Fetal delivery operation drilling system and training method based on multi-modal simulation

Abstract

The invention relates to the technical field of medical training, and discloses a fetus delivery operation drilling system and training method based on multi-modal simulation, and the system comprises a hardware module, a scene construction module, a multi-modal simulation training module, a dynamic risk intervention module and a multi-dimensional redisk analysis module. The training method corresponds to the system. According to the application, the problems of lack of maternal and fetal physiological dynamic simulation, insufficient team cooperation mechanism and weak risk pre-judgment ability in traditional training are effectively solved, a full-process standardized immersive training system is constructed, the safety, team cooperation efficiency and emergency processing ability of high-risk fetus surgery operation are significantly improved, and the training efficiency is improved. Scientific and efficient technical support is provided for clinical training of fetal surgery during delivery.

Description

Technical Field

[0001] The present application relates to the field of medical training technology, and specifically to a fetal delivery surgery rehearsal system and training method based on multimodal simulation. Background Art

[0002] Intrapartum fetal surgery (IFO) refers to neonatal surgical treatment for the correction of birth defects during and immediately after delivery of a fetus with congenital birth defects, including: (1) placental-supported IFO (OOPS); (2) extrauterine intrapartum management (EXIT). This operation is a high-difficulty, high-risk, interdisciplinary, multi-team operation that is relatively rare and involves anesthesiologists, neonatologists, obstetricians, and nurses. It requires the medical team to respond quickly and perform their duties. In existing surgical training, most of the training is conducted offline, but offline methods are difficult to operate and the training effect is not good.

[0003] The Chinese invention patent with application number CN202411985487.3 discloses a surgical training method and system based on three-dimensional trajectory tracking of surgical instruments, but the invention cannot provide training for interdisciplinary and multi-team collaborative surgeries.

[0004] In summary, there is an urgent need for a new technical solution for fetal delivery surgical rehearsal based on multimodal simulation. Summary of the Invention

[0005] The purpose of this application is to provide a fetal delivery surgery rehearsal system and training method based on multimodal simulation to solve the technical problems raised in the above background technology.

[0006] To achieve the above objectives, this application discloses the following technical solutions:

[0007] In a first aspect, the present application discloses a fetal delivery surgery rehearsal system based on multimodal simulation, which includes a hardware module, a scenario construction module, a multimodal simulation training module, a dynamic risk intervention module, and a multidimensional replay analysis module;

[0008] The hardware module includes a maternal-fetal physiological simulation chamber and a fetal simulator, wherein the maternal-fetal physiological simulation chamber is used to simulate the maternal-fetal pathophysiological interaction process, and the fetal simulator is used to simulate fetal physical signs;

[0009] The scenario construction module is configured to: construct a case scenario based on the performed fetal intrapartum surgery;

[0010] The multimodal simulation training module includes an anesthesiologist unit, a neonatologist unit, an obstetrician unit, and a nurse unit, each unit corresponding to independent operation simulation and collaborative operation simulation of different roles;

[0011] The dynamic risk intervention module is configured to: pop up correct operation instructions when an incorrect operation is detected, and start emergency plan simulation;

[0012] The multi-dimensional review and analysis module is configured to visually present operation trajectories, physiological parameter inflection points, and team collaboration breakpoints, and mark improvement priorities.

[0013] Preferably, the construction of the case scenario includes:

[0014] Obtain clinical data based on historical, real-life fetal intrapartum surgery;

[0015] configuring different types of fetal congenital defect parameters based on the clinical data;

[0016] Setting a dynamic change curve of maternal and fetal physiological parameters based on the clinical data, wherein the dynamic change curve is generated by fitting historical surgical data and is used for detecting the erroneous operation;

[0017] A case scenario is constructed based on the fetal congenital defect parameters and the dynamic change curve.

[0018] Preferably, the independent operation simulation includes:

[0019] The anesthesiologist unit is used to simulate airway management, anesthesia depth control and drug dosage adjustment;

[0020] The neonatal doctor unit is used to simulate the resuscitation operation, tracheal intubation and vital sign maintenance of the fetus after delivery;

[0021] The obstetrician unit is used to simulate hysterectomy, fetal exposure and surgical operations;

[0022] The nurse unit is used to simulate instrument delivery, bleeding control and record key time nodes.

[0023] Preferably, the collaborative operation simulation is based on the anesthesiologist unit, the neonatologist unit, the obstetrician unit and the nurse unit, and the collaborative operation simulation at least includes: simulating the time-synchronized operation of the interdisciplinary team; simulating the dynamic decision-making based on the case scenario; and simulating the collaborative process of emergency handling.

[0024] Preferably, the detection of erroneous operation includes:

[0025] Verifying the steps of a standardized operating procedure to identify errors or omissions in the operating sequence, and determining that an incorrect operation has been detected when an error in the operating sequence and / or omission is identified; wherein the standardized operating procedure is based on historical, actual fetal intrapartum surgery;

[0026] monitoring a preset physiological parameter threshold, and determining that an erroneous operation is detected when the simulated maternal and fetal physiological parameters do not meet the physiological parameter threshold;

[0027] Based on the preset sensor comparison of the operation force and position feedback, when the feedback exceeds the limit, it is determined that an incorrect operation has been detected.

[0028] Preferably, the pop-up of the correct operation guide includes:

[0029] The flowchart corresponding to the standardized operation process is displayed in real time, and the key points of the current step are marked; wherein, the flowchart is based on historical and real fetal delivery operations; the operation path prompts are superimposed on the operation area based on augmented reality technology; and the preset voice guidance unit is used to synchronously explain the key points and precautions of the operation.

[0030] Preferably, the initiation of the emergency plan simulation includes a preset hierarchical response mechanism, dynamic adjustment of simulation parameters and generation of alternative operation plans;

[0031] The hierarchical response mechanism triggers different levels of emergency plans based on the severity of the erroneous operation;

[0032] The dynamically adjusted simulation parameters are adjusted based on the erroneous operation, the simulation parameters are used to characterize the situation during the fetal delivery surgery, and the simulation parameters are obtained based on historical and real fetal delivery surgeries;

[0033] The generating of the alternative operation plan is to provide at least two corresponding rescue processes based on the erroneous operation for the team to make a decision.

[0034] Preferably, the team collaboration breakpoints include at least delay breakpoints, conflict breakpoints, imbalance breakpoints and disagreement breakpoints;

[0035] The delay breakpoint is obtained based on the delay of interdisciplinary communication; the delay breakpoint is determined as follows: when the information transmission time exceeds a preset delay threshold, a delay breakpoint is determined to occur;

[0036] The conflict breakpoint is obtained based on the conflict of operation timing; the conflict breakpoint is determined as follows: when the operation steps of different roles overlap or have gaps in time, a conflict breakpoint is determined to occur;

[0037] The imbalance breakpoint is obtained based on the imbalance in resource allocation; the imbalance breakpoint is determined as follows: when equipment, medicine or personnel support is not in place in time, the imbalance breakpoint is determined to occur;

[0038] The disagreement breakpoint is obtained based on the moment when the decision-making disagreement occurs; the disagreement breakpoint is determined as follows: when the team members fail to reach a consensus on the treatment plan, the disagreement breakpoint is determined and the corresponding time node is recorded.

[0039] Preferably, the marking of the improvement priority includes:

[0040] Based on the case scenario, analyzing the impact of each team collaboration breakpoint on maternal and fetal physiological parameters to obtain a physiological parameter inflection point, and calculating the impact weight of each team collaboration breakpoint on maternal and fetal physiological indicators based on the physiological parameter inflection point;

[0041] Matching the teamwork breakpoints with a preset risk matrix based on the impact weights to calculate associated risk levels; wherein the risk matrix is based on historical, real fetal intrapartum surgeries;

[0042] To predict the cumulative effect of uncorrected teamwork breakpoints on surgical outcomes;

[0043] An improvement priority score is generated based on the impact weight and the cumulative impact, and an improvement priority is marked based on the improvement priority score.

[0044] In a second aspect, the present application discloses a fetal intrapartum surgery rehearsal training method based on multimodal simulation, the training method being applicable to the fetal intrapartum surgery rehearsal system based on multimodal simulation as described above, the method comprising:

[0045] S1: Build a hardware foundation using a maternal-fetal physiological simulation chamber and a fetal simulator, wherein the maternal-fetal physiological simulation chamber is used to simulate the maternal-fetal pathophysiological interaction process, and the fetal simulator is used to simulate fetal physical signs;

[0046] S2: Construct case scenarios based on performed intrapartum fetal surgeries;

[0047] S3: Conduct multimodal simulation training, including anesthesiologist simulation training, neonatologist simulation training, obstetrician simulation training, and nurse simulation training, with each simulation training corresponding to independent operation simulation and collaborative operation simulation of different roles;

[0048] S4: When an incorrect operation is detected, the correct operation guide will pop up and the emergency plan simulation will be started;

[0049] S5: Visualize the operation trajectory, physiological parameter inflection points, and team collaboration breakpoints, and mark the improvement priorities.

[0050] Beneficial effects: The fetal delivery surgery rehearsal system and training method based on multimodal simulation in this application effectively solves the problems of lack of maternal and fetal physiological dynamic simulation, insufficient team collaboration mechanism and weak risk prediction ability in traditional training, and constructs an immersive training system with full-process standardization. It significantly improves the safety of high-risk fetal surgical operations, team collaboration efficiency and emergency response capabilities, and provides scientific and efficient technical support for clinical training of fetal surgery during delivery. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without paying any creative work.

[0052] Figure 1 A structural block diagram of a fetal delivery surgery rehearsal system based on multimodal simulation provided in an embodiment of the present application;

[0053] Figure 2 This is a flowchart of a multimodal simulation-based fetal delivery surgery training method provided in an embodiment of the present application. DETAILED DESCRIPTION

[0054] The following is a clear and complete description of the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments of this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0055] In this document, the term "comprising" is intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising..." does not preclude the presence of additional identical elements in the process, method, article, or apparatus that includes the element.

[0056] The first aspect of this embodiment discloses Figure 1 A fetal delivery surgery rehearsal system based on multimodal simulation is shown, which includes a hardware module, a scenario construction module, a multimodal simulation training module, a dynamic risk intervention module, and a multidimensional review analysis module;

[0057] The hardware module includes a maternal-fetal physiological simulation chamber and a fetal simulator. The maternal-fetal physiological simulation chamber is used to simulate the maternal-fetal pathophysiological interaction process, and the fetal simulator is used to simulate fetal physical signs.

[0058] The scenario construction module is configured as follows: constructing case scenarios based on performed intrapartum fetal surgeries;

[0059] The multimodal simulation training module includes an anesthesiologist unit, a neonatologist unit, an obstetrician unit, and a nurse unit. Each unit corresponds to independent operation simulation and collaborative operation simulation for different roles.

[0060] The dynamic risk intervention module is configured to: pop up correct operation instructions when an incorrect operation is detected, and start emergency plan simulation;

[0061] The multi-dimensional review and analysis module is configured to visually present operation trajectories, physiological parameter inflection points, and team collaboration breakpoints, and mark improvement priorities.

[0062] In the specific application of this embodiment, the hardware module serves as the physical foundation. The maternal-fetal physiological simulation chamber and fetal simulator utilize existing simulation equipment to achieve real-time simulation of parameters such as uterine contraction force and placental blood flow resistance, as well as simulated fetal limb movements and surgical tactile feedback. It should be noted that the simulation of fetal delivery surgery rehearsal in this embodiment utilizes existing simulation technologies, such as virtual reality technology.

[0063] Through the above, full-process simulation allows trainees to intuitively experience the logic of interdisciplinary collaboration, improve the intervention rate of erroneous operations, and shorten the team's emergency response time.

[0064] Specifically, the construction of case scenarios includes:

[0065] Obtain clinical data based on historical, real-life fetal intrapartum surgery;

[0066] Configure different types of fetal congenital defect parameters based on clinical data;

[0067] A dynamic change curve of maternal and fetal physiological parameters is set based on clinical data. The dynamic change curve is generated by fitting historical surgical data and is used to detect incorrect operations.

[0068] Construct case scenarios based on fetal congenital defect parameters and dynamic change curves.

[0069] In this specific application, the scenario-building module connects to the hospital's existing data interface to obtain electronic medical records, anesthesia records, and monitor waveform data from historical surgeries. After desensitization, the data is imported into the rehearsal system. Furthermore, parameters for fetal congenital defects are set based on the experience of those skilled in the art. Existing fitting techniques are used to fit the data from multiple similar surgeries. In this example, 30 similar surgeries were selected for fitting.

[0070] Through the above, the construction of case scenarios improves the authenticity of the scenarios based on clinical data and enhances the sense of immersion in the scenarios.

[0071] Specific, independent operation simulation, including:

[0072] The anesthesiologist unit is used to simulate airway management, anesthesia depth control and drug dosage adjustment;

[0073] The neonatal doctor unit is used to simulate the resuscitation operation, endotracheal intubation and maintenance of vital signs after fetal delivery;

[0074] The obstetrician unit is used to simulate hysterectomy, fetal exposure and surgical operations;

[0075] The nurse unit is used to simulate instrument delivery, bleeding control and record key time nodes.

[0076] In the specific application of this embodiment, the anesthesiologist unit, neonatologist unit, obstetrician unit, and nurse unit are configured based on expertise in different fields, and the expertise used in this embodiment is knowledge that is well known to those skilled in the art. For example, if the instrument delivery interface of the nurse unit is set to a 10-second response time, if the delivery is not completed within the time limit, it will be recorded as a nursing cooperation delay.

[0077] Through the above, through independent operation simulation, the pass rate of special operation assessment of each role can be improved and the operation standards can be optimized.

[0078] Specifically, the collaborative operation simulation is based on the anesthesiologist unit, the neonatologist unit, the obstetrician unit and the nurse unit. The collaborative operation simulation at least includes: simulating the time-synchronized operation of the interdisciplinary team; simulating the dynamic decision-making based on the case scenario; simulating the collaborative process of emergency handling.

[0079] In the specific application of this embodiment, the collaborative operation simulation is performed based on the same timeline, requiring strict timing matching. The dynamic decision-making simulation module makes dynamic decisions based on the feedback from the fetal simulator and the dynamic change curve. The emergency response process is simulated based on typical emergency scenarios during pre-set surgery, such as maternal hemorrhage.

[0080] Through the above, collaborative operation simulation can reduce the collaborative errors of key team operations and improve the decision-making consistency in complex scenarios.

[0081] Specifically, the detection of incorrect operations includes:

[0082] Verify the steps of a standardized operating procedure to identify errors or omissions in the operating sequence, and determine that an incorrect operation has been detected when an error in the operating sequence and / or omission is identified; wherein the standardized operating procedure is based on historical, real fetal intrapartum surgery;

[0083] Monitoring preset physiological parameter thresholds, and determining that an erroneous operation is detected when the simulated maternal and fetal physiological parameters do not meet the physiological parameter thresholds;

[0084] Based on the preset sensor comparison of the operation force and position feedback, when the feedback exceeds the limit, it is determined that an incorrect operation has been detected.

[0085] In the specific application of this embodiment, the standardized operation process can be an existing standardized operation process for fetal delivery surgery. Based on the standardized operation process for fetal delivery surgery, the wrong operation and the corresponding level are determined. For example, if the operation force sensor sets a pressure range of 0.1-0.3N for the fetal simulator action, if it exceeds the range, it is determined to be a rough operation.

[0086] Through the above, the automatic detection coverage of operational compliance can be improved through the detection of incorrect operations, and the detection rate of incorrect operations during training is better than the quality of manual inspection.

[0087] Specific, correct operation instructions pop up, including:

[0088] The flowchart corresponding to the standardized operation process is displayed in real time, and the key points of the current step are marked. The flowchart is based on historical and real fetal delivery operations. The operation path prompts are superimposed on the operation area based on augmented reality technology. The preset voice guidance unit is used to simultaneously explain the key points and precautions of the operation.

[0089] It should be noted that this embodiment utilizes existing image processing, augmented reality, and voice guidance technologies to display pop-up instructions for correct operation. In a specific application, this embodiment employs multimodal feedback, including visual, auditory, and tactile feedback. In a simple example, if a nurse unit makes an error in delivering an instrument without verifying the number of instruments, a pop-up animation demonstrates the correct verification process, while simultaneously generating pulsed vibrations on the operating handle to simulate the tactile feedback of a real instrument inventory.

[0090] Through the above, based on the pop-up of correct operation instructions, the error correction response time is shortened and the retention rate of trainees' operation memory is improved.

[0091] Specifically, the initiation of emergency plan simulations includes pre-set hierarchical response mechanisms, dynamic adjustment of simulation parameters, and generation of alternative operation plans;

[0092] The graded response mechanism triggers different levels of emergency response plans based on the severity of the erroneous operation;

[0093] Dynamically adjusting simulation parameters is adjusting simulation parameters based on erroneous operations. The simulation parameters are used to characterize the situation during fetal delivery surgery, and the simulation parameters are obtained based on historical and real fetal delivery surgeries.

[0094] Generate alternative operation plans to provide at least two corresponding rescue processes based on erroneous operations for team decision-making.

[0095] In this specific application, the hierarchical response mechanism categorizes operational errors as minor (Level I), severe (Level II), and fatal (Level III): Level I errors (e.g., delayed instrument delivery) trigger only a text prompt; Level II errors (e.g., incorrect drug injection) initiate a local scene reset (rolling back to the state 30 seconds before the error); and Level III errors (e.g., forced delivery) trigger a full-process emergency plan, simulating chain reactions such as fetal intracranial hemorrhage and maternal cardiac arrest. The dynamic parameter adjustment module automatically modifies simulation variables based on the error type and generates two alternative solutions for the team to choose from.

[0096] Through the above, by launching the emergency plan simulation, the team's familiarity with the emergency plan will be improved and the multi-plan decision-making ability will be strengthened.

[0097] Specifically, team collaboration breakpoints include at least delay breakpoints, conflict breakpoints, imbalance breakpoints, and disagreement breakpoints;

[0098] The delay breakpoint is obtained based on the delay of interdisciplinary communication. The delay breakpoint is determined as follows: when the information transmission time exceeds the preset delay threshold, a delay breakpoint is determined to have occurred.

[0099] Conflict breakpoints are determined based on conflicting operation sequences. Conflict breakpoints are determined when there are overlaps or gaps in the operation steps of different roles.

[0100] Imbalance breakpoints are derived based on imbalances in resource allocation. Imbalance breakpoints are determined when equipment, drugs, or personnel support are not in place in a timely manner.

[0101] The disagreement breakpoint is obtained based on the moment when the decision-making disagreement occurs; the disagreement breakpoint is determined as follows: when team members fail to reach a consensus on the treatment plan, the disagreement breakpoint is determined and the corresponding time node is recorded.

[0102] In this specific application, existing IoT technology is used to collect operational data from various roles, such as the time an anesthesiologist sends medication instructions, the time an obstetrician triggers a surgical step, and the time a nurse confirms the delivery of an instrument. This data is then used to construct a timeline coordinate system. Based on this timeline coordinate system, corresponding delay breakpoints, conflict breakpoints, imbalance breakpoints, and divergence breakpoints are determined. For example, if a neonatal doctor sends an intubation request but receives no response from a nurse within 15 seconds, this is considered a delay breakpoint.

[0103] Through the above, the accuracy of team collaboration breakpoint identification has been improved, thereby providing data reference for optimizing team communication efficiency.

[0104] Specifically, the improved priority marking includes:

[0105] Based on the case scenario, the impact of each team collaboration breakpoint on maternal and fetal physiological parameters was analyzed to obtain the physiological parameter inflection point. Based on the physiological parameter inflection point, the impact weight of each team collaboration breakpoint on maternal and fetal physiological indicators was calculated;

[0106] Based on the impact weights, the team collaboration breakpoints were matched to a pre-defined risk matrix to calculate the associated risk level; the risk matrix was based on historical, real-life intrapartum surgeries;

[0107] To predict the cumulative effect of uncorrected teamwork breakpoints on surgical outcomes;

[0108] An improvement priority score is generated based on the impact weight and the cumulative impact, and the improvement priority is marked based on the improvement priority score.

[0109] It should be noted that this embodiment utilizes existing deep learning technology to construct a labeling model for improving priority labeling. This labeling model determines physiological parameter inflection points based on existing medical knowledge and uses the importance coefficients of the changed maternal and fetal physiological parameters and their corresponding parameter changes to determine the impact weights. This importance coefficient is set based on the experience known to those skilled in the art. Furthermore, the risk level is determined based on the risk matrix and the historical probability of occurrence of team collaboration breakpoints. In this embodiment, the risk matrix is a two-dimensional table, and its behavior impact weights are listed as occurrence probabilities.

[0110] In the specific application of this embodiment, the cumulative impact is determined based on the ideal outcome value without a breakpoint (i.e., the outcome under the standard procedure) and the predicted outcome value with a team collaboration breakpoint, where the outcome value is proportional to the duration of the surgery. Furthermore, the predicted outcome value can be determined using an existing time prediction model, taking into account the time delay caused by the team collaboration breakpoint.

[0111] As a preferred implementation of this embodiment, a weighted sum method is used to calculate the impact weight and cumulative impact based on the team collaboration breakpoints, thereby generating an improvement priority score.

[0112] In a simple example of this embodiment, a delay breakpoint causes a 2% drop in fetal blood oxygen, corresponding to an impact weight coefficient of 0.8. The risk matrix matches this breakpoint to a medium risk level, with a 30% probability of occurrence and a 60% severity of consequence. The time prediction model predicts that if this breakpoint is not corrected, the surgical duration will increase, leading to an increased risk of failure. Ultimately, a weighted summation algorithm (40% impact weight, 30% risk level, and 30% cumulative impact) generates a priority score of 78 (out of 100 in this embodiment), marking this improvement priority as a less urgent improvement.

[0113] The second aspect of this embodiment discloses Figure 2A fetal delivery surgery training method based on multimodal simulation is shown. The training method is applicable to the fetal delivery surgery training system based on multimodal simulation as described above. The method includes:

[0114] S1: The hardware foundation is constructed using a maternal-fetal physiological simulation chamber and a fetal simulator. The maternal-fetal physiological simulation chamber is used to simulate the maternal-fetal pathophysiological interaction process, and the fetal simulator is used to simulate fetal physical signs.

[0115] S2: Construct case scenarios based on performed intrapartum fetal surgeries;

[0116] S3: Conduct multimodal simulation training, including anesthesiologist simulation training, neonatologist simulation training, obstetrician simulation training, and nurse simulation training. Each simulation training corresponds to independent operation simulation and collaborative operation simulation of different roles;

[0117] S4: When an incorrect operation is detected, the correct operation guide will pop up and the emergency plan simulation will be started;

[0118] S5: Visualize the operation trajectory, physiological parameter inflection points, and team collaboration breakpoints, and mark the improvement priorities.

[0119] It should be noted that the multimodal simulation-based fetal intrapartum surgery rehearsal training method of this embodiment corresponds to the aforementioned multimodal simulation-based fetal intrapartum surgery rehearsal system. Therefore, any content not specifically described in the multimodal simulation-based fetal intrapartum surgery rehearsal training method of this embodiment, including but not limited to functional definitions, working principles, and technical effects, can be referred to the description of the aforementioned multimodal simulation-based fetal intrapartum surgery rehearsal system, and will not be repeated in this document.

[0120] In summary, the fetal delivery surgery rehearsal system and training method based on multimodal simulation of this embodiment realizes high-simulation simulation of maternal-fetal pathophysiological interaction process and fetal signs through the collaboration of maternal-fetal physiological simulation cabin and fetal simulator in the hardware module, and combines the restoration of real surgical cases by the scenario construction module to provide an immersive scenario foundation for multimodal simulation training; the multimodal simulation training module supports multi-role independent and collaborative operation simulation, and strengthens the process coordination and dynamic decision-making ability of the interdisciplinary team; the dynamic risk intervention module improves the team's risk management through error operation detection, real-time guidance and emergency plan simulation. The multi-dimensional review and analysis module realizes the traceability of the rehearsal process and the precise positioning of the improvement direction based on the visualization and priority marking of the operation trajectory, physiological parameter inflection points and team collaboration breakpoints; based on the above, it effectively solves the problems of lack of maternal and fetal physiological dynamic simulation, insufficient team collaboration mechanism and weak risk prediction ability in traditional training, builds an immersive training system with full-process standardization, significantly improves the safety of high-risk fetal surgery, team collaboration efficiency and emergency response capabilities, and provides scientific and efficient technical support for the clinical training of intrapartum fetal surgery.

[0121] In the embodiments provided herein, it should be understood that the embodiments described herein can be implemented in hardware, software, firmware, middleware, code, or any appropriate combination thereof. For hardware implementation, the processor can be implemented in one or more of the following units: an application-specific integrated circuit (ASIC), a digital signal processor (DSP), a digital signal processing device (DSPD), a programmable logic device (PLD), a field programmable gate array (FPGA), a processor, a controller, a microcontroller, a microprocessor, other electronic units designed to implement the functions described herein, or a combination thereof. For software implementation, part or all of the processes of the embodiments can be completed by instructing the relevant hardware through a computer program. When implemented, the above program can be stored in a computer-readable storage medium or transmitted as one or more instructions or codes on a computer-readable storage medium. Computer-readable storage media include computer storage media and communication media, wherein the communication media include any medium that facilitates the transmission of a computer program from one place to another. The storage medium can be any available medium that a computer can access. The computer-readable storage medium can include, but is not limited to, RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage media or other magnetic storage devices, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer.

[0122] Finally, it should be noted that the above is only a preferred embodiment of the present application and is not intended to limit the present application. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or make equivalent replacements for some of the technical features therein. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should be included in the scope of protection of the present application.

Claims

1. A fetal delivery surgery training system based on multimodal simulation, characterized in that: The system includes hardware modules, scenario construction modules, multimodal simulation training modules, dynamic risk intervention modules and multi-dimensional review analysis modules; The hardware module includes a maternal-fetal physiological simulation chamber and a fetal simulator, wherein the maternal-fetal physiological simulation chamber is used to simulate the maternal-fetal pathophysiological interaction process, and the fetal simulator is used to simulate fetal physical signs; The scenario construction module is configured to: construct a case scenario based on the performed fetal intrapartum surgery; The multimodal simulation training module includes an anesthesiologist unit, a neonatologist unit, an obstetrician unit, and a nurse unit, each unit corresponding to independent operation simulation and collaborative operation simulation of different roles; The dynamic risk intervention module is configured to: pop up correct operation instructions when an incorrect operation is detected, and start emergency plan simulation; The multi-dimensional review and analysis module is configured to visually present operation trajectories, physiological parameter inflection points, and team collaboration breakpoints, and mark improvement priorities.

2. The fetal delivery surgery training system based on multimodal simulation according to claim 1, characterized in that: The construction of the case scenario includes: Obtain clinical data based on historical, real-life fetal intrapartum surgery; configuring different types of fetal congenital defect parameters based on the clinical data; Setting a dynamic change curve of maternal and fetal physiological parameters based on the clinical data, wherein the dynamic change curve is generated by fitting historical surgical data and is used for detecting the erroneous operation; A case scenario is constructed based on the fetal congenital defect parameters and the dynamic change curve.

3. The fetal delivery surgery training system based on multimodal simulation according to claim 1, characterized in that: The independent operation simulation includes: The anesthesiologist unit is used to simulate airway management, anesthesia depth control and drug dosage adjustment; The neonatal doctor unit is used to simulate the resuscitation operation, tracheal intubation and vital sign maintenance of the fetus after delivery; The obstetrician unit is used to simulate hysterectomy, fetal exposure and surgical operations; The nurse unit is used to simulate instrument delivery, bleeding control and record key time nodes.

4. The fetal delivery surgery training system based on multimodal simulation according to claim 1, characterized in that: The collaborative operation simulation is based on the collaborative operation simulation of the anesthesiologist unit, the neonatologist unit, the obstetrician unit and the nurse unit, and the collaborative operation simulation at least includes: simulating the time-synchronized operation of the interdisciplinary team; simulating the dynamic decision-making based on the case scenario; and simulating the collaborative process of emergency handling.

5. The fetal delivery surgery training system based on multimodal simulation according to claim 3 or 4, characterized in that: The detection of the erroneous operation includes: Verifying the steps of a standardized operating procedure to identify errors or omissions in the operating sequence, and determining that an incorrect operation has been detected when an error in the operating sequence and / or omission is identified; wherein the standardized operating procedure is based on historical, actual fetal intrapartum surgery; monitoring a preset physiological parameter threshold, and determining that an erroneous operation is detected when the simulated maternal and fetal physiological parameters do not meet the physiological parameter threshold; Based on the preset sensor comparison of the operation force and position feedback, when the feedback exceeds the limit, it is determined that an incorrect operation has been detected.

6. The fetal delivery surgery training system based on multimodal simulation according to claim 5, characterized in that: The pop-up of the correct operation guide includes: The flowchart corresponding to the standardized operation process is displayed in real time, and the key points of the current step are marked; wherein, the flowchart is based on historical and real fetal delivery operations; the operation path prompts are superimposed on the operation area based on augmented reality technology; and the preset voice guidance unit is used to synchronously explain the key points and precautions of the operation.

7. The fetal delivery surgery training system based on multimodal simulation according to claim 5, characterized in that: The initiation of the emergency plan simulation, including the preset hierarchical response mechanism, dynamic adjustment of simulation parameters and generation of alternative operation plans; The hierarchical response mechanism triggers different levels of emergency plans based on the severity of the erroneous operation; The dynamically adjusted simulation parameters are adjusted based on the erroneous operation, the simulation parameters are used to characterize the situation during the fetal delivery surgery, and the simulation parameters are obtained based on historical and real fetal delivery surgeries; The generating of the alternative operation plan is to provide at least two corresponding rescue processes based on the erroneous operation for the team to make a decision.

8. The fetal delivery surgery training system based on multimodal simulation according to claim 1, characterized in that: The team collaboration breakpoints include at least delay breakpoints, conflict breakpoints, imbalance breakpoints and disagreement breakpoints; The delay breakpoint is obtained based on the delay of interdisciplinary communication; the delay breakpoint is determined as follows: when the information transmission time exceeds a preset delay threshold, a delay breakpoint is determined to occur; The conflict breakpoint is obtained based on the conflict of operation timing; the conflict breakpoint is determined as follows: when the operation steps of different roles overlap or have gaps in time, a conflict breakpoint is determined to occur; The imbalance breakpoint is obtained based on the imbalance in resource allocation; the imbalance breakpoint is determined as follows: when equipment, medicine or personnel support is not in place in time, the imbalance breakpoint is determined to occur; The disagreement breakpoint is obtained based on the moment when the decision-making disagreement occurs; the disagreement breakpoint is determined as follows: when the team members fail to reach a consensus on the treatment plan, the disagreement breakpoint is determined and the corresponding time node is recorded.

9. The fetal delivery surgery training system based on multimodal simulation according to claim 8, characterized in that: The improvement priority marking includes: Based on the case scenario, analyzing the impact of each team collaboration breakpoint on maternal and fetal physiological parameters to obtain a physiological parameter inflection point, and calculating the impact weight of each team collaboration breakpoint on maternal and fetal physiological indicators based on the physiological parameter inflection point; Matching the teamwork breakpoints with a preset risk matrix based on the impact weights to calculate associated risk levels; wherein the risk matrix is based on historical, real fetal intrapartum surgeries; To predict the cumulative effect of uncorrected teamwork breakpoints on surgical outcomes; An improvement priority score is generated based on the impact weight and the cumulative impact, and an improvement priority is marked based on the improvement priority score.

10. A fetal intrapartum surgery training method based on multimodal simulation, the training method being applicable to the fetal intrapartum surgery training system based on multimodal simulation as claimed in any one of claims 1 to 9, characterized in that: The method includes: S1: Build a hardware foundation using a maternal-fetal physiological simulation chamber and a fetal simulator, wherein the maternal-fetal physiological simulation chamber is used to simulate the maternal-fetal pathophysiological interaction process, and the fetal simulator is used to simulate fetal physical signs; S2: Construct case scenarios based on performed intrapartum fetal surgeries; S3: Conduct multimodal simulation training, including anesthesiologist simulation training, neonatologist simulation training, obstetrician simulation training, and nurse simulation training, with each simulation training corresponding to independent operation simulation and collaborative operation simulation of different roles; S4: When an incorrect operation is detected, the correct operation guide will pop up and the emergency plan simulation will be started; S5: Visualize the operation trajectory, physiological parameter inflection points, and team collaboration breakpoints, and mark the improvement priorities.

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