturbance factor of the high-frequency electrical energy parameter. Based on the interaction feature vector, the interaction influence coefficient of the high-frequency electrical energy-radio frequency ablation energy parameter pair and the interaction influence coefficient of the radio frequency ablation energy-low-temperature plasma ablation energy parameter pair are combined into the radio frequency ablation energy disturbance factor of the radio frequency ablation energy parameter. Based on the interaction feature vector, the interaction influence coefficients of the high-frequency electrical energy-low-temperature plasma ablation energy parameter pair and the radio frequency ablation energy-low-temperature plasma ablation energy parameter pair are combined into the plasma ablation energy disturbance factor of the plasma ablation energy parameter. Based on the high-frequency electrical energy perturbation factor, the radiofrequency ablation energy perturbation factor, and the plasma ablation energy perturbation factor, the independent energy parameters of the three-energy electrosurgical procedure are perturbed and stripped to obtain the deperturbed parameter sequence of the independent energy parameters; The amplitude fluctuation statistics and frequency fluctuation statistics of the undisturbed parameter sequence are performed to obtain the amplitude fluctuation statistics and frequency fluctuation statistics of the undisturbed parameter sequence. The amplitude fluctuation statistics and the frequency fluctuation statistics are mapped to risk base values ​​corresponding to energy types, and the risk base values ​​are combined in order of energy type to obtain the single-energy risk base of the three-energy electrosurgery.

7. The method for predicting the risk of complications in three-energy electrosurgical procedures as described in claim 1, characterized in that, Based on the interaction feature vector and the single-energy risk basis, the standardized multi-source parameters are decomposed into a three-energy cumulative effect to obtain the patient's tissue energy deposition amount. This tissue energy deposition amount is then coupled and compared with tissue tolerance parameters from the clinical data to obtain the comprehensive energy load of the three-energy electrosurgery, including: The high-frequency electrical energy output parameter sequence, radiofrequency ablation energy output parameter sequence, and low-temperature plasma ablation energy output parameter sequence in the standardized multi-source parameters are subjected to time-period integration processing to obtain the initial cumulative effect of the high-frequency electrical energy output parameter sequence, radiofrequency ablation energy output parameter sequence, and low-temperature plasma ablation energy output parameter sequence. Based on the interaction feature vector, the initial cumulative effect is corrected by interaction, and the corrected cumulative effect of the high-frequency electrical energy output parameter sequence, the radiofrequency ablation energy output parameter sequence, and the low-temperature plasma ablation energy output parameter sequence is obtained. Based on the single-energy risk basis, the modified cumulative effect is weighted by stability to obtain the weighted cumulative effect of the high-frequency electrical energy output parameter sequence, the radiofrequency ablation energy output parameter sequence, and the low-temperature plasma ablation energy output parameter sequence; The weighted cumulative effects are summed to obtain the tissue energy deposition amount of the patient. The overall energy load of the three-energy electrosurgery is calculated based on the amount of energy deposited in the tissue and the tissue tolerance parameters in the clinical data.

8. The method for predicting the risk of complications in three-energy electrosurgical procedures as described in claim 7, characterized in that, The formula for calculating the overall energy load is as follows: ； in, For the aforementioned comprehensive energy load, The amount of energy deposited in the tissue. The tissue tolerance parameter, For the single-energy risk basis, This is the feature vector of the interaction.

9. The method for predicting the risk of complications in three-energy electrosurgery as described in claim 1, characterized in that, The determination of the complication risk type and risk level of the three-energy electrosurgery based on the comprehensive energy load, the single-energy risk base, and the interaction feature vector includes: The overall energy load level of the three-energy electrosurgical procedure is obtained by segmenting the comprehensive energy load into segments and mapping the load level. The risk contribution of the single-energy risk base was analyzed to obtain the risk contribution ratio of high-frequency electrical energy, radiofrequency ablation energy and low-temperature plasma ablation energy in the three-energy electrosurgery. The interaction feature vector is analyzed for coupling strength to obtain the interaction coupling strength index of the three-energy electrosurgery. Based on the overall energy load level, the risk contribution ratio of high-frequency electrical energy, the risk contribution ratio of radiofrequency ablation energy, the risk contribution ratio of low-temperature plasma ablation energy, and the interaction coupling strength index, the risk type of the three-energy electrosurgical procedure is determined, and the complication risk type of the three-energy electrosurgical procedure is obtained. Based on the complication risk type, a comprehensive risk level mapping is performed on the overall energy load level and the interaction coupling strength index to obtain the complication risk level of the three-energy electrosurgery.

10. A three-energy electrosurgical complication risk prediction system, characterized in that, The system for implementing the three-energy electrosurgical complication risk prediction method according to claim 1 includes: The multi-source data fusion module is used to fuse and normalize the operating parameters of the three-energy electrosurgical procedure with the patient's clinical diagnosis and treatment data to obtain the standardized multi-source parameters of the three-energy electrosurgical procedure. The energy time-frequency feature extraction module is used to extract time-frequency domain features from the three sets of energy operation parameters in the standardized multi-source parameters to obtain the energy feature vector of the three-energy electrosurgery. The energy interaction feature analysis module is used to associate and pair the energy feature vectors to obtain three sets of energy parameter pairs for the three-energy electrosurgical procedure, and to perform interaction influence analysis on the three sets of energy parameter pairs to obtain the interaction feature vector of the three-energy electrosurgical procedure. The single-energy risk baseline assessment module is used to evaluate the operational stability of the three-energy output parameters in the standardized multi-source parameters based on the interaction feature vector, so as to obtain the single-energy risk baseline of the three-energy electrosurgery. The energy load coupling calculation module is used to perform three-energy cumulative effect decomposition on the standardized multi-source parameters based on the interaction feature vector and the single-energy risk basis to obtain the tissue energy deposition amount of the patient, and to couple and compare the tissue energy deposition amount with the tissue tolerance parameter in the clinical diagnosis and treatment data to obtain the comprehensive energy load of the three-energy electrosurgery. The concurrency risk assessment module is used to determine the complication risk type and risk level of the three-energy electrosurgery based on the comprehensive energy load, the single-energy risk base and the interaction feature vector.

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