Surgical planning apparatus, medical system, and storage medium

Abstract

The invention provides operation planning equipment, a medical system and a storage medium. The operation planning equipment comprises a memory and at least one processor, a computer program is stored in the memory, and the at least one processor is configured to implement the following steps when executing the computer program: acquiring skull image data, implanted equipment data and preset redundant information of a patient; determining a preset collision condition according to the preset redundant information; according to the preset collision condition, collision screening is carried out according to implanted equipment data and skull image data to obtain screening information, the screening information is used for indicating whether a screening point set exists in the skull of the patient or not, and the screening point set is used for indicating one or more areas meeting the preset collision condition. According to the operation planning equipment provided by the invention, the patient skull image data and the implantation equipment data are reasonably utilized, and the preset redundant information is combined to carry out collision screening, so that the implantation accuracy is improved.

Description

Technical Field

[0001] The present application relates to the technical field of medical equipment, for example, to surgical planning equipment, medical systems and computer-readable storage media. Background Art

[0002] In some disease treatment surgical plans, medical devices are implanted in the patient's skull. In order to reduce the surgical risks brought by this procedure, it is necessary to plan the implantation area before surgery.

[0003] Before surgery, the patient's imaging data is used to select the implantation area on the skull, so as to perform skull resection or bone grinding to implant the medical device. However, the determination of the implantation area based solely on preoperative imaging data will affect the judgment of the implantation area, and the implantation accuracy is not ideal.

[0004] Based on this, the present application provides a surgical planning device, a medical system and a computer-readable storage medium to solve the problems existing in the above-mentioned technologies. Summary of the invention

[0005] The purpose of the present application is to provide a surgical planning device, a medical system and a computer-readable storage medium to solve the problem of not considering individual differences of patients and affecting implant accuracy.

[0006] The purpose of this application is achieved by the following technical solutions:

[0007] In a first aspect, the present application provides a surgical planning device, the surgical planning device comprising a memory and at least one processor, the memory storing a computer program, and the at least one processor being configured to implement the following steps when executing the computer program:

[0008] Obtain the patient's skull imaging data, implant device data, and preset redundant information;

[0009] Determining a preset collision condition according to the preset redundant information;

[0010] According to the preset collision condition, collision screening is performed based on the implant device data and the skull image data to obtain screening information, wherein the screening information is used to indicate whether there is a screening point set in the patient's skull, and the screening point set is used to indicate one or more areas that meet the preset collision condition.

[0011] The beneficial effect of this technical solution is that in the preoperative stage, by acquiring the patient's skull image data, implant device data and preset redundant information, screening information matching the patient can be obtained to assist the user in formulating a personalized surgical plan for the patient. It first determines the preset collision condition based on the preset redundant information. Then, according to the preset collision condition, the implant device data and the skull image data are subjected to collision screening to obtain screening information. The screening information indicates whether there is a set of screening points in the patient's skull, and these point sets represent areas that may meet the preset collision conditions. This embodiment combines skull image data, implant device data and preset redundant information to perform collision screening to evaluate the safety of the implant device in the patient's skull.

[0012] Compared to over-reliance on the user to determine the implant area only through preoperative imaging data, this embodiment can, on the one hand, obtain screening information for guiding the user to determine the implant area based on the skull structure and implant device data corresponding to the skull imaging data, and then conduct personalized implant planning, which helps to ensure the best fit between the implant device and the patient's skull. On the other hand, by presetting redundant information and the preset collision conditions determined by it, different implant schemes can be obtained for comparison during the surgical planning stage, which helps to assist the user in selecting the best implant location. On the other hand, fully consider the operational errors or positioning accuracy errors that may occur during the implantation process. When determining the implant area, appropriate margins are left by presetting redundant information to deal with possible errors and uncertainties, which helps to improve the accuracy and safety of the implant and reduce the risk of implant area selection.

[0013] In summary, by rationally utilizing the patient's skull imaging data and implant device data, and combining it with preset redundant information for collision screening, the screening information obtained can assist users in providing effective personalized implant plans, helping to improve the safety and accuracy of implant device implantation surgery.

[0014] In some possible implementations, after obtaining the screening information, the at least one processor is configured to implement the following steps when executing the computer program:

[0015] Outputting plan prompt information according to the screening information, wherein the plan prompt information is used to indicate whether the patient meets the skull implantation condition of the implant device.

[0016] The beneficial effect of this technical solution is that the screening information can be used to generate plan prompt information to indicate whether the skull implantation conditions of the implant device are met. Among them, the screening information may include a set of points in the skull that meet the preset collision conditions. On the one hand, the plan prompt information can provide users with detailed information on the feasibility of implanting the implant device into the skull. Based on this information, users can more accurately evaluate whether the patient is suitable for implant device surgery, so as to make scientific and reasonable decisions. On the other hand, the plan prompt information helps users optimize the surgical plan during the surgical planning stage. If there is an area in the patient's skull suitable for implanting the device, the user can choose the best implantation position and angle in a targeted manner to improve the success rate of the operation and the treatment effect on the patient.

[0017] In some possible implementations, the at least one processor is configured to perform collision screening according to the implant device data and the skull image data to obtain screening information in the following manner when executing the computer program:

[0018] Acquire a device three-dimensional model according to the implanted device data, wherein the device three-dimensional model has a relative device upper surface and device lower surface;

[0019] Acquire a three-dimensional skull model of the patient according to the skull image data, wherein the three-dimensional skull model has a relative skull inner surface and a skull outer surface;

[0020] Using the three-dimensional model of the device to perform collision screening in the three-dimensional model of the skull;

[0021] When one or more screening points meeting the preset collision condition are obtained during the collision screening process, a set of position information of each screening point meeting the preset collision condition in the three-dimensional skull model is used as a screening point set;

[0022] When no screening point that meets the preset collision condition is obtained during the collision screening process, empty information is used as the screening information.

[0023] The beneficial effects of this technical solution are: on the one hand, the three-dimensional model of the device and the three-dimensional model of the skull are created based on real data, which can more accurately reflect the real anatomical morphology of the patient's skull. By using a model of real anatomical morphology, the position and relative relationship of the implant device in the skull can be more accurately simulated, so as to better predict potential collision problems during and after implantation. On the other hand, in the three-dimensional model, the relative position and angle of the implant device and the skull can be accurately observed, and the relationship between the implant device and the skull can be more comprehensively evaluated, so as to avoid potential collision problems. On the other hand, by comparing the three-dimensional model of the device with the three-dimensional model of the skull, more accurate collision detection can be achieved. The three-dimensional model provides more information and geometric shapes, making collision detection more accurate and detailed. On the other hand, each patient's skull morphology and implant device parameters are unique. Using the three-dimensional model of the device and the three-dimensional model of the skull for collision screening can achieve personalized surgical planning. According to the specific situation of the patient, the most suitable implantation plan is formulated to avoid conflicts between the device and the skull, improve the success rate of the operation and the treatment effect of the patient.

[0024] In some possible implementations, when one or more regions meeting the preset collision condition are obtained during the collision screening process, the at least one processor is configured to implement the following steps when executing the computer program:

[0025] Acquire a plurality of three-dimensional visualization areas in the three-dimensional skull model, each of the three-dimensional visualization areas being used to display the thickness of a different position of the skull;

[0026] For each of the three-dimensional visualization areas, a visualization collision result of the three-dimensional model of the device in the three-dimensional visualization area is obtained according to the screening point set, and the visualization collision result is used to assist in selecting an implantation position of the implant device.

[0027] The beneficial effect of the technical solution is that when one or more areas that meet the preset collision conditions are obtained during the collision screening process, the three-dimensional skull model can be further processed to obtain multiple three-dimensional visualization areas. Each three-dimensional visualization area is used to display the thickness of the skull at different positions. Then, for each three-dimensional visualization area, the visualization collision result of the three-dimensional model of the device in the area is obtained according to the previously obtained set of screening points. On the one hand, by obtaining multiple three-dimensional visualization areas, the thickness of the skull at different positions can be more intuitively displayed in the three-dimensional skull model, providing users with more comprehensive information, helping to understand the morphological characteristics of the skull at different positions, and helping to better evaluate the adaptability of the implant device. On the other hand, for each three-dimensional visualization area, the visualization result of the implant device in the area is obtained by comparing the three-dimensional model of the device with the set of screening points, so as to intuitively display the relative position relationship between the implant device and the skull, which helps the user to select the best implant position. On the other hand, by performing a three-dimensional visualization display of the skull thickness at different positions and generating the visualization collision results of the device in each area according to the set of screening points, the user is assisted in making personalized surgical plans for the patient. According to the specific situation of the patient, the most suitable implant position can be selected in a targeted manner to ensure the best adaptability of the implant device to the skull.

[0028] In some possible implementations, the visual collision result includes:

[0029] For each area meeting the preset collision condition, a mapping image is generated in the three-dimensional visualization area according to the vertical distance between each point on the upper surface of the implant device and the outer surface of the skull; or,

[0030] For each area that meets the preset collision condition, a mapping image is generated in the three-dimensional visualization area based on the difference between the vertical distance between each point on the upper surface of the device and the outer surface of the skull and the skull thickness corresponding to the screened qualified area.

[0031] The beneficial effects of this technical solution are: on the one hand, by generating a mapping image based on the vertical distance between the upper surface point of the device and the outer surface of the skull, or combining the mapping image of the skull thickness difference, a more detailed and accurate mapping image can be provided, so that the user can understand the spatial relationship between the implant device and the skull through the mapping image, discover potential collision risks, and thus better plan the surgical plan. On the other hand, by combining the mapping image of the skull thickness difference and comprehensively considering the skull thickness, the adaptability of the implant device can be more comprehensively evaluated, which helps users to more comprehensively understand the potential collision problems of the implant device in different areas and make decisions.

[0032] In some possible implementations, the preset redundant information includes a collision redundant distance, and the preset collision condition includes that when the implant device is implanted into the patient's skull and the lower surface of the device is not in contact with the inner surface of the skull, the distance of any point on the upper surface of the device above the outer surface of the skull does not exceed the collision redundant distance;

[0033] The at least one processor is configured to acquire the collision redundancy distance in the following manner when executing the computer program:

[0034] Acquiring disease information of the patient, wherein the disease information is obtained by analyzing the medical record information of the patient;

[0035] Performing a skull implant difficulty assessment of the implant device according to the patient's disease information to obtain an implant difficulty coefficient;

[0036] The collision redundancy distance is obtained according to the implantation difficulty coefficient and the disease information.

[0037] The beneficial effects of this technical solution are: on the one hand, the collision redundancy distance is obtained according to the patient's disease information and implantation difficulty assessment, which reflects personalized treatment. Each patient's condition and skull structure are unique. By determining the collision redundancy distance based on individual conditions, a personalized surgical plan can be tailored for each patient to ensure the best fit between the implant device and the skull and improve the success rate of the operation. On the other hand, the lower surface of the device does not contact the inner surface of the skull, and the distance between the upper surface of the device and the outer surface of the skull is limited, which can prevent the implant device from colliding with the skull and avoid surgical complications and adverse effects. On the other hand, the collision redundancy distance is determined based on the patient's condition and the difficulty of implantation, which makes surgical planning more detailed and safe. Users can fully assess the patient's condition before surgery, avoid unnecessary surgical risks, and improve the safety and success rate of surgery.

[0038] In some possible implementations, the at least one processor is configured to obtain the implantation difficulty coefficient in the following manner when executing the computer program:

[0039] Inputting the disease information into a difficulty assessment model to obtain an implantation difficulty coefficient corresponding to the disease information; and / or,

[0040] When a coefficient selection operation for the disease information is received, the implantation difficulty coefficient corresponding to the disease information is obtained according to the coefficient selection operation.

[0041] The beneficial effects of this technical solution are: on the one hand, by calculating the implantation difficulty coefficient based on the patient's disease information, a personalized assessment of each patient can be achieved. The patient's disease condition and individual characteristic factors will be taken into consideration, thereby obtaining a more accurate implantation difficulty assessment result. On the other hand, by calculating the implantation difficulty coefficient, for patients with high difficulty, users can be reminded to make adequate preparations in advance to avoid potential surgical risks and improve the success rate of the operation.

[0042] In some possible implementations, the at least one processor is configured to acquire the collision redundancy distance in the following manner when executing the computer program:

[0043] Input the disease information into the distance recommendation model to obtain model output data corresponding to the disease information; weight the model output data using the implantation difficulty coefficient as a weight value to obtain a collision redundancy distance; and / or,

[0044] When a distance selection operation for the implantation difficulty coefficient and the disease information is received, a collision redundant distance corresponding to the implantation difficulty coefficient and the disease information is obtained according to the distance selection operation.

[0045] The beneficial effects of this technical solution are: on the one hand, by combining the patient's disease information and the implantation difficulty coefficient, the collision redundancy distance can more accurately reflect the patient's actual situation, avoiding the error that may be caused by the collision redundancy distance determined by universal means. On the other hand, considering the impact of the implantation difficulty coefficient, the collision redundancy distance obtained for patients with greater implantation difficulty is smaller, which can increase the safety of the operation.

[0046] In a second aspect, the present application further provides a surgical planning method, the surgical planning method comprising:

[0047] Obtain the patient's skull imaging data, implant device data, and preset redundant information;

[0048] Determining a preset collision condition according to the preset redundant information;

[0049] According to the preset collision condition, collision screening is performed based on the implant device data and the skull image data to obtain screening information, wherein the screening information is used to indicate whether there is a screening point set in the patient's skull, and the screening point set is used to indicate one or more areas that meet the preset collision condition.

[0050] In some possible implementations, after obtaining the screening information, the method further includes:

[0051] Outputting plan prompt information according to the screening information, wherein the plan prompt information is used to indicate whether the patient meets the skull implantation condition of the implant device.

[0052] In some possible implementations, performing collision screening according to implant device data and skull image data to obtain screening information includes:

[0053] Acquire a device three-dimensional model according to the implanted device data, wherein the device three-dimensional model has a relative device upper surface and device lower surface;

[0054] Acquire a three-dimensional skull model of the patient according to the skull image data, wherein the three-dimensional skull model has a relative skull inner surface and a skull outer surface;

[0055] Using the three-dimensional model of the device to perform collision screening in the three-dimensional model of the skull;

[0056] When one or more screening points meeting the preset collision condition are obtained during the collision screening process, a set of position information of each screening point meeting the preset collision condition in the three-dimensional skull model is used as a screening point set;

[0057] When no screening point that meets the preset collision condition is obtained during the collision screening process, empty information is used as the screening information.

[0058] In some possible implementations, when one or more regions meeting the preset collision condition are obtained during the collision screening process, the method further includes:

[0059] Acquire a plurality of three-dimensional visualization areas in the three-dimensional skull model, each of the three-dimensional visualization areas being used to display the thickness of a different position of the skull;

[0060] For each of the three-dimensional visualization areas, a visualization collision result of the three-dimensional model of the device in the three-dimensional visualization area is obtained according to the screening point set, and the visualization collision result is used to assist in selecting an implantation position of the implant device.

[0061] In some possible implementations, the visual collision result includes:

[0062] For each area meeting the preset collision condition, a mapping image is generated in the three-dimensional visualization area according to the vertical distance between each point on the upper surface of the implant device and the outer surface of the skull; or,

[0063] For each area that meets the preset collision condition, a mapping image is generated in the three-dimensional visualization area based on the difference between the vertical distance between each point on the upper surface of the device and the outer surface of the skull and the skull thickness corresponding to the screened qualified area.

[0064] In some possible implementations, the preset redundant information includes a collision redundant distance, and the preset collision condition includes that when the implant device is implanted into the patient's skull and the lower surface of the device is not in contact with the inner surface of the skull, the distance of any point on the upper surface of the device above the outer surface of the skull does not exceed the collision redundant distance;

[0065] Methods for obtaining the collision redundant distance include:

[0066] Acquiring disease information of the patient, wherein the disease information is obtained by analyzing the medical record information of the patient;

[0067] Performing a skull implant difficulty assessment of the implant device according to the patient's disease information to obtain an implant difficulty coefficient;

[0068] The collision redundancy distance is obtained according to the implantation difficulty coefficient and the disease information.

[0069] In some possible implementations, performing a skull implant difficulty assessment of the implant device according to the patient's disease information to obtain an implant difficulty coefficient includes:

[0070] Inputting the disease information into a difficulty assessment model to obtain an implantation difficulty coefficient corresponding to the disease information; and / or,

[0071] When a coefficient selection operation for the disease information is received, the implantation difficulty coefficient corresponding to the disease information is obtained according to the coefficient selection operation.

[0072] In some possible implementations, the methods for obtaining the collision redundancy distance include:

[0073] Input the disease information into the distance recommendation model to obtain model output data corresponding to the disease information; weight the model output data using the implantation difficulty coefficient as a weight value to obtain a collision redundancy distance; and / or,

[0074] When a distance selection operation for the implantation difficulty coefficient and the disease information is received, a collision redundant distance corresponding to the implantation difficulty coefficient and the disease information is obtained according to the distance selection operation.

[0075] In a third aspect, the present application further provides a medical system, the medical system comprising:

[0076] An implant device, the implant device being used to be implanted into a patient's skull;

[0077] The surgical planning device according to any one of the first aspects.

[0078] In some possible implementations, the implanted device is a pulse generator.

[0079] In a fourth aspect, the present application also provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by at least one processor, it implements the functions of the surgical planning device described in any one of the above-mentioned items, or when the computer program is executed by at least one processor, it implements the steps of the surgical planning method described in any one of the above-mentioned items.

[0080] In a fifth aspect, the present application provides a computer program product, which includes a computer program, and when the computer program is executed by at least one processor, it implements the functions of the surgical planning device described in any one of the above, or when the computer program is executed by at least one processor, it implements the steps of the surgical planning method described in any one of the above. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] The present application is further described below in conjunction with the accompanying drawings and specific implementation methods.

[0082] Figure 1 It is a flowchart of a surgical planning method provided in an embodiment of the present application.

[0083] Figure 2 It is a flowchart of another surgical planning method provided in an embodiment of the present application.

[0084] Figure 3 It is a flow chart of obtaining screening information provided in an embodiment of the present application.

[0085] Figure 4 It is a flow chart of obtaining a visual collision result provided in an embodiment of the present application.

[0086] Figure 5 It is a schematic diagram of a process for obtaining a collision redundancy distance provided in an embodiment of the present application.

[0087] Figure 6 It is a structural block diagram of a surgical planning device provided in an embodiment of the present application.

[0088] Figure 7 It is a structural schematic diagram of a medical system provided in an embodiment of the present application.

[0089] Figure 8 It is a structural diagram of a computer program product provided in an embodiment of the present application. DETAILED DESCRIPTION

[0090] The technical solution in the present application will be described below in conjunction with the drawings and specific implementation methods of the specification of the present application. It should be noted that, under the premise of no conflict, the various implementation methods or technical features described below can be arbitrarily combined to form a new implementation method.

[0091] In the embodiments of the present application, the words "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any implementation or design scheme described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other implementations or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0092] The first, second, etc. descriptions appearing in the embodiments of the present application are only used for illustration and distinction of the description objects. There is no order, nor does it indicate any special limitation on the quantity in the embodiments of the present application, and cannot constitute any limitation on the embodiments of the present application.

[0093] The following is a brief description of the technical field and related terms of the embodiments of the present application.

[0094] Implantable medical systems include implantable neural stimulation systems, implantable cardiac stimulation systems (also known as pacemakers), implantable drug delivery systems (IDDS), and lead switching systems. Examples of implantable neural stimulation systems include deep brain stimulation (DBS), implantable cortical nerve stimulation (CNS), implantable spinal cord stimulation (SCS), implantable sacral nerve stimulation (SNS), and implantable vagus nerve stimulation (VNS).

[0095] The implantable neural electrical stimulation system includes a stimulator implanted in the patient's body (i.e., an implantable neural stimulator) and a programmable device disposed outside the patient's body. In other words, the stimulator is a medical device, or in other words, the medical device includes the stimulator. The relevant neural regulation technology mainly implants electrodes (electrodes, for example, in the form of electrode wires) in specific parts of the tissues of the organism (i.e., target points) through stereotactic surgery, and emits discharge pulses to the target points through the electrodes to regulate the electrical activity and function of the corresponding neural structures and networks, thereby improving symptoms and relieving pain.

[0096] As an example, DBS includes an IPG (Implantable Pulse Generator), an extension wire and an electrode wire, and the IPG is connected to the electrode wire via the extension wire. The IPG is implanted in the patient's body, for example, in the patient's chest or other body parts.

[0097] As another example, DBS includes an IPG and an electrode lead, and the IPG is directly connected to the electrode lead. The IPG is implanted in the patient's head, for example, a groove is made in the patient's skull, and then the IPG is installed in the groove of the skull. In this case, the IPG may not protrude from the outer surface of the skull, or may partially protrude from the outer surface of the skull.

[0098] The IPG responds to the program-controlled instructions sent by the program-controlled device, and relies on sealed batteries and circuits to provide controllable electrical stimulation therapy (or electrical stimulation energy) to the tissues in the body. The IPG delivers one or more controllable specific electrical stimulations to specific areas of the tissues in the body through electrode wires.

[0099] In some embodiments, the extension lead is used in conjunction with the IPG as a transmission medium for electrical stimulation, and transmits the electrical stimulation generated by the IPG to the electrode lead.

[0100] In some embodiments, the electrical stimulation may be delivered in the form of a pulse signal or in the form of a non-pulse signal. For example, the electrical stimulation may be delivered as a signal having various waveform shapes, frequencies, and amplitudes. Thus, the electrical stimulation in the form of a non-pulse signal may be a continuous signal, which may have a sinusoidal waveform or other continuous waveform.

[0101] After receiving the electrical stimulation transmitted by the IPG or the extension wire, the electrode wire delivers the electrical stimulation to a specific area of ​​the tissue in the body through a plurality of electrode contacts. The stimulator is provided with, for example, one or more electrode wires on one side or both sides, and a plurality of electrode contacts are provided on the electrode wire, and the electrode contacts can be arranged uniformly or non-uniformly in the circumferential direction of the electrode wire. As an example, the electrode contacts can be arranged in an array of 4 rows and 3 columns (a total of 12 electrode contacts) in the circumferential direction of the electrode wire. The electrode contacts can include stimulation electrode contacts and / or collection electrode contacts. The electrode contacts can be in the shape of sheets, rings, dots, etc., for example.

[0102] In some embodiments, the stimulated in vivo tissue may be the patient's brain tissue, and the stimulated site may be a specific site of the brain tissue. When the patient's disease type is different, the stimulated site is generally different, and the number of stimulation contacts (single source or multiple sources), the use of one or more (single channel or multiple channels) of specific electrical stimulation, and the stimulation parameters (values) are also different.

[0103] The embodiments of the present application do not limit the types of diseases that can be used, and they can be the types of diseases that deep brain stimulation (DBS), spinal cord stimulation (SCS), sacral nerve stimulation, gastric stimulation, peripheral nerve stimulation, and functional electrical stimulation can be used for. Among them, the types of diseases that DBS can be used to treat or manage include, but are not limited to: spastic diseases (e.g., epilepsy), pain, migraine, mental illness (e.g., major depressive disorder (MDD)), bipolar disorder, anxiety, post-traumatic stress disorder, mild depression, obsessive-compulsive disorder (OCD), behavioral disorders, mood disorders, memory disorders, mental state disorders, movement disorders (e.g., essential tremor or Parkinson's disease), Huntington's disease, Alzheimer's disease, drug addiction, autism or other neurological or psychiatric diseases and injuries.

[0104] In the embodiment of the present application, when the programmable device and the stimulator establish a programmable connection, the programmable device can be used to adjust one or more stimulation parameters of the stimulator (or one or more stimulation parameters of the pulse generator, different stimulation parameters correspond to different electrical stimulations), and the stimulator can also sense the patient's electrophysiological activities to collect electrophysiological signals, and the collected electrophysiological signals can be used to continue to adjust the stimulation parameters of the stimulator, thereby realizing closed-loop control (or adaptive adjustment) of the stimulation parameters.

[0105] The stimulation parameters may include at least one of the following: the electrode contact identification used to deliver electrical stimulation (for example, electrode contact #2 and electrode contact #3), frequency (for example, the number of electrical stimulation pulse signals within a unit time of 1s, in Hz), pulse width (the duration of each pulse, in μs), amplitude (generally expressed in voltage, that is, the intensity of each pulse, in V), timing (for example, it can be continuous or burst, and burst refers to discontinuous timing behavior composed of multiple processes), stimulation mode (including one or more of current mode, voltage mode, timed stimulation mode and cyclic stimulation mode), upper and lower limits controlled by the doctor (the range that can be adjusted by the doctor) and upper and lower limits controlled by the patient (the range that can be adjusted autonomously by the patient).

[0106] In some embodiments, various stimulation parameters of the stimulator can be adjusted in current mode or voltage mode.

[0107] Programmable devices may include doctor programmable devices (i.e. programmable devices used by doctors) and / or patient programmable devices (i.e. programmable devices used by patients). Doctor programmable devices are, for example, tablet computers, laptop computers, desktop computers, mobile phones and other intelligent terminal devices equipped with programmable software. Patient programmable devices are, for example, tablet computers, laptop computers, desktop computers, mobile phones and other intelligent terminal devices equipped with programmable software. Patient programmable devices may also be other electronic devices with programmable functions (e.g., chargers with programmable functions, electrophysiological acquisition devices, etc.).

[0108] The embodiment of the present application does not restrict the data interaction between the doctor's programmable device and the stimulator. When the doctor performs remote programming, the doctor's programmable device can interact with the stimulator through the server and the patient's programmable device. When the doctor performs offline programming with the patient face to face, the doctor's programmable device can interact with the stimulator through the patient's programmable device, and the doctor's programmable device can also interact with the stimulator directly.

[0109] In some embodiments, the patient programmable device may include a host (communicating with a server) and a slave (communicating with a stimulator), and the host and the slave are communicatively connected. Among them, the doctor programmable device can exchange data with the server through a 3G / 4G / 5G network, the server can exchange data with the host through a 3G / 4G / 5G network, the host can exchange data with the slave through a Bluetooth protocol / WIFI protocol / USB protocol, the slave can exchange data with the stimulator through a 401MHz-406MHz working frequency band / 2.4GHz-2.48GHz working frequency band, and the doctor programmable device can directly exchange data with the stimulator through a 401MHz-406MHz working frequency band / 2.4GHz-2.48GHz working frequency band.

[0110] In the related art, doctors use preoperative imaging data and experience to select the implantation area on the patient's skull and perform skull resection or bone grinding to implant the medical device. This process relies too much on the user to determine the implantation area solely through preoperative imaging data. However, the determination of the implantation area solely through preoperative imaging data cannot fully consider the individual differences of patients, which will affect the judgment of the implantation area and the accuracy of the implantation is not ideal. Especially in the actual situation where the implant device has a certain volume, the implant device in the skull will collide with or excessively contact the skull and surrounding tissues during the implantation process, affecting the overall implantation effect and accuracy. The user mentioned in this application refers to the person who plans the implantation of the implant device for the patient, which can be a doctor, scientific researcher, etc., and this application does not limit it.

[0111] Based on this, the present application provides surgical planning equipment, a medical system, a computer-readable storage medium and a computer program product. The technical solution of the present application calculates the implant position based on collision screening and collision redundancy, and comprehensively considers the patient's own skull imaging data, the device data to be implanted and the pre-set redundant information to obtain screening information, thereby improving the above-mentioned related technologies.

[0112] The following will first describe the surgical planning method for cranial implantation, and then describe the surgical planning device. It should be noted that the description order of the following embodiments is not intended to limit the preferred order of the embodiments.

[0113] Method embodiment.

[0114] See also Figure 1 , Figure 1 It is a flowchart of a surgical planning method provided in an embodiment of the present application.

[0115] The surgical planning method comprises:

[0116] Step S101: Acquire the patient's skull image data, implant device data, and preset redundant information;

[0117] Step S102: determining a preset collision condition according to the preset redundant information;

[0118] Step S103: According to the preset collision condition, collision screening is performed based on the implant device data and the skull image data to obtain screening information; the screening information is used to indicate whether there is a screening point set in the skull of the patient, and the screening point set is used to indicate one or more areas that meet the preset collision condition. The embodiment of the present application does not limit the number of screening points in the screening point set, which may be 1, 10, 100, 1000, 10000, etc.

[0119] Among them, skull image data is, for example, anatomical parameters obtained from images generated by X-rays, magnetic resonance imaging (MRI), and computed tomography (CT), which are used to indicate the skull contour, skull thickness, etc. Taking CT images as an example, a CT image of the skull is a series of two-dimensional slice images used to present different anatomical structures of the patient's skull. Each slice image has its corresponding pixel value, which represents the tissue density at that location and can be represented and stored in the form of numerical values.

[0120] The preset redundant information may be a medical standard or data derived from the doctor's experience, which is used to set the redundancy or safety margin during the implantation process, aiming to ensure the best fit between the implant device and the patient's skull during the operation and the safety of the operation. The preset redundant information may include a preset redundant distance, a preset redundant volume, etc. The preset redundant distance can be used to indicate the distance value between the implant device and the skull surface when implanted into the skull, and the preset redundant volume can be used to indicate the volume value between the implant device and the skull surface after implantation.

[0121] The preset collision condition can be set according to the preset redundant information. The preset collision condition can represent one or more of the minimum distance between the implant device and the upper surface of the skull after the implant device is implanted into the skull, the implant angle or the redundant volume, so as to avoid collision between the implant device and the skull during the implantation process, thereby ensuring the safety of the surgical process and the aesthetic appearance of the patient after the implant device is implanted into the skull. After the implant device is implanted into the patient's skull, it will generate tension on the scalp. The area that meets the preset collision condition is selected as the implant area of ​​the implant device. Taking into account the preset redundant information, it is beneficial to skin repair and tissue regeneration in wound healing, thereby reducing scar formation and improving the aesthetic appearance of the wound.

[0122] The implant device data includes, for example, the size, shape, model, and material of the implant device, including data on the parameters and features of the implant device, such as 3×1×6.2 mm, IPG-2345A, and the like.

[0123] Thus, in the preoperative stage, by acquiring the patient's skull image data, implant device data and preset redundant information, screening information matching the patient can be obtained to assist the user in formulating a personalized surgical plan for the patient. It first determines the preset collision condition based on the preset redundant information. Subsequently, according to the preset collision condition, the implant device data and the skull image data are subjected to collision screening to obtain screening information. The screening information indicates whether there is a set of screening points in the patient's skull, and these point sets represent areas that may meet the preset collision conditions. This embodiment combines skull image data, implant device data and preset redundant information to perform collision screening to evaluate the safety of the implant device in the patient's skull.

[0124] Compared to over-reliance on the user to determine the implant area only through preoperative image data, this embodiment can, on the one hand, obtain screening information for guiding the user to determine the implant area based on the skull structure and implant device data corresponding to the skull image data, and then perform personalized implant planning, which helps to ensure the best fit between the implant device and the patient's skull. On the other hand, by presetting redundant information and the preset collision conditions determined by it, different implant plans can be obtained for comparison during the surgical planning stage, which helps to assist the user in selecting the best implant location.

[0125] In summary, by rationally utilizing the patient's skull imaging data and implant device data, and combining it with preset redundant information for collision screening, the screening information obtained can assist users in providing effective personalized implant plans, helping to improve the safety and accuracy of implant device implantation surgery.

[0126] As an example, suppose that the user needs to perform a skull implant surgery on a 30-year-old male patient A with depression. First, the patient's skull CT scan image data (skull image data) is obtained. At the same time, a suitable pulse generator is selected as the implant device according to the patient's specific situation. According to user experience, preset redundant information is set. Since the male skull is thicker, stronger and stronger than the female skull, and the bone density is higher than that of the elderly, the bone sutures may not be completely closed, so the preset redundant information is set to a skull gap of 0.5 mm, and the preset collision condition is determined to leave at least 0.5 mm gap between the implant device and each surface of the skull after the implant device is implanted into the skull. Collision screening is performed under the preset collision conditions based on the implant device data and the skull image data. When there is a 1# area that meets the preset collision condition, the screening information can indicate that there is a set of screening points in the skull, and the distribution and position of the screening points represent the collision-free area between the implant device and the skull. The user can determine the implant position and device parameters based on the screening information to ensure the safety and effectiveness of the implant device during and after implantation.

[0127] See also Figure 2 , Figure 2 It is a flowchart of another surgical planning method provided in an embodiment of the present application.

[0128] In some embodiments, after obtaining the screening information (i.e. after step S103), the method may further include:

[0129] Step S104: outputting plan prompt information according to the screening information, wherein the plan prompt information is used to indicate whether the patient meets the skull implantation conditions of the implant device.

[0130] Thus, the screening information can be used to generate planning prompt information to indicate whether the skull implantation conditions of the implant device are met. The screening information may include a set of points (ie, regions) in the skull that meet the preset collision conditions.

[0131] On the one hand, the planning prompt information can provide users with detailed information on the feasibility of implanting the device into the skull. Based on this information, users can more accurately assess whether the patient is suitable for device implant surgery, thereby making scientific and reasonable decisions. On the other hand, the planning prompt information helps users optimize the surgical plan during the surgical planning stage. If there is an area in the patient's skull that is suitable for implanting the device, the user can specifically select the best implantation position and angle to improve the success rate of the surgery and the treatment effect on the patient.

[0132] The plan prompt information can be obtained by using the user's display device and displayed to the user. The display device is, for example, a tablet, mobile phone, notebook or desktop computer, and the plan prompt information is displayed by using the display device in the form of a combination of one or more of a pop-up window, voice information, and picture information.

[0133] As an example, when the screening information indicates that the screening point set does not exist in the skull of patient A, a pop-up window is pushed to the doctor through the doctor's tablet, and the pop-up window displays the information: Patient A does not meet the preset collision conditions for skull implantation, please judge carefully.

[0134] As another example, when the screening information indicates that there is a set of screening points in the skull of patient B, a voice message is pushed to the doctor via a laptop computer. The voice message is: Patient B meets the preset collision conditions for skull implantation, and the set of screening points that meets the preset collision conditions has been sent to your mailbox in the form of a picture. Please check.

[0135] See also Figure 3 , Figure 3 It is a flow chart of obtaining screening information provided in an embodiment of the present application.

[0136] In some embodiments, performing collision screening based on implant device data and skull image data to obtain screening information (i.e., step S103) may include:

[0137] Step S201: acquiring a three-dimensional model of the device according to the implanted device data, wherein the three-dimensional model of the device has a relative upper device surface and a lower device surface;

[0138] Step S202: acquiring a three-dimensional skull model of the patient according to the skull image data, wherein the three-dimensional skull model has a relative inner skull surface and an outer skull surface;

[0139] Step S203: using the three-dimensional model of the device to perform collision screening in the three-dimensional model of the skull;

[0140] Step S204: when one or more screening points meeting the preset collision condition are obtained during the collision screening process, a set of position information of each screening point meeting the preset collision condition in the three-dimensional skull model is used as a screening point set;

[0141] Step S205: when no screening point that meets the preset collision condition is obtained during the collision screening process, empty information is used as the screening information.

[0142] Among them, the position information can be the specific spatial position coordinates of the screening points that meet the preset collision conditions in the three-dimensional model of the skull. The coordinates can be expressed using a three-dimensional coordinate system, for example, in a Cartesian coordinate system, the X, Y and Z axes are used to locate the position of each screening point in three-dimensional space. Compared with two-dimensional imaging data (CT and MRI images) that can only provide cross-sectional information, the three-dimensional model can provide complete three-dimensional spatial information. Therefore, on the one hand, the three-dimensional model of the device and the three-dimensional model of the skull are created based on real patient data, which can more accurately reflect the real anatomical morphology of the patient's skull. By using a model of real anatomical morphology, the position and relative relationship of the implanted device in the skull can be more accurately simulated, so as to better predict potential collision problems during and after implantation. On the other hand, in the three-dimensional model, the relative position and angle of the implanted device and the skull can be accurately observed, and the relationship between the implanted device and the skull can be more comprehensively evaluated, so as to avoid potential collision problems. On the other hand, by comparing the three-dimensional model of the device with the three-dimensional model of the skull, more accurate collision detection can be achieved. The three-dimensional model provides more information and geometric shapes, making collision detection more accurate and detailed. On the other hand, each patient's skull morphology and implant device parameters are unique. Using the device 3D model and the skull 3D model for collision screening can achieve personalized surgical planning. According to the patient's specific situation, the most appropriate implant plan can be formulated to avoid conflicts between the device and the skull, thereby improving the success rate of the operation and the patient's treatment effect.

[0143] This application does not limit the process and method of obtaining a three-dimensional model (device three-dimensional model, skull three-dimensional model) based on two-dimensional data (implant device data, skull image data). As an example, the two-dimensional data is two-dimensional image data obtained by imaging technology such as MRI scanning, and the process includes:

[0144] During the data acquisition process, MRI scanning imaging technology is used to obtain two-dimensional image data of the patient's head. The two-dimensional image data is multiple tomographic images of cross sections of the patient's body, and each tomographic image represents the tissue and structure information of the patient at a specific location.

[0145] The image preprocessing process performs one or more of the following preprocessing steps on the two-dimensional image data: noise removal, image enhancement, and image registration.

[0146] The 3D reconstruction process uses computer image processing algorithms to perform 3D reconstruction on the pre-processed 2D image data. For example, voxel interpolation, edge detection, surface reconstruction, etc. are used to restore the shape and structure of the 3D object based on the 2D image data. Finally, a 3D model of the patient will be obtained to represent the tissues, organs or other structures in the patient's body and provide complete 3D spatial information.

[0147] During the visualization and post-processing process, the generated 3D model is visualized to present the patient’s 3D structure on the computer screen.

[0148] Through the above steps, the patient's three-dimensional model can be obtained from the two-dimensional image data. These models can be used for medical diagnosis, surgical planning, medical research, etc. Compared with two-dimensional image data, the three-dimensional model provides richer and more comprehensive anatomical information, enabling doctors and researchers to better understand the patient's anatomical structure and make more accurate decisions.

[0149] As an example, patient A needs to undergo skull implant surgery. The user collects the patient's implant device data and skull imaging data, and uses a collision screening method to determine the optimal implant position and angle to ensure the best fit between the implant device and the patient's skull, thereby improving the success rate and safety of the surgery.

[0150] The user determines the length, width and height of the pulse generator according to the model of the pulse generator to be implanted in the patient's skull, and generates a three-dimensional model of the device. The three-dimensional model of the device has geometric shape information on the upper and lower surfaces of the device, and can simulate the actual pulse generator more realistically. At the same time, a three-dimensional skull model is generated based on the skull imaging data obtained by CT of the patient. The three-dimensional skull model has geometric shape information on the inner and outer surfaces of the skull. The user can use the three-dimensional model of the device to perform collision screening in the three-dimensional skull model, and move the three-dimensional model of the pulse generator in the three-dimensional skull model to find areas where there are no collision points and potential conflicts. By simulating the implantation process of the device in the three-dimensional skull model, it is ensured that there is no conflict between the device and the skull.

[0151] During the collision screening process, it is detected whether there are multiple adjacent points that meet the collision screening conditions to obtain the collision screening results. The screening points can form an area that meets the collision screening conditions, that is, the area that meets the collision screening conditions can be understood as a continuous area similar to a shape or boundary composed of screening points that meet the conditions. Specifically, by calculating the distance between the three-dimensional model of the device and the skull surface, it can be determined whether there is a collision. If there are areas that do not collide with the skull surface, these areas can be marked as areas that meet the collision screening conditions, and it can be considered that these areas can keep the implanted device at a sufficient distance from the skull surface.

[0152] In this example, based on the collision screening results, it was found that there was an implant area in the patient's skull that met the preset collision conditions. This area was located in the left area of ​​the parietal bone of the skull. The user developed a personalized surgical plan based on this area and performed a skull implant surgery of the pulse generator, which improved the safety and success rate of the surgery.

[0153] In a specific application, in addition to forming a continuous area by detecting points that meet the collision screening conditions, the specific implementation of collision screening can also be:

[0154] The skull 3D model and the device 3D model are represented using voxel or mesh methods. The collision between the voxels or meshes of the skull 3D model and the device 3D model can be detected to more accurately obtain the area that meets the collision screening conditions. Voxels and meshes are used to discretize objects in three-dimensional space, respectively. Alternatively, a collision screening operation is performed between the skull 3D model and the device 3D model using a surface topology method, and the area that meets the collision screening conditions can be identified by comparing the surface topology structures of the two models. For example, the curvature difference or geometric change between the skull 3D model and the device 3D model can be detected to determine whether there is a collision to obtain the area that meets the collision screening conditions.

[0155] See also Figure 4 , Figure 4 It is a flow chart of obtaining a visual collision result provided in an embodiment of the present application.

[0156] In some embodiments, when one or more regions meeting the preset collision condition are obtained during the collision screening process, the method may further include:

[0157] Step S105: acquiring a plurality of three-dimensional visualization regions in the three-dimensional skull model, each of the three-dimensional visualization regions being used to display the thickness of a different position of the skull;

[0158] Step S106: For each of the three-dimensional visualization areas, a visualization collision result of the three-dimensional model of the device in the three-dimensional visualization area is obtained according to the screening point set, and the visualization collision result is used to assist in selecting an implantation position of the implant device.

[0159] Obtain multiple three-dimensional visualization areas in the three-dimensional model of the skull, and each of the three-dimensional visualization areas can be used to display the thickness of different positions of the skull by means of a skull thickness distribution heat map or a skull volume rendering. Taking the means of the skull thickness distribution heat map as an example, the thickness information of the skull at different positions can be measured by processing the patient's skull imaging data (such as CT or MRI images), and visualized in the form of a heat map, where the color represents the value of different thicknesses, and a gradient color is usually used to represent the range of different thicknesses, and the greater the thickness, the darker the color. Then the heat map data is matched with the three-dimensional model of the skull, and the corresponding position of the three-dimensional model of the skull is colored according to the heat map data to show the thickness of different positions of the skull. The depth of color can be described by the hue, brightness and saturation of the color, and this embodiment does not limit it.

[0160] By displaying the skull thickness distribution heat map in 3D, users can more intuitively understand the thickness of the patient's skull, which is of great significance for surgical planning, disease assessment, and the study of changes in the skull anatomical structure. At the same time, 3D visualization can also help users better understand the current patient's skull structure and more accurately locate and operate during surgery, thereby improving the safety and success rate of surgery.

[0161] Similarly, the visual collision result of the three-dimensional model of the device in the three-dimensional visualization area is obtained based on the set of filtered points. The areas with collision and the areas without collision can be distinguished by different coloring, or the non-collision area can be colored according to the distance between the three-dimensional model of the device and the outer surface of the skull, the inner surface of the skull or the reference surface of the skull. Generally speaking, the closer the distance, the darker the coloring. The reference surface is a surface obtained by path offsetting the outer surface of the skull at a preset distance in the direction away from the brain tissue. The user can pre-set a reference surface, which is an imaginary surface located on the outside of the skull at a preset distance from the outer surface of the skull. It can be understood that path offset means: translating each point on the outer surface of the skull outward by a given distance along the normal direction at that point to obtain the corresponding point. The new surface formed by all the points obtained by the above path offset is the reference surface mentioned above.

[0162] Thus, when one or more regions that meet the preset collision conditions are obtained during the collision screening process, the skull three-dimensional model can be further processed to obtain multiple three-dimensional visualization regions. Each three-dimensional visualization region is used to display the thickness of the skull at different positions. Then, for each three-dimensional visualization region, the visualization collision result of the device three-dimensional model in the region is obtained according to the previously obtained screening point set. On the one hand, by obtaining multiple three-dimensional visualization regions, the skull thickness at different positions can be more intuitively displayed in the skull three-dimensional model, providing users with more comprehensive information, helping to understand the morphological characteristics of the skull at different positions, and helping to better evaluate the adaptability of the implant device. On the other hand, for each three-dimensional visualization region, the visualization result of the implant device in the region is obtained by comparing the device three-dimensional model with the screening point set to intuitively display the relative position relationship between the implant device and the skull, which helps the user select the best implant position. On the other hand, by performing a three-dimensional visualization display of the skull thickness at different positions and generating the visualization collision results of the device in each region according to the screening point set, the user is assisted in making personalized surgical plans for the patient. According to the specific situation of the patient, the most suitable implant position can be selected in a targeted manner to ensure the best adaptability of the implant device to the skull.

[0163] In some embodiments, the visualization of the collision result may include:

[0164] For each area meeting the preset collision condition, a mapping image is generated in the three-dimensional visualization area according to the vertical distance between each point on the upper surface of the implant device and the outer surface of the skull; or,

[0165] For each area that meets the preset collision condition, a mapping image is generated in the three-dimensional visualization area based on the difference between the vertical distance between each point on the upper surface of the device and the outer surface of the skull and the skull thickness corresponding to the screened qualified area.

[0166] The visual collision result is a mapping image generated for each area that meets the preset collision conditions. These mapping images can be generated based on the vertical distance between the upper surface point of the implanted device and the outer surface of the skull or the vertical distance between the upper surface point of the device and the outer surface of the skull and the difference between the skull thickness corresponding to the screened qualified area. The area that meets the preset collision conditions can be colored according to the obtained difference or distance value to obtain a mapping image.

[0167] Therefore, on the one hand, by generating a mapping image based on the vertical distance between the upper surface point of the device and the outer surface of the skull, or combining the mapping image of the skull thickness difference, a more detailed and accurate mapping image can be provided, so that the user can understand the spatial relationship between the implant device and the skull through the mapping image, discover potential collision risks, and thus better plan the surgical plan. On the other hand, by combining the mapping image of the skull thickness difference and comprehensively considering the skull thickness, the adaptability of the implant device can be more comprehensively evaluated, which helps users to more comprehensively understand the potential collision problems of the implant device in different areas and make decisions.

[0168] As an example, performing collision screening in the three-dimensional skull model using the three-dimensional model of the device may include:

[0169] The device 3D model is placed (horizontally or longitudinally) in the skull 3D model with the lower surface of the device in contact with the inner surface of the skull. The position of the device 3D model is moved to simulate different implantation positions to determine whether the device 3D model collides with the skull 3D model.

[0170] At the same time, the distance between the spatial position of the device three-dimensional model in the three-dimensional skull model and the position of its projection point on the inner surface of the skull is calculated to generate a distance distribution heat map.

[0171] See also Figure 5 , Figure 5 It is a schematic diagram of a process for obtaining a collision redundancy distance provided in an embodiment of the present application.

[0172] In some embodiments, the preset redundant information may include a collision redundant distance, and the preset collision condition includes that when the implant device is implanted into the patient's skull and the lower surface of the device is not in contact with the inner surface of the skull, the distance of any point on the upper surface of the device above the outer surface of the skull does not exceed the collision redundant distance. This embodiment does not limit the collision redundant distance, which is, for example, 1 mm, 1.1 mm, or 1.121 mm.

[0173] like Figure 5 As shown, the method of obtaining the collision redundant distance may also include:

[0174] Step S301: Acquire the patient's disease information, where the disease information is obtained by analyzing the patient's medical record information;

[0175] Step S302: evaluating the difficulty of implanting the implant device into the skull according to the patient's disease information to obtain an implant difficulty coefficient;

[0176] Step S303: Obtaining a collision redundancy distance according to the implantation difficulty coefficient and the disease information.

[0177] Therefore, on the one hand, obtaining the collision redundancy distance based on the patient's disease information and implant difficulty assessment reflects personalized treatment. Each patient's condition and skull structure are unique. By determining the collision redundancy distance based on individual conditions, a personalized surgical plan can be tailored for each patient to ensure the best fit between the implant device and the skull and improve the success rate of the operation. On the other hand, the lower surface of the device does not contact the inner surface of the skull, and the distance that the upper surface of the device is higher than the outer surface of the skull is limited, which can prevent the implant device from colliding with the skull and avoid surgical complications and adverse effects. On the other hand, the collision redundancy distance is determined based on the patient's condition and the difficulty of implantation, which makes surgical planning more detailed and safe. Users can fully assess the patient's condition before surgery, avoid unnecessary surgical risks, and improve the safety and success rate of surgery.

[0178] Disease information is obtained by analyzing the patient's medical record information. This application does not limit the process of obtaining disease information. The process is, for example, to digitize the patient's medical record information, including symptom descriptions, physical sign data, laboratory test results, imaging examination reports, and doctor information. The above data can come from the hospital's electronic medical record system, imaging database or other health information system. Data preprocessing of the above data may include data cleaning, standardization and format conversion to ensure data consistency and availability. Feature data is extracted from the data center by means of cluster analysis, etc. Feature data is data describing disease information, which may include the frequency of symptoms, laboratory test values, imaging indicators, doctor's treatment level, etc. According to the feature data, an appropriate machine learning model or deep learning model is established. The established model can be a classification model or a clustering model. The model is trained using labeled sample data so that it can learn the association and pattern between different features and diseases. After the model training is completed, the new patient data is input into the model for automatic analysis. The model will automatically output the corresponding disease information based on the patient's medical record information.

[0179] In some embodiments, the preset redundant information may also include a collision redundant volume. The collision redundant volume refers to the redundant volume between the device volume and the internal space of the skull taken into account during the implantation process of the implant device. The redundant volume is to ensure the compatibility of the implant device with the patient's skull and avoid collision between the device and the tissue structure in the skull.

[0180] As an example, patient A needs to have an implant device implanted in the skull. The shape of the implant device is a cylindrical shape. During the surgery planning stage, the user obtains a 3D model of the patient's skull through skull imaging data. The 3D model of the implant device is obtained and collision screening is performed in the skull model.

[0181] During collision screening, in addition to considering the vertical distance between the device surface and the outer surface of the skull, we will also check whether the device volume overlaps with the internal space of the skull, that is, the collision redundant volume. If the device volume has enough redundant space in the skull, then this area is considered to meet the preset collision conditions and can be selected as the implant location. On the contrary, if the device volume overlaps with the internal tissue structure of the skull, this area will be excluded because the implanted device may collide with the internal structure and increase the risk of surgery.

[0182] In some embodiments, the step of performing a skull implant difficulty assessment of the implant device according to the patient's disease information to obtain an implant difficulty coefficient (i.e., step S302) may include:

[0183] Inputting the disease information into a difficulty assessment model to obtain an implantation difficulty coefficient corresponding to the disease information; and / or,

[0184] When a coefficient selection operation for the disease information is received, the implantation difficulty coefficient corresponding to the disease information is obtained according to the coefficient selection operation.

[0185] Therefore, on the one hand, by calculating the implant difficulty coefficient based on the patient's disease information, a personalized assessment of each patient can be achieved. The patient's disease condition and individual characteristic factors will be taken into consideration, resulting in a more accurate implant difficulty assessment result. On the other hand, by calculating the implant difficulty coefficient, for patients with high difficulty, users can be reminded to make adequate preparations in advance to avoid potential surgical risks and improve the success rate of the operation.

[0186] As an example, a pre-trained difficulty assessment model may be used to obtain the implantation difficulty coefficient. Similarly, the distance recommendation model mentioned below may also be pre-trained.

[0187] The following describes the training process of the difficulty assessment model. The training process of the distance recommendation model is similar and will not be described in detail.

[0188] The training process of the difficulty assessment model includes: obtaining a training set, the training set includes a plurality of training data, each of the training data includes disease information of a sample object and labeled data of the implantation difficulty coefficient of the sample object; for each training data in the training set, performing the following processing:

[0189] The disease information of the sample object in the training data is input into a preset deep learning model to obtain the predicted data of the sample object; based on the predicted data and labeled data of the sample object, the model parameters of the deep learning model are updated; it is detected whether the preset training end condition is met; if so, the trained deep learning model is used as the classification model; if not, the deep learning model is continued to be trained using the next training data.

[0190] The present application does not limit the preset training end conditions, which may be, for example, the number of training times reaches a preset number (the preset number of times may be, for example, 1 time, 3 times, 10 times, 100 times, 1000 times, 10000 times, etc.), or the training data in the training set has completed one or more trainings, or the total loss value obtained from this training is not greater than the preset loss value.

[0191] In some embodiments, the method of obtaining the collision redundancy distance includes:

[0192] Input the disease information into the distance recommendation model to obtain model output data corresponding to the disease information; weight the model output data using the implantation difficulty coefficient as a weight value to obtain a collision redundancy distance; and / or,

[0193] When a distance selection operation for the implantation difficulty coefficient and the disease information is received, a collision redundant distance corresponding to the implantation difficulty coefficient and the disease information is obtained according to the distance selection operation.

[0194] Therefore, on the one hand, by combining the patient's disease information and the implantation difficulty coefficient, the collision redundancy distance can more accurately reflect the patient's actual situation, avoiding the error caused by the collision redundancy distance determined by universal means. On the other hand, considering the impact of the implantation difficulty coefficient, the collision redundancy distance obtained for patients with greater implantation difficulty is smaller, which can increase the safety of the operation.

[0195] As an example, a pulse generator may need to be implanted in the skull to treat epilepsy.

[0196] Obtain disease information, including: epilepsy type is partial focal epilepsy, imaging examination shows that the abnormal electrical activity area of ​​the brain is located in the left temporal lobe. Determine the difficulty of the operation based on factors such as the patient's age, the severity of the disease in the disease information, and the attending physician's familiarity with the disease type. Assume that the patient's implant difficulty coefficient is 0.9. Use the implant difficulty coefficient to weight the model output data to obtain the collision redundancy distance.

[0197] Assuming that the output data of the distance recommendation model is 3.1mm, multiply the model output data and the implant difficulty coefficient to obtain the weighted distance: 3.1mm×0.9=2.79mm. According to the weighted distance, the redundant distance between the implant device and the skull structure is 2.79mm, which is used to represent the safe distance between the implant device and the surrounding skull structure. In practical applications, different collision redundant distances can be calculated based on the disease information and implant difficulty coefficient of different patients, so as to provide personalized surgical planning for each patient.

[0198] In a specific application scenario, the present application embodiment further provides a surgical planning method, including:

[0199] Acquiring the patient's skull image data, implant device data, and preset redundant information; the preset redundant information includes a collision redundant distance;

[0200] Determine a preset collision condition according to the preset redundant information; the preset collision condition includes that when the implant device is implanted into the patient's skull and the lower surface of the device is not in contact with the inner surface of the skull, the distance of any point on the upper surface of the device above the outer surface of the skull does not exceed the collision redundant distance;

[0201] Acquire a device three-dimensional model according to the implanted device data, wherein the device three-dimensional model has a relative device upper surface and device lower surface;

[0202] Acquire a three-dimensional skull model of the patient according to the skull image data, wherein the three-dimensional skull model has a relative inner skull surface and an outer skull surface; and perform collision screening in the three-dimensional skull model using the three-dimensional model of the device;

[0203] When one or more screening points meeting the preset collision condition are obtained during the collision screening process, a set of position information of each screening point meeting the preset collision condition in the three-dimensional skull model is used as a screening point set;

[0204] When no screening point that meets the preset collision condition is obtained during the collision screening process, empty information is used as the screening information;

[0205] Outputting plan prompt information according to the screening information, wherein the plan prompt information is used to indicate whether the patient meets the skull implantation condition of the implant device.

[0206] When one or more regions meeting the preset collision conditions are obtained during the collision screening process, a plurality of three-dimensional visualization regions are obtained in the three-dimensional skull model, each of the three-dimensional visualization regions being used to display the thickness of different positions of the skull;

[0207] For each of the three-dimensional visualization areas, a visualization collision result of the three-dimensional model of the device in the three-dimensional visualization area is obtained according to the screening point set, and the visualization collision result is used to assist in selecting an implantation position of the implant device.

[0208] The visual collision result includes:

[0209] For each area meeting the preset collision condition, a mapping image is generated in the three-dimensional visualization area according to the vertical distance between each point on the upper surface of the implant device and the outer surface of the skull; or,

[0210] For each area that meets the preset collision condition, a mapping image is generated in the three-dimensional visualization area based on the difference between the vertical distance between each point on the upper surface of the device and the outer surface of the skull and the skull thickness corresponding to the screened qualified area.

[0211] Methods for obtaining the collision redundant distance include:

[0212] Acquiring disease information of the patient, wherein the disease information is obtained by analyzing the medical record information of the patient;

[0213] Inputting the disease information into a difficulty assessment model to obtain an implantation difficulty coefficient corresponding to the disease information; and / or,

[0214] When a coefficient selection operation for the disease information is received, obtaining an implantation difficulty coefficient corresponding to the disease information according to the coefficient selection operation;

[0215] Input the disease information into the distance recommendation model to obtain model output data corresponding to the disease information; weight the model output data using the implantation difficulty coefficient as a weight value to obtain a collision redundancy distance; and / or,

[0216] When a distance selection operation for the implantation difficulty coefficient and the disease information is received, a collision redundant distance corresponding to the implantation difficulty coefficient and the disease information is obtained according to the distance selection operation.

[0217] Device Embodiments.

[0218] The embodiment of the present application also provides a surgical planning device, the specific embodiment of which is consistent with the embodiment recorded in the above method embodiment and the technical effects achieved, and some contents will not be repeated here.

[0219] The surgical planning device comprises a memory and at least one processor, wherein the memory stores a computer program, and the at least one processor is configured to implement the following steps when executing the computer program:

[0220] Obtain the patient's skull imaging data, implant device data, and preset redundant information;

[0221] Determining a preset collision condition according to the preset redundant information;

[0222] According to the preset collision condition, collision screening is performed based on the implant device data and the skull image data to obtain screening information, wherein the screening information is used to indicate whether there is a screening point set in the patient's skull, and the screening point set is used to indicate one or more areas that meet the preset collision condition.

[0223] In some embodiments, after obtaining the screening information, the at least one processor is configured to implement the following steps when executing the computer program:

[0224] Outputting plan prompt information according to the screening information, wherein the plan prompt information is used to indicate whether the patient meets the skull implantation condition of the implant device.

[0225] In some embodiments, the at least one processor is configured to perform collision screening according to the implant device data and the skull image data to obtain screening information in the following manner when executing the computer program:

[0226] Acquire a device three-dimensional model according to the implanted device data, wherein the device three-dimensional model has a relative device upper surface and device lower surface;

[0227] Acquire a three-dimensional skull model of the patient according to the skull image data, wherein the three-dimensional skull model has a relative skull inner surface and a skull outer surface;

[0228] Using the three-dimensional model of the device to perform collision screening in the three-dimensional model of the skull;

[0229] When one or more screening points meeting the preset collision condition are obtained during the collision screening process, a set of position information of each screening point meeting the preset collision condition in the three-dimensional skull model is used as a screening point set;

[0230] When no screening point that meets the preset collision condition is obtained during the collision screening process, empty information is used as the screening information.

[0231] In some embodiments, when one or more regions meeting the preset collision condition are obtained during the collision screening process, the at least one processor is configured to implement the following steps when executing the computer program:

[0232] Acquire a plurality of three-dimensional visualization areas in the three-dimensional skull model, each of the three-dimensional visualization areas being used to display the thickness of a different position of the skull;

[0233] For each of the three-dimensional visualization areas, a visualization collision result of the three-dimensional model of the device in the three-dimensional visualization area is obtained according to the screening point set, and the visualization collision result is used to assist in selecting an implantation position of the implant device.

[0234] In some embodiments, the visual collision result includes:

[0235] For each area meeting the preset collision condition, a mapping image is generated in the three-dimensional visualization area according to the vertical distance between each point on the upper surface of the implant device and the outer surface of the skull; or,

[0236] For each area that meets the preset collision condition, a mapping image is generated in the three-dimensional visualization area based on the difference between the vertical distance between each point on the upper surface of the device and the outer surface of the skull and the skull thickness corresponding to the screened qualified area.

[0237] In some embodiments, the preset redundant information includes a collision redundant distance, and the preset collision condition includes that when the implant device is implanted into the patient's skull and the lower surface of the device is not in contact with the inner surface of the skull, the distance of any point on the upper surface of the device above the outer surface of the skull does not exceed the collision redundant distance;

[0238] The at least one processor is configured to acquire the collision redundancy distance in the following manner when executing the computer program:

[0239] Acquiring disease information of the patient, wherein the disease information is obtained by analyzing the medical record information of the patient;

[0240] Performing a skull implant difficulty assessment of the implant device according to the patient's disease information to obtain an implant difficulty coefficient;

[0241] The collision redundancy distance is obtained according to the implantation difficulty coefficient and the disease information.

[0242] In some embodiments, the at least one processor is configured to obtain the implantation difficulty coefficient in the following manner when executing the computer program:

[0243] Inputting the disease information into a difficulty assessment model to obtain an implantation difficulty coefficient corresponding to the disease information; and / or,

[0244] When a coefficient selection operation for the disease information is received, the implantation difficulty coefficient corresponding to the disease information is obtained according to the coefficient selection operation.

[0245] In some embodiments, the at least one processor is configured to acquire the collision redundancy distance in the following manner when executing the computer program:

[0246] Input the disease information into the distance recommendation model to obtain model output data corresponding to the disease information; weight the model output data using the implantation difficulty coefficient as a weight value to obtain a collision redundancy distance; and / or,

[0247] When a distance selection operation for the implantation difficulty coefficient and the disease information is received, a collision redundant distance corresponding to the implantation difficulty coefficient and the disease information is obtained according to the distance selection operation.

[0248] See also Figure 6 , Figure 6 It is a structural block diagram of a surgical planning device 10 provided in an embodiment of the present application.

[0249] The surgical planning device 10 may include, for example, at least one memory 11 , at least one processor 12 , and a bus 13 connecting different platform systems.

[0250] The memory 11 may include a (computer) readable medium in the form of a volatile memory, such as a random access memory (RAM) 111 and / or a cache memory 112, and may further include a read-only memory (ROM) 113. The memory 11 also stores a computer program, which can be executed by the processor 12, so that the processor 12 implements the steps of any of the above methods. The memory 11 may also include a utility 114 having at least one program module 115, such program module 115 includes but is not limited to: an operating system, one or more application programs, other program modules and program data, each of which or some combination thereof may include the implementation of a network environment.

[0251] Accordingly, the processor 12 may execute the above-mentioned computer program and may execute the utility 114. The processor 12 may be implemented by one or more application specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field programmable gate arrays (FPGAs), or other electronic components.

[0252] The bus 13 may be a local bus representing one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or any of a variety of bus architectures.

[0253] The surgical planning device 10 may also communicate with one or more external devices such as a keyboard, a pointing device, a Bluetooth device, etc., and may also communicate with one or more devices that can interact with the surgical planning device 10, and / or communicate with any device (such as a router, a modem, etc.) that enables the surgical planning device 10 to communicate with one or more other computing devices. Such communication may be performed through an input / output interface 14. In addition, the surgical planning device 10 may also communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN) and / or a public network, such as the Internet) through a network adapter 15. The network adapter 15 may communicate with other modules of the surgical planning device 10 through a bus 13. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the surgical planning device 10 in actual applications, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms.

[0254] System Embodiments.

[0255] The embodiments of the present application also provide a medical system, the specific embodiments of which are consistent with the embodiments recorded in the above method embodiments and the technical effects achieved, and some contents will not be repeated here.

[0256] See also Figure 7 , Figure 7 It is a structural schematic diagram of a medical system provided in an embodiment of the present application.

[0257] The medical system comprises an implant device and a surgical planning device provided by any one of the device embodiments, wherein the implant device is used to be implanted into a patient's skull.

[0258] In some embodiments, the implanted device is a pulse generator.

[0259] Although the devices, methods, and systems described in the present disclosure refer to skull-based implants, the above-described devices, methods, and systems are not limited to use in skull-based implant device surgeries and may be used in medical surgical procedures related to implanting implant devices in other areas of a patient, such as the patient's spine or pelvic area.

[0260] Storage Medium Embodiments

[0261] The embodiment of the present application also provides a computer-readable storage medium, the specific embodiment of which is consistent with the embodiment recorded in the above method embodiment and the technical effects achieved, and some contents will not be repeated here.

[0262] The computer-readable storage medium stores a computer program, and when the computer program is executed by at least one processor, the steps of any of the above methods or the functions of any of the above surgical planning devices are implemented.

[0263] The computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. In an embodiment of the present application, the computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by an instruction execution system, device or device or used in combination with it. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination thereof. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0264] The computer-readable storage medium may include a data signal propagated in a baseband or as part of a carrier wave, wherein a readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer-readable storage medium may also be any computer-readable medium that can send, propagate, or transmit a program for use by an instruction execution system, device, or device or for use in combination with it. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the above. The program code for performing the operation of the present invention may be written in any combination of one or more programming languages, including Java, C++, Python, C#, JavaScript, PHP, Ruby, Swift, Go, Kotlin, etc. The program code may be executed entirely on a user computing device, partially on a user device, as a separate software package, partially on a user device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device may be connected to the user device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., through the Internet using an Internet service provider).

[0265] Program Product Embodiments.

[0266] The embodiment of the present application also provides a computer program product, the specific embodiments of which are consistent with the embodiments recorded in the above method embodiments and the technical effects achieved, and some contents will not be repeated here.

[0267] The computer program product comprises a computer program, and when the computer program is executed by at least one processor, the steps of any one of the above methods or the functions of any one of the above surgical planning devices are implemented.

[0268] See also Figure 8 , Figure 8 It is a structural diagram of a computer program product provided in an embodiment of the present application.

[0269] The computer program product is used to implement the steps of any of the above methods or to implement the functions of any of the above surgical planning devices. The computer program product may be a portable compact disk read-only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the computer program product of the present invention is not limited thereto, and the computer program product may be any combination of one or more computer-readable media.

[0270] The present application is explained from the viewpoints of purpose of use, effectiveness, progress and novelty. The above description and drawings of the present application are only preferred embodiments of the present application, and are not intended to limit the present application. Therefore, all structures, devices, features, etc. that are similar or identical to the present application, that is, all equivalent replacements or modifications made in accordance with the scope of the patent application of the present application, should fall within the scope of protection of the patent application of the present application.

Claims

1. A surgical planning device, characterized in that: The surgical planning device comprises a memory and at least one processor, wherein the memory stores a computer program, and the at least one processor is configured to implement the following steps when executing the computer program: Obtain the patient's skull imaging data, implant device data, and preset redundant information; Determining a preset collision condition according to the preset redundant information; According to the preset collision condition, collision screening is performed based on the implant device data and the skull image data to obtain screening information, wherein the screening information is used to indicate whether there is a screening point set in the patient's skull, and the screening point set is used to indicate one or more areas that meet the preset collision condition.

2. The surgical planning device according to claim 1, characterized in that: After obtaining the screening information, the at least one processor is configured to implement the following steps when executing the computer program: Outputting plan prompt information according to the screening information, wherein the plan prompt information is used to indicate whether the patient meets the skull implantation condition of the implant device.

3. The surgical planning device according to claim 1, characterized in that: The at least one processor is configured to perform collision screening according to the implant device data and the skull image data to obtain screening information in the following manner when executing the computer program: Acquire a device three-dimensional model according to the implanted device data, wherein the device three-dimensional model has a relative device upper surface and device lower surface; Acquire a three-dimensional skull model of the patient according to the skull image data, wherein the three-dimensional skull model has a relative skull inner surface and a skull outer surface; Using the three-dimensional model of the device to perform collision screening in the three-dimensional model of the skull; When one or more screening points meeting the preset collision condition are obtained during the collision screening process, a set of position information of each screening point meeting the preset collision condition in the three-dimensional skull model is used as a screening point set; When no screening point that meets the preset collision condition is obtained during the collision screening process, empty information is used as the screening information.

4. The surgical planning device according to claim 3, characterized in that: When one or more regions meeting the preset collision condition are obtained during the collision screening process, the at least one processor is configured to implement the following steps when executing the computer program: Acquire a plurality of three-dimensional visualization areas in the three-dimensional skull model, each of the three-dimensional visualization areas being used to display the thickness of a different position of the skull; For each of the three-dimensional visualization areas, a visualization collision result of the three-dimensional model of the device in the three-dimensional visualization area is obtained according to the screening point set, and the visualization collision result is used to assist in selecting an implantation position of the implant device.

5. The surgical planning device according to claim 4, characterized in that: The visual collision results include: For each area meeting the preset collision condition, a mapping image is generated in the three-dimensional visualization area according to the vertical distance between each point on the upper surface of the implant device and the outer surface of the skull; or, For each area that meets the preset collision condition, a mapping image is generated in the three-dimensional visualization area based on the difference between the vertical distance between each point on the upper surface of the device and the outer surface of the skull and the skull thickness corresponding to the screened qualified area.

6. The surgical planning device according to claim 1, characterized in that: The preset redundant information includes a collision redundant distance, and the preset collision condition includes that when the implant device is implanted into the patient's skull and the lower surface of the device is not in contact with the inner surface of the skull, the distance of any point on the upper surface of the device above the outer surface of the skull does not exceed the collision redundant distance; The at least one processor is configured to acquire the collision redundancy distance in the following manner when executing the computer program: Acquiring disease information of the patient, wherein the disease information is obtained by analyzing the medical record information of the patient; Performing a skull implant difficulty assessment of the implant device according to the patient's disease information to obtain an implant difficulty coefficient; The collision redundancy distance is obtained according to the implantation difficulty coefficient and the disease information.

7. The surgical planning device according to claim 6, characterized in that: The at least one processor is configured to obtain the implantation difficulty coefficient in the following manner when executing the computer program: Inputting the disease information into a difficulty assessment model to obtain an implantation difficulty coefficient corresponding to the disease information; and / or, When a coefficient selection operation for the disease information is received, the implantation difficulty coefficient corresponding to the disease information is obtained according to the coefficient selection operation.

8. The surgical planning device according to claim 7, characterized in that: The at least one processor is configured to acquire the collision redundancy distance in the following manner when executing the computer program: Input the disease information into the distance recommendation model to obtain model output data corresponding to the disease information; weight the model output data using the implantation difficulty coefficient as a weight value to obtain a collision redundancy distance; and / or, When a distance selection operation for the implantation difficulty coefficient and the disease information is received, a collision redundant distance corresponding to the implantation difficulty coefficient and the disease information is obtained according to the distance selection operation.

9. A medical system, characterized in that: The medical system includes: An implant device, the implant device being used to be implanted into a patient's skull; The surgical planning device according to any one of claims 1 to 8.

10. The medical system according to claim 9, characterized in that The implanted device is a pulse generator.

11. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by at least one processor, the function of the device according to any one of claims 1 to 8 is implemented.

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