Prediction method and device for prognosis effect of fat transplantation treatment of eyeball retraction, and medium

Abstract

The invention belongs to the field of intelligent medical treatment, and particularly relates to a method, equipment and medium for predicting the prognosis effect of fat transplantation treatment of eyeball retraction. The method comprises the following steps: S1, acquiring a bony orbital volume difference, an orbital cavity tissue soft body volume difference, an eyeball convexity variable quantity and a preoperative injection volume of a patient to be treated before an operation; s2, based on the difference between the preoperative injection volume and the volume of the orbital cavity soft tissue, obtaining a predicted difference between the volume of the orbital cavity soft tissue after an operation; and S3, inputting the bony orbital volume difference and the predicted postoperative orbital cavity soft tissue volume difference into any prediction model of the eyeball convexity variation to obtain the predicted postoperative eyeball convexity variation. The absorption coefficient is obtained through clinical data evaluation; on the basis of the absorption coefficient, the injection volume is corrected before an operation by combining prediction of a prediction model of eyeball convexity variation based on the orbital volume and the orbital cavity tissue volume, so that a better postoperative treatment effect is achieved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent medicine, and more specifically, to a method, device, medium and program product for predicting the prognosis effect of fat transplantation in the treatment of enophthalmos. Background Art

[0002] Post-traumatic Enophthalmos (PE) refers to the condition where partial tissue necrosis and adhesion in the orbital cavity occur due to injuries, orbital tumor resection / radiotherapy, etc., resulting in posterior and outward-inferior movement of the eyeball at the equator due to the loss of orbital contents and / or the increase in orbital cavity volume, and the eyeball appears sunken and retracted visually. Currently, this condition is usually confirmed in ophthalmological evaluations through the following diagnostic criteria: (1) Clinical examination: observing the appearance and manually detecting the position of the eyeball; (2) Imaging examination: mainly evaluating the structural changes in the orbit and the relative position of the eyeball through CT or MRI scans; (3) Quantitative measurement: using an exophthalmometer to quantify the protrusion of the eyeball. A recession outside the normal value range is considered a depression. Generally, a recession of more than 2 mm compared to the contralateral eyeball is considered clinically significant.

[0003] The necessity of treating enophthalmos is mainly based on the following considerations: (1) Improving visual function: Correcting the position of the eyeball through surgery or other treatment methods can improve vision and visual field and reduce diplopia; (2) Aesthetic and psychological impacts: Improving enophthalmos can restore the symmetry and beauty of the face, thereby enhancing the patient's self-confidence and social skills; Appropriate treatment can prevent further structural and functional complications caused by enophthalmos, such as vision loss, limited eye movement, orbital bone structure damage, eyelid dysfunction, and diplopia. Through timely and effective intervention, eye movement can be improved, vision can be restored, damaged orbital structures can be repaired, and normal eyelid function can be maintained.

[0004] The treatment methods include using autologous fat filling. The advantages are that autologous materials are used, avoiding the problem of foreign body reaction, with excellent biocompatibility, suitable for multiple injections or small amounts of filling; however, the disadvantage is that there may be problems with long-term volume stability. Experienced techniques are required to optimize the survival rate and shaping effect. Summary of the Invention

[0005] In view of the above problems, the present invention provides a method, device, and medium for predicting the prognosis effect of fat transplantation in the treatment of enophthalmos, and predicts the correction effect of fat filling according to the postoperative absorption rate of the patient.

[0006] The present application discloses a method for evaluating the retained volume after fat transplantation, and the method includes:

[0007] Obtain the preoperative injection volume;

[0008] The postoperative remaining volume is obtained by correcting the preoperative injection volume based on the absorption coefficient; the absorption coefficient is obtained by: the ratio of the postoperative remaining volume to the preoperative injection volume, which is obtained by fitting the preoperative injection volume of the patients in the training set and the postoperative remaining volume measured within a certain period of time after the operation.

[0009] Furthermore, the postoperative remaining volume is expressed as:

[0010] V fr =V fi ·R

[0011] In the formula, V fr represents the postoperative remaining volume, V fi represents the preoperative injection volume, and R represents the absorption coefficient.

[0012] Optionally, the certain period of time after the operation includes one or more of the following: one week after the operation, one month after the operation, three months after the operation, one year after the operation;

[0013] Optionally, the fat used for treatment is autologous fat;

[0014] Optionally, the preoperative injection volume is obtained by:

[0015] Obtaining images of patients with enophthalmos, where the images include the healthy orbit and the affected orbit;

[0016] Based on the differences between the healthy orbit and the affected orbit in the images, the preoperative injection volume is obtained.

[0017] This application also discloses a method for constructing a prediction model of the change amount of eyeball protrusion degree, including:

[0018] Obtaining the difference in bony orbital volume, the difference in soft volume of orbital cavity tissue, and the change amount of eyeball protrusion degree in the training set;

[0019] Using the difference in bony orbital volume and the difference in soft tissue volume of the orbital cavity as input data, and the change amount of eyeball protrusion degree as the prediction target, inputting into a machine learning model for training to obtain a prediction model of the change amount of eyeball protrusion degree.

[0020] Furthermore, the machine learning model is any one or more of the following: linear regression, ridge regression;

[0021] Optionally, the linear regression model is expressed as:

[0022] ΔPE=-K1·ΔV+K2·ΔOV

[0023] Among them, ΔPE represents the change in proptosis, ΔV represents the difference in bony orbital volume, ΔOV represents the difference in orbital cavity soft tissue, K1 represents the influence coefficient of the difference in bony orbital volume on proptosis, and K2 represents the influence coefficient of the difference in orbital cavity soft tissue volume on proptosis;

[0024] Optionally, obtain the bony volumes of the affected eye and the healthy eye, and the difference in bony volume is the difference between the bony volumes of the affected eye and the healthy eye;

[0025] Optionally, obtain the orbital cavity soft tissue volumes of the affected eye and the healthy eye, and the difference in orbital cavity soft tissue volume is the difference between the orbital cavity soft tissue volume of the affected side and the orbital cavity soft tissue volume of the healthy side;

[0026] Optionally, the difference in bony orbital volume is expressed as:

[0027] ΔV = V affected -V healthy

[0028] Among them, ΔV represents the difference in bony orbital volume, V affected represents the bony orbital volume of the affected side, V healthy represents the bony orbital volume of the healthy side;

[0029] Optionally, the calculation method of the change in orbital cavity tissue is expressed as:

[0030] ΔOV = V affected-content -V healthy-content

[0031] Among them, ΔOV represents the difference in orbital cavity soft tissue volume, V affected-content represents the orbital cavity soft tissue volume of the affected side, V healthy-content represents the orbital cavity soft tissue volume;

[0032] Optionally, obtain the proptosis of the affected eye and the healthy eye, and the change in proptosis is the difference between the proptosis of the healthy eye and the proptosis of the affected eye;

[0033] Optionally, the change in proptosis is expressed as:

[0034] ΔPE = PE healthy -PE affected

[0035] Among them, ΔPE represents the change in proptosis, PE healthy represents the proptosis of the healthy eye, PE affected represents the proptosis of the affected eye;

[0036] Optionally, obtain the images of the training set, and based on the images, extract the bony volumes of the affected eye and the healthy eye, and the orbital cavity soft tissue volumes of the affected eye and the healthy eye;

[0037] Optionally, the orbital cavity soft tissues include any one or more of the following: eyeball, orbital fat, extraocular muscles, optic nerve, cornea, lacrimal gland;

[0038] Optionally, the change in eyeball protrusion is measured based on an eyeball protrusion meter.

[0039] This application (first aspect) also discloses a method for predicting the prognosis effect of fat transplantation in the treatment of enophthalmos, the method comprising:

[0040] S1: Obtaining the difference in bony orbital volume, the difference in orbital cavity soft tissue volume, the change in eyeball protrusion, and the preoperative injection volume of the patient to be treated;

[0041] S2: Obtaining the predicted postoperative orbital cavity soft tissue volume difference based on the preoperative injection volume and the orbital cavity soft tissue volume difference;

[0042] S3: Inputting the difference in bony orbital volume and the predicted postoperative orbital cavity soft tissue volume difference into the prediction model of the change in eyeball protrusion to obtain the predicted postoperative change in eyeball protrusion.

[0043] Further, the predicted postoperative orbital cavity soft tissue volume difference is the sum of the orbital cavity soft tissue volume difference and the preoperative injection volume;

[0044] Optionally, the specific steps for obtaining the predicted postoperative orbital cavity soft tissue volume difference based on the preoperative injection volume and the orbital cavity soft tissue volume difference include: using the evaluation method for the retained volume after any one of the fat transplantation to obtain the predicted postoperative retained volume based on the preoperative injection volume, and obtaining the predicted postoperative orbital cavity soft tissue volume difference based on the predicted postoperative retained volume and the orbital cavity soft tissue volume difference;

[0045] Optionally, the predicted postoperative orbital cavity soft tissue volume difference is the sum of the orbital cavity soft tissue volume difference and the predicted postoperative retained volume;

[0046] Optionally, the method further comprises: obtaining the postoperative improvement in eyeball protrusion based on the predicted postoperative change in eyeball protrusion and the preoperative change in eyeball protrusion;

[0047] Optionally, the postoperative improvement in eyeball protrusion is the difference between the predicted postoperative change in eyeball protrusion and the preoperative change in eyeball protrusion;

[0048] Optionally, the predicted postoperative change in eyeball protrusion is expressed as:

[0049] ΔPE post =-K1·ΔV + K2·ΔOV post

[0050] where ΔPEpost Indicates the predicted change in postoperative proptosis, ΔV indicates the difference in bony orbital volume, and ΔOV post Indicates the difference in postoperative orbital cavity soft tissues, K1 indicates the influence coefficient of the difference in bony orbital volume on proptosis, and K2 indicates the influence coefficient of the difference in orbital cavity soft tissue volume on proptosis;

[0051] Optionally, the difference in postoperative orbital cavity soft tissues is expressed as:

[0052] ΔOV post = ΔOV pre + V fi

[0053] Wherein, ΔOV post Indicates the difference in postoperative orbital cavity soft tissue volume, ΔOV pre Indicates the preoperative orbital cavity soft tissue volume, and V fi Indicates the preoperative injection volume;

[0054] Optionally, the difference in postoperative orbital cavity soft tissues is expressed as:

[0055] ΔOV post = ΔOV pre + V fi ·R

[0056] Wherein, ΔOV post Indicates the difference in postoperative orbital cavity soft tissue volume, ΔOV pre Indicates the preoperative orbital cavity soft tissue volume, and V fi Indicates the preoperative injection volume, and R indicates the absorption coefficient;

[0057] Optionally, the predicted change in postoperative proptosis is expressed as:

[0058] ΔPE post = -K1·ΔV + K2·(ΔOV pre + V fi ·R) = -K1·ΔV + K2·ΔOV post

[0059] ΔPE post Indicates the predicted change in postoperative proptosis, ΔV indicates the difference in bony orbital volume, and ΔOV pre Indicates the preoperative orbital cavity soft tissue volume, and V fi Indicates the preoperative injection volume, and R indicates the absorption coefficient.

[0060] This application also discloses a method for calculating the fat filling amount for the treatment of enophthalmos by fat transplantation. The method includes:

[0061] Obtain the differences in the bony orbital volume, the volume of orbital cavity soft tissues, and the change amount of the expected postoperative eyeball protrusion degree of patients with enophthalmos;

[0062] According to the difference in the bony orbital volume, find the difference in the volume of orbital cavity soft tissues after surgery that makes the output of the prediction model of any of the change amounts of the eyeball protrusion degree be the change amount of the expected postoperative eyeball protrusion degree;

[0063] Based on the difference in the volume of orbital cavity soft tissues and the difference in the volume of orbital cavity soft tissues after surgery, obtain the fat filling amount for the first enophthalmos.

[0064] Furthermore, the fat filling amount for the first enophthalmos is expressed as: the difference between the difference in the volume of orbital cavity soft tissues after surgery and the difference in the volume of orbital cavity soft tissues before surgery;

[0065] Optionally, the fat filling amount for the first enophthalmos is expressed as:

[0066] V fi1 =ΔOV post -ΔOV pre

[0067] where, V fi1 is the fat filling amount for the first enophthalmos; ΔOV post represents the difference in the volume of orbital cavity soft tissues after surgery; ΔOV pre represents the difference in the volume of orbital cavity soft tissues before surgery;

[0068] Optionally, find the postoperative retention volume obtained by the evaluation method of the retention volume after the fat transplantation, and the preoperative injection volume of the fat filling amount for the first enophthalmos is used to obtain the fat filling amount for the second enophthalmos;

[0069] Optionally, the fat filling amount for the second enophthalmos is expressed as:

[0070]

[0071] V fi2 is the fat filling amount for the second enophthalmos; ΔOV post represents the difference in the volume of orbital cavity soft tissues after surgery; ΔOV pre represents the difference in the volume of orbital cavity soft tissues before surgery, and R represents the absorption coefficient;

[0072] Optionally, the fat filling amount for the second enophthalmos is expressed as:

[0073]

[0074] V fi2 is the fat filling amount for the second enophthalmos; ΔOV post represents the difference in the volume of orbital cavity soft tissues after surgery; ΔOV preIndicates the preoperative soft tissue volume difference in the orbital cavity, R represents the absorption coefficient; ΔPE is the expected change in exophthalmos after surgery, and ΔOV pre is the preoperative soft tissue volume difference in the orbital cavity;

[0075] Optionally, when the expected change in exophthalmos after surgery is complete correction, the amount of fat filling for the second enophthalmos is expressed as:

[0076]

[0077] V fi2 is the amount of fat filling for the second enophthalmos; ΔOV post represents the postoperative soft tissue volume difference in the orbital cavity; ΔOV pre represents the preoperative soft tissue volume difference in the orbital cavity, R represents the absorption coefficient, and ΔOV pre is the preoperative soft tissue volume difference in the orbital cavity.

[0078] The second aspect of the present application discloses a prediction system for the prognosis of fat transplantation in the treatment of enophthalmos, including: an acquisition module 201: used to acquire the bony orbital volume difference, orbital cavity soft tissue volume difference, exophthalmos change amount, and preoperative injection volume of the patient to be treated before surgery;

[0079] A preoperative planning module 202: used to obtain the predicted postoperative soft tissue volume difference in the orbital cavity based on the preoperative injection volume and the soft tissue volume difference in the orbital cavity;

[0080] A prediction effect module 203: used to input the bony orbital volume difference and the predicted postoperative soft tissue volume difference in the orbital cavity into the prediction model of any of the above-mentioned exophthalmos change amounts to obtain the predicted postoperative exophthalmos change amount.

[0081] The present application also discloses an evaluation system for the retained volume after fat transplantation, including:

[0082] A second acquisition module: used to acquire the preoperative injection volume;

[0083] An evaluation module: used to correct the preoperative injection volume based on the absorption coefficient to obtain the postoperative retained volume; the acquisition method of the absorption coefficient is: the ratio of the postoperative retained volume to the preoperative injection volume obtained by fitting the preoperative injection volume of the training set patients and the postoperative retained volume measured within a certain period after surgery.

[0084] The present application also discloses a calculation system for the amount of fat filling during the treatment of enophthalmos by fat transplantation, including:

[0085] A third acquisition module: used to acquire the bony orbital volume difference, orbital cavity soft tissue volume difference, and expected postoperative exophthalmos change amount of the enophthalmos patient;

[0086] The third pre-operative planning module: It is used to find the difference in the volume of postoperative orbital soft tissues that makes the output of the prediction model of any of the changes in the exophthalmos degree be the expected change in the exophthalmos degree after surgery according to the difference in the volume of the bony orbit.

[0087] The pre-operative correction module: It is used to obtain the amount of fat filling for the first enophthalmos based on the difference in the volume of orbital soft tissues and the difference in the volume of postoperative orbital soft tissues.

[0088] The third aspect of the present application discloses a computer device, which includes: a memory and a processor; the memory is used to store program instructions; the processor is used to call the program instructions, and when the program instructions are executed, it is used to execute the steps of the above-mentioned method for evaluating the retained volume after fat transplantation, or the method for constructing a prediction model of the change in the exophthalmos degree, or the method for predicting the prognosis effect of fat transplantation for treating enophthalmos, or the method for calculating the amount of fat filling for fat transplantation for treating enophthalmos.

[0089] The fourth aspect of the present application discloses a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the above-mentioned method for evaluating the retained volume after fat transplantation, or the method for constructing a prediction model of the change in the exophthalmos degree, or the method for predicting the prognosis effect of fat transplantation for treating enophthalmos, or the method for calculating the amount of fat filling for fat transplantation for treating enophthalmos.

[0090] The fifth aspect of the present application discloses a computer program product, including a computer program, and when the computer program is executed by a processor, it implements the steps of the above-mentioned method for evaluating the retained volume after fat transplantation, or the method for constructing a prediction model of the change in the exophthalmos degree, or the method for predicting the prognosis effect of fat transplantation for treating enophthalmos, or the method for calculating the amount of fat filling for fat transplantation for treating enophthalmos.

[0091] The present application has the following beneficial effects:

[0092] (1) First, the absorption coefficient is obtained through clinical data collection and evaluation, so that the calculation of the filling volume is more accurate.

[0093] (2) Based on the prediction of the orbital volume and the model of the volume of orbital tissues in this solution, the postoperative treatment effect of the patient can be predicted more accurately.

[0094] (3) Based on the prediction of the absorption coefficient and the model of the orbital volume and the volume of orbital tissues, the present application corrects the injection volume before surgery, so as to achieve a better postoperative treatment effect. Description of the Drawings

[0095] To more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the accompanying drawings required for the description of the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those skilled in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0096] Figure 1 It is a schematic flowchart of the method provided in the first aspect of the embodiments of the present invention;

[0097] Figure 2 It is a schematic diagram of the program product provided in the second aspect of the embodiments of the present invention;

[0098] Figure 3 It is a schematic diagram of the computer device provided in the embodiments of the present invention;

[0099] Figure 4 It is a schematic diagram of the architecture of an exemplary computing device provided in the embodiments of the present invention;

[0100] Figure 5 It is a schematic diagram of the storage medium provided in the embodiments of the present invention;

[0101] Figure 6 It is a schematic diagram of the volume of the affected orbital cavity tissue and the volume of the affected orbital cavity tissue provided in the embodiments of the present invention;

[0102] Figure 7 It is a schematic diagram of obtaining the filling absorption rate after surgery provided in the embodiments of the present invention;

[0103] Figure 8 It is a schematic flowchart of obtaining the absorption rate after surgery provided in the embodiments of the present invention. Detailed implementation manners

[0104] In order to enable those skilled in the art of the present technology to better understand the solutions of the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings in the embodiments of the present invention.

[0105] In some processes described in the specification, claims and the above accompanying drawings of the present invention, multiple operations appear in a specific order. However, it should be clearly understood that these operations can be executed not in the order in which they appear herein or in parallel. The serial numbers of the operations, such as S101, S102, etc., are only used to distinguish different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes can include more or fewer operations, and these operations can be executed in sequence or in parallel. It should be noted that the descriptions such as "first", "second", etc. in this article are used to distinguish different messages, devices, modules, etc., do not represent a sequence, and do not limit that "first" and "second" are of different types.

[0106] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of the present invention.

[0107] Figure 1 It is a schematic flowchart of a method for predicting the prognosis effect of fat transplantation in the treatment of enophthalmos. Specifically, the method includes the following steps:

[0108] S101: Obtain the preoperative bony orbital volume difference, orbital cavity tissue soft volume difference, change in eyeball protrusion, and preoperative injection volume of the patient to be treated;

[0109] In this application, 3D digital technology is used to quantitatively analyze the volume of orbital soft tissues in patients with post-traumatic enophthalmos deformity, so as to achieve precise correction of enophthalmos deformity; and evaluate the retention rate of grafts after surgery, optimize the surgical plan, and improve the predictability of the correction effect.

[0110] For complex post-traumatic enophthalmos deformity patients with orbital volume deficiency and increased orbital cavity volume at the same time, precise quantitative filling is achieved, and the treatment effect is predicted at the same time.

[0111] In some embodiments, the research methods include:

[0112] 1. Collect cases: 80 patients (80 eyes) with post-traumatic enophthalmos deformity who needed autologous granular fat filling were included in the Department of Ophthalmic Orbital Surgery, the Third Medical Center of Chinese PLA General Hospital.

[0113] 2. Preoperative marking of the quantitative filling volume:

[0114] Before surgery, based on the Dicom data of CT, 22 orbital cavity tissue structures such as extraocular muscles, eyeballs, and optic nerves were manually marked and segmented using VECTRA M3 3D Slicer, so as to achieve preoperative quantitative evaluation of the volume of orbital soft tissue fillers.

[0115] Specifically:

[0116] Obtain the image set of enophthalmos patients, including the healthy eye and the affected eye;

[0117] The boundaries of the orbital cavity tissue regions and the orbital boundaries of the healthy side and the affected side are manually marked by trained doctors on the images;

[0118] Perform three-dimensional reconstruction based on the image set, the marked boundaries of the orbital cavity soft tissue region, and the orbital boundaries to obtain the three-dimensional orbital structure and the corresponding three-dimensional boundaries of the orbital cavity tissues and orbits on the healthy and diseased sides;

[0119] Based on the three-dimensional orbital structure and the three-dimensional boundaries of the orbital cavity tissues and orbits on the healthy and diseased sides, obtain the volumes of the orbital cavity soft tissues and the bony orbital volumes on the healthy and diseased sides, and calculate the differences in the bony orbital volumes and the differences in the volumes of the orbital cavity soft tissues between the healthy and diseased sides;

[0120] In previous studies, some scholars found that an increase in the bony orbital volume by 1 cm³ would lead to a decrease in the exophthalmos by approximately 0.8 mm;

[0121] After research in this application, it is considered that the change in exophthalmos is not only related to the change in bony orbital volume, but also related to the change in the volume of orbital cavity soft tissues (such as atrophy of orbital cavity soft tissues). Therefore, in this application, an exophthalmometer is used to obtain the exophthalmos in the patient group, which will be used as input data, and the exophthalmos is used as the prediction target to train a machine learning model (such as support vector regression) to obtain a prediction model for the change in exophthalmos.

[0122] In some embodiments, the change in exophthalmos has a non-linear relationship with the prediction model of the change in exophthalmos, expressed as:

[0123] ΔPE = -K1·ΔV in1 +K2·ΔOV in2

[0124] where ΔPE represents the change in exophthalmos, ΔV represents the difference in bony orbital volume, in1 represents the exponential change of ΔV, in2 represents the exponential change of ΔOV, ΔOV represents the difference in orbital cavity soft tissues, and K1 represents the influence coefficient of ΔV in1 on exophthalmos, and K2 represents the influence coefficient of ΔOV in2 on exophthalmos.

[0125] In some embodiments,

[0126] In some embodiments, in1 = in2 = 1, expressed as:

[0127] ΔPE = -K1·ΔV + K2·ΔOV

[0128] where ΔPE represents the change in exophthalmos, ΔV represents the difference in bony orbital volume, ΔOV represents the difference in orbital cavity soft tissues, K1 represents the influence coefficient of the difference in bony orbital volume on exophthalmos, and K2 represents the influence coefficient of the difference in the volume of orbital cavity soft tissues on exophthalmos;

[0129] The difference in the volume of the bony orbit is expressed as:

[0130] ΔV = V affected - V healthy

[0131] where ΔV represents the difference in the volume of the bony orbit, V affected represents the volume of the bony orbit on the affected side, and V healthy represents the volume of the bony orbit on the healthy side;

[0132] where the calculation method of the change amount of the orbital cavity tissue is expressed as:

[0133] ΔOV = V affected-content - V healthy-content

[0134] where ΔOV represents the difference in the volume of the soft tissue in the orbital cavity, V affected-content represents the volume of the soft tissue in the orbital cavity on the affected side, and V healthy-content represents the volume of the soft tissue in the orbital cavity;

[0135] where the exophthalmos of the affected eye and the healthy eye is obtained, and the change amount of the exophthalmos is the difference between the exophthalmos of the healthy side and the exophthalmos of the affected side;

[0136] Optionally, the change amount of the exophthalmos is expressed as:

[0137] ΔPE = PE healthy - PE affected

[0138] The preoperative injection volume is measured by the clinician based on the difference in the volume of the soft tissue in the orbital cavity and the difference in the bony volume.

[0139] S102: Obtain the predicted difference in the volume of the soft tissue in the orbital cavity after surgery based on the preoperative injection volume and the difference in the volume of the soft tissue in the orbital cavity;

[0140] Generally speaking, the surgical treatment of enophthalmos includes:

[0141] (1) Orbital floor reconstruction: Fill the missing orbital floor tissue with materials such as autologous cartilage and silicone to restore the position of the eyeball. This surgery is applicable to enophthalmos caused by orbital floor fractures due to trauma.

[0142] (2) Implantation of intraocular fillers: Inject artificial materials such as hydroxypropyl methylcellulose into the posterior part of the eyeball to increase the intraocular pressure and achieve the purpose of reducing the eyeball. This method is applicable to enophthalmos caused by congenital enophthalmos or mild sequelae of trauma.

[0143] Here, we consider the treatment method of implanting intraocular fillers: after filling the preoperative injection volume determined by the doctor according to the imaging measurement results into the affected orbit, it is expected that the patient can achieve a better recovery effect.

[0144] , after filling the preoperative injection volume, the expected postoperative soft tissue difference in the orbital cavity is expressed as:

[0145] ΔOV post1 =ΔOV pre +V fi

[0146] Where, ΔOV post1 represents the postoperative soft tissue volume difference in the orbital cavity, ΔOV pre represents the preoperative soft tissue volume difference in the orbital cavity, and V fi represents the preoperative injection volume;

[0147] Substitute ΔV and ΔOV post1 into the prediction model of the change in exophthalmos constructed in the previous text to obtain the predicted change in exophthalmos.

[0148] However, in actual clinical work, it is found that despite precise measurement and quantification, the postoperative recovery is not as predicted. Based on this, this application believes that the actual injection volume that takes effect after surgery may be lower than the injection volume during the operation. Therefore, for patients who receive fat injection treatment for enophthalmos, we record the orbital volume and orbital cavity tissue volume of the affected eye and the healthy eye at multiple time points after surgery to evaluate the retained volume after surgery; thereby evaluating the retention rate of the graft, including the short-term retention rate (7 days after surgery, 1 month after surgery), and the long-term stable retention rate (3 months, 6 months after surgery).

[0149] Among them, quantitative evaluation is to manually delineate and segment 22 orbital cavity tissue structures such as extraocular muscles, eyeballs, and optic nerves based on CT-based Dicom data using VECTRA M3 3D Slicer, so as to achieve multiple quantitative evaluations of the volume of orbital cavity soft tissue fillers and orbital volume before and after surgery.

[0150] Based on the preoperative expected filling volume, the soft tissue volume of the affected orbital cavity and the soft tissue volume of the healthy orbital cavity obtained from the postoperative evaluation, the actual absorbed amount is obtained.

[0151] Actual absorbed amount = preoperative expected filling volume - (soft tissue volume of the healthy orbital cavity - soft tissue of the affected orbital cavity detected within a specific time after surgery)

[0152] In some embodiments of this application, manual tracing is performed by three doctors with more than 10 years of clinical experience in orbital surgery, and the average value of the volume is taken to reduce the manual tracing error.

[0153] In some embodiments, the pre-operative injection volume and the post-operative retained volume are obtained by manual tracing, and the ratio of the post-operative retained volume to the pre-operative injection volume, i.e., the absorption coefficient R, is obtained by fitting the data in the training set.

[0154] In some embodiments, the absorption coefficient of fat is R = 0.6 six months after the operation.

[0155] In some embodiments, we adjust the calculation method of the post-operative orbital soft tissue difference to:

[0156] ΔOV post2 = ΔOV pre +V fi ·R

[0157] where, ΔOV post2 represents the post-operative orbital soft tissue volume difference, ΔOV pre represents the pre-operative orbital soft tissue volume difference, V fi represents the pre-operative injection volume, and R represents the absorption coefficient.

[0158] Input ΔV and ΔOV post2 into the prediction model of the amount of change in exophthalmos constructed in the previous text to obtain the predicted amount of change in exophthalmos.

[0159] Preliminary estimate: Generally speaking, at least 30 - 50 cases of patient data need to be collected for preliminary statistical analysis. This is because in statistics, when the sample size reaches 30, the data begins to show a trend of normal distribution, which is convenient for parameter estimation.

[0160] Improving accuracy: To obtain a more accurate and reliable K value, it is recommended to collect data from 100 or more patients. The larger the sample size, the higher the credibility of the estimated value and the smaller the random error.

[0161] Retention rate R of the filler: The retention rates of different types of fillers are different, and should be estimated according to specific materials and clinical experience.

[0162] Unit consistency: Ensure that all volume units are cm 3 , and the length unit is mm.

[0163] Other influencing factors: In addition to the factors considered in the formula, scar formation, muscle function changes, etc. may also affect the position of the eyeball.

[0164] Clinical verification: It is recommended to verify and adjust the model in actual clinical practice to improve the accuracy of prediction.

[0165] S103: The predicted change in postoperative exophthalmos is obtained by inputting either the difference in bony orbital volume or the predicted difference in postoperative orbital soft tissue volume into the prediction model for the change in exophthalmos described above.

[0166] In some embodiments, ΔV and ΔOV post1 are input into the prediction model for the change in exophthalmos constructed above to obtain the predicted change in exophthalmos.

[0167] In some embodiments, ΔV and ΔOV post2 are input into the prediction model for the change in exophthalmos constructed above to obtain the predicted change in exophthalmos.

[0168] Based on the methods involved in the above prediction models, the present application also proposes a method for calculating the fat filling amount for the treatment of enophthalmos by fat transplantation. The method includes:

[0169] Obtaining the difference in bony orbital volume, the difference in orbital soft tissue volume, and the expected change in postoperative exophthalmos of a patient with enophthalmos;

[0170] According to the difference in bony orbital volume, finding the difference in postoperative orbital soft tissue volume that makes the output of any of the prediction models for the change in exophthalmos be the expected change in postoperative exophthalmos;

[0171] Based on the difference in orbital soft tissue volume and the difference in postoperative orbital soft tissue volume, obtaining the fat filling amount for the first enophthalmos.

[0172] In some embodiments, a new model for predicting the difference in postoperative orbital soft tissue volume is obtained by retraining after reversing the dataset and labels of the prediction model for the change in exophthalmos;

[0173] In some embodiments, the automatic optimization program can find the corresponding input for the output limit condition based on the trained prediction model for the change in exophthalmos.

[0174] In some embodiments, when the prediction model for the change in exophthalmos is expressed in the following form,

[0175] ΔPE = -K1·ΔV in1 +K2·ΔOV in2

[0176] The corrected preoperative fat filling amount is expressed as:

[0177]

[0178] It represents the pre-operative filling volume after correction. The interpretations of other expressions are the same as above.

[0179] In some embodiments, when the prediction model of the change in eyeball protrusion is as follows (i.e., when in1 = in2 = 1):

[0180] ΔPE = -K1·ΔV + K2·ΔOV

[0181] The corrected pre-operative fat filling volume is expressed as:

[0182]

[0183] When complete correction (i.e., ΔPE = 0), it is expressed as:

[0184]

[0185] V fi2 is the fat filling volume of the second enophthalmos; ΔOV post represents the difference in the volume of orbital soft tissues after surgery; ΔOV pre represents the difference in the volume of orbital soft tissues before surgery, R represents the absorption coefficient, ΔOV pre is the difference in the volume of orbital soft tissues before surgery.

[0186] In one embodiment, it is measured that:

[0187] V healthy = 30 cm 3

[0188] V affected = 32 cm 3

[0189] It is calculated that: ΔV = 32 - 30 = +2 cm 3

[0190] V healthy-content = 28 cm 3

[0191] V affected-contend-pre = 26 cm 3

[0192] It is calculated that ΔOV = 26 - 28 = -2 cm 3

[0193] R = 0.6

[0194] The measured K1 = 0.7; K2 = 0.8;

[0195] Then

[0196]

[0197] Preoperative injection: 6.25 cm 3 It can achieve the effect of complete correction half a year after the operation. This example is only for illustrating the calculation process and results, and does not represent the clinical actual values.

[0198] Precautions: Empirical coefficients K1 and K2 need to be determined through clinical research and may vary due to factors such as individual patient differences, causes of the disease, and treatment methods.

[0199] Figure 3 It is a schematic diagram of a computer device provided by an embodiment of the present invention. As Figure 3 shown, the device may include: one or more processors, and one or more memories; wherein, computer-readable code is stored in the memory, and when the computer-readable code is run by the one or more processors, the above-mentioned method can be executed.

[0200] The processor in this embodiment may be an integrated circuit chip with signal processing capabilities. The above-mentioned processor may be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. It can implement or execute the various methods, operations and logic block diagrams disclosed in the embodiments of the present disclosure. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc., and may be of the X86 architecture or the ARM architecture.

[0201] Generally speaking, the various example embodiments of the present disclosure may be implemented in hardware or a dedicated circuit, software, firmware, logic, or any combination thereof. Some aspects may be implemented in hardware, while other aspects may be implemented in firmware or software that can be executed by a controller, a microprocessor or other computing devices. When the aspects of the embodiments of the present disclosure are illustrated or described as block diagrams, flowcharts or using some other graphical representation, it will be understood that the blocks, devices, systems, technologies or methods described herein may be implemented as non-limiting examples in hardware, software, firmware, dedicated circuits or logic, general-purpose hardware or controllers or other computing devices, or some combination thereof.

[0202] For example, the method or device according to the embodiments of the present disclosure may also be implemented by means of Figure 4 the architecture of the computing device 3000 shown. As Figure 4As shown, the computing device 3000 may include a bus 3010, one or more CPUs 3020, a read-only memory (ROM) 3030, a random access memory (RAM) 3040, a communication port 3050 connected to a network, input / output components 3060, a hard disk 3070, etc. Storage devices in the computing device 3000, such as the ROM 3030 or the hard disk 3070, may store various data or files used for the processing and / or communication of the methods provided by this disclosure, as well as program instructions executed by the CPU. The computing device 3000 may also include a user interface 3080. Of course, Figure 4 the architecture shown is merely exemplary, and when implementing different devices, one or more components in the computing device shown may be omitted according to actual needs. Figure 4

[0203] An embodiment of the present invention also provides a computer-readable storage medium, such as Figure 5 shown, which is a schematic diagram of the storage medium provided by an embodiment of the present invention. Computer-readable instructions 4010 are stored on the computer storage medium 4020. When the computer-readable instructions 4010 are run by a processor, the methods according to the embodiments of the present disclosure described with reference to the above figures may be executed. The computer-readable storage medium in the embodiments of the present disclosure may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. The non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example but not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct memory bus random access memory (DR RAM). It should be noted that the memories for the methods described herein are intended to include, but are not limited to, these and any other suitable types of memories. It should be noted that the memories for the methods described herein are intended to include, but are not limited to, these and any other suitable types of memories.

[0204] An embodiment of the present disclosure also provides a computer program product or a computer program, which, when executed by a processor, implements the steps of the above method, such as Figure 2 shown, the computer program product or the computer program includes:

[0205] Acquisition module 201: configured to acquire the bony orbital volume difference, the soft tissue volume difference of the orbital cavity, the change amount of the eyeball protrusion degree, and the preoperative injection volume of the patient to be treated before surgery.

[0206] Preoperative planning module 202: configured to obtain the predicted postoperative soft tissue volume difference of the orbital cavity based on the preoperative injection volume and the soft tissue volume difference of the orbital cavity.

[0207] Predicted effect module 203: configured to input the bony orbital volume difference and the predicted postoperative soft tissue volume difference of the orbital cavity into a prediction model of any one of the change amounts of the eyeball protrusion degree to obtain the predicted postoperative change amount of the eyeball protrusion degree.

[0208] It should be noted that the flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code, and the module, the program segment, or the part of code includes one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order from that marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and the combination of blocks in the block diagram and / or flowchart, may be implemented by a dedicated hardware-based system for performing the specified functions or operations, or may be implemented by a combination of dedicated hardware and computer instructions.

[0209] Generally speaking, various example embodiments of the present disclosure may be implemented in hardware or dedicated circuits, software, firmware, logic, or any combination thereof. Some aspects may be implemented in hardware, while other aspects may be implemented in firmware or software that can be executed by a controller, a microprocessor, or other computing devices. When aspects of the embodiments of the present disclosure are illustrated or described as block diagrams, flowcharts, or using some other graphical representation, it will be understood that the blocks, devices, systems, technologies, or methods described herein may be implemented as non-limiting examples in hardware, software, firmware, dedicated circuits or logic, general hardware or controllers or other computing devices, or some combination thereof.

[0210] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described systems, devices, and units may refer to the corresponding processes in the foregoing method embodiments, and will not be described herein again.

[0211] In several embodiments provided by the present application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection to each other can be through some interfaces. The indirect coupling or communication connection of the devices or units can be in electrical, mechanical, or other forms.

[0212] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0213] In addition, in each embodiment of the present invention, the functional units can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0214] The exemplary embodiments of the present disclosure described in detail above are merely illustrative and not restrictive. Those skilled in the art should understand that various modifications and combinations can be made to these embodiments or their features without departing from the principles and spirit of the present disclosure, and such modifications should fall within the scope of the present disclosure.

Claims

1. A method for evaluating the retained volume after fat transplantation, characterized in that: The method comprises: Obtain preoperative injection volume; The preoperative injection volume is corrected based on the absorption coefficient to obtain the postoperative retention volume; the absorption coefficient is obtained by fitting the preoperative injection volume of the training set patients and the postoperative retention volume measured within a certain period of time after the operation to the ratio of the postoperative retention volume to the preoperative injection volume.

2. A method for constructing a prediction model for changes in eyeball protrusion, characterized in that: The method comprises: Obtain the bony orbital volume difference, orbital tissue soft volume difference, and eyeball protrusion change of the training set; use the bony orbital volume difference and orbital soft tissue volume difference as input data, and the eyeball protrusion change as the prediction target, and input them into the machine learning model to train and obtain the prediction model for the eyeball protrusion change.

3. The method for constructing a prediction model for eyeball protrusion change according to claim 2, characterized in that: The machine learning model is any one or more of the following: linear regression, ridge regression, support vector regression, multivariate power regression; Optionally, the linear regression is expressed as: ΔPE=-K1·ΔV+K2·ΔOV Among them, ΔPE represents the change in eyeball protrusion, ΔV represents the difference in bony orbital volume, ΔOV represents the difference in orbital soft tissue volume, K1 represents the influence coefficient of bony orbital volume difference on eyeball protrusion, and K2 represents the influence coefficient of orbital soft tissue volume difference on eyeball protrusion; Optionally, the bony volumes of the affected eye and the healthy eye are obtained, and the bony orbital volume difference is the difference between the bony volumes of the affected eye and the healthy eye; Optionally, the orbital soft tissue volumes of the affected eye and the healthy eye are obtained, and the orbital soft tissue volume difference is the difference between the orbital soft tissue volume of the affected side and the orbital soft tissue volume of the healthy side; Optionally, the bony orbital volume difference is expressed as: ΔV=V affected -V healthy Where ΔV represents the difference in bony orbital volume, V affected V represents the volume of the bony orbit on the affected side. healthy represents the volume of the bony orbit of the healthy side; Optionally, the calculation method of the orbital soft tissue volume difference is expressed as: ΔOV=V affected-content -V healthy-content Among them, ΔOV represents the volume difference of orbital soft tissue, V affected-content V represents the volume of soft tissue in the orbital cavity on the affected side. healthy-content It represents the volume of orbital soft tissue; Optionally, the eyeball protrusion of the affected eye and the healthy eye is obtained, and the eyeball protrusion change is the difference between the eyeball protrusion of the healthy eye and the eyeball protrusion of the affected eye; Optionally, the change in eyeball protrusion is expressed as: ΔPE=PE healthy -ON affected Among them, ΔPE represents the change in eyeball protrusion, PE healthy Expressed as the protrusion of the healthy eyeball, PE affected It is expressed as the protrusion of the eyeball on the affected side; Optionally, images of a training set are obtained, and the bone volume of the affected eye and the healthy eye, and the orbital soft tissue volume of the affected eye and the healthy eye are obtained based on image extraction; Optionally, the orbital soft tissue includes any one or more of the following: eyeball, orbital fat, extraocular muscle, optic nerve, cornea, lacrimal gland; Optionally, the change in eye protrusion is obtained based on an eye protrusion measuring instrument.

4. A method for predicting the prognosis of enophthalmos treated with fat transplantation, characterized in that: The method comprises: S1: Obtain the preoperative bony orbital volume difference, orbital tissue soft volume difference, eyeball protrusion change, and preoperative injection volume of the patient to be treated; S2: The predicted postoperative orbital soft tissue volume difference is obtained based on the preoperative injection volume and the orbital soft tissue volume difference; S3: The bony orbital volume difference and the predicted postoperative orbital soft tissue volume difference are input into the prediction model of eyeball protrusion change described in any one of claims 2-3 to obtain the predicted postoperative eyeball protrusion change.

5. The method for predicting the prognosis of enophthalmos treated with fat transplantation according to claim 4, characterized in that: The predicted postoperative orbital soft tissue volume difference is the sum of the orbital soft tissue volume difference and the preoperative injection volume; Optionally, the specific steps of obtaining the predicted postoperative orbital soft tissue volume difference based on the preoperative injection volume and the orbital soft tissue volume difference include: obtaining the predicted postoperative retention volume based on the preoperative injection volume using the fat transplantation retention volume assessment method of claim 1, and obtaining the predicted postoperative orbital soft tissue volume difference based on the predicted postoperative retention volume and the orbital soft tissue volume difference; Optionally, the predicted postoperative orbital soft tissue volume difference is the sum of the orbital soft tissue volume difference and the predicted postoperative retained volume; Optionally, the method further comprises: obtaining an improvement amount of the postoperative eyeball protrusion based on the predicted postoperative eyeball protrusion change amount and the preoperative eyeball protrusion change amount; Optionally, the improvement in eyeball protrusion after surgery is the difference between the predicted change in eyeball protrusion after surgery and the change in eyeball protrusion before surgery; Optionally, the predicted postoperative eyeball protrusion change is expressed as: ΔPE post =-K1·ΔV+K2·ΔOV post Among them, ΔPE post represents the predicted postoperative change in eyeball protrusion, ΔV represents the difference in bony orbital volume, and ΔOV post represents the postoperative orbital soft tissue volume difference, K1 represents the influence coefficient of the bony orbital volume difference on the eyeball protrusion, and K2 represents the influence coefficient of the orbital soft tissue volume difference on the eyeball protrusion; optionally, the postoperative orbital soft tissue volume difference is expressed as: ΔOV post =ΔOV pre +V fi Where, ΔOV post Indicates the difference in orbital soft tissue volume after surgery, ΔOV pre V represents the difference in orbital soft tissue volume before surgery. fi represents the volume injected before surgery; Optionally, the postoperative orbital soft tissue volume difference is expressed as: ΔOV post =ΔOV pre +V fi ·R Where, ΔOV post Denotes the difference in orbital soft tissue volume after surgery, ΔOV pre V represents the difference in orbital soft tissue volume before surgery. fi represents the preoperative injection volume, R represents the absorption coefficient; Optionally, the predicted postoperative eyeball protrusion change is expressed as: ΔPE post =-K1·ΔV+K2·(ΔOV pre +V fi ·R)=-K1·ΔV+K2·ΔOV post ΔPE post represents the predicted postoperative change in eyeball protrusion, ΔV represents the difference in bony orbital volume, and ΔOV pre V represents the difference in orbital soft tissue volume before surgery. fi represents the preoperative injection volume, and R represents the absorption coefficient.

6. A method for calculating the amount of fat filling for fat transplantation to treat enophthalmos, characterized in that: The method comprises: To obtain the difference in bony orbital volume, orbital soft tissue volume, and expected postoperative eyeball protrusion change in patients with enophthalmos; Finding a postoperative orbital soft tissue volume difference based on the bony orbital volume difference so that the output of the prediction model for the change in eyeball protrusion described in any one of claims 4-5 is the expected postoperative eyeball protrusion change; The amount of fat filling for the first enophthalmos was obtained based on the difference in orbital soft tissue volume and the difference in orbital soft tissue volume after surgery.

7. The method for calculating the amount of fat filling for fat transplantation to treat enophthalmos according to claim 6, characterized in that: The amount of fat filling for the first enophthalmos is expressed as: the difference between the volume difference of the orbital soft tissue after surgery and the volume difference of the orbital soft tissue before surgery; Optionally, the amount of fat filling for the first enophthalmos is expressed as: In fi1 =ΔOV post -ΔOV pre Among them, V fi1 ΔOV is the amount of fat filling for the first enophthalmos; post ΔOV represents the difference in orbital soft tissue volume after surgery; pre It indicates the difference in orbital soft tissue volume before surgery; Optionally, the postoperative retained volume obtained by the method for evaluating the postoperative retained volume of fat transplantation according to claim 1 is the preoperative injection volume of the first enophthalmos fat filling volume to obtain the second enophthalmos fat filling volume; Optionally, the amount of fat filling for the second enophthalmos is expressed as: V fi2 ΔOV is the amount of fat filling for the second enophthalmos; post ΔOV represents the difference in orbital soft tissue volume after surgery; pre represents the difference in orbital soft tissue volume before surgery, and R represents the absorption coefficient; Optionally, the amount of fat filling for the second enophthalmos is expressed as: V fi2 ΔOV is the amount of fat filling for the second enophthalmos; post ΔOV represents the difference in orbital soft tissue volume after surgery; pre represents the difference in orbital soft tissue volume before surgery, R represents the absorption coefficient; ΔPE represents the expected change in eyeball protrusion after surgery, ΔOV pre It is the difference in orbital soft tissue volume before surgery; Optionally, when the postoperative change in eyeball protrusion is expected to be fully corrected, the amount of fat filling for the second enophthalmos is expressed as: V fi2 ΔOV is the amount of fat filling for the second enophthalmos; post ΔOV represents the difference in orbital soft tissue volume after surgery; pre represents the difference in orbital soft tissue volume before surgery, R represents the absorption coefficient, ΔOV pre It is the difference of orbital soft tissue volume before operation.

8. A computer device, characterized in that: The device comprises: a memory and a processor; the memory is used to store a computer program; the processor executes the computer program to implement the steps of the method according to any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method described in any one of claims 1 to 7 are implemented.

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