Customized implant recommendation device based on hybrid modeling and operation method therefor

Abstract

A method of operation for a customized implant recommendation device based on hybrid modeling according to the present embodiment comprises: a step of generating first 3D image information including a plurality of human tissues associated with a surgical site for plastic surgery, wherein the first 3D image information is implemented to enable shape adjustment for the plurality of human tissues; a step of generating second 3D image information through data processing for position adjustment for at least one human tissue based on the first 3D image information; a step of performing position adjustment for at least one human tissue based on the second 3D image information; and a step of generating recommended implant list information by performing filtering on a pre-stored implant library based on the second 3D image information reflecting the position adjustment and at least one preset filtering element.

Description

Technology Field

[0001] The present specification relates to a customized implant recommendation device based on hybrid modeling and a method of operation for the same. More specifically, it relates to a customized implant recommendation device based on hybrid modeling implemented to recommend a customized implant according to the adjusted shape of a specific human tissue (e.g., cartilage) by applying hybrid modeling capable of adjusting a specific human tissue (e.g., cartilage), and a method of operation for the same. Background Technology

[0002] Plastic surgery has established itself as a common procedure, recognized as a cosmetic concept not only for patients requiring reconstructive surgery due to accidents but also for the general public seeking external changes for purely aesthetic reasons. Consequently, with the popularization of plastic surgery, the demand for various types of implants suitable for the procedure is increasing.

[0003] On the other hand, conventional plastic surgery is not based on consultations using the patient's medical imaging, and since the surgery is planned based on the subjective judgment of the medical staff, there are cases where surgical results do not meet the intended purpose.

[0004] Conventional simulation technology visualizes image changes based on surface mesh movement; however, because it is based on mesh data of the skin's surface, it is difficult to reflect detailed patient information.

[0005] In addition, prefabricated implants do not conform to the affected area, so there is a high probability of side effects such as bending or displacement of the implant, which often leads to revision surgery due to decreased patient satisfaction after surgery.

[0006] For a prior proposal, reference may be made to Public Patent No. 10-2012-0096238 concerning a method for providing a facial correction image and a system thereof. The problem to be solved

[0007] The purpose of this specification is to provide a customized implant recommendation device and a method of operation for the same, which is implemented to not only enable the adjustment of a specific human tissue (e.g., cartilage) to suit an individual's affected area, but also to recommend a customized implant based on the shape according to the adjusted specific human tissue (e.g., cartilage). means of solving the problem

[0008] A method of operation for a customized implant recommendation device based on hybrid modeling according to the present embodiment comprises: a step of generating first 3D image information including a plurality of human tissues associated with a surgical site for plastic surgery, wherein the first 3D image information is implemented to enable shape adjustment for the plurality of human tissues; a step of generating second 3D image information through data processing for position adjustment for at least one human tissue based on the first 3D image information; a step of performing position adjustment for at least one human tissue based on the second 3D image information; and a step of generating recommended implant list information by performing filtering on a pre-stored implant library based on the second 3D image information reflecting the position adjustment and at least one preset filtering element.

[0009] According to the present embodiment, a plurality of human tissues correspond to a first human tissue corresponding to a nasal bone and a second human tissue corresponding to a nasal cartilage, at least one human tissue is associated with the second human tissue, and the first 3D image information is characterized by being expressed in the form of a tri-angle mesh with three vertices and one vector as basic units.

[0010] According to the present embodiment, at least one human tissue is characterized by being converted from a tri-angle mesh form into a one-dimensional element (1D element) form with two nodes and one beam as basic units through data processing.

[0011] According to the present embodiment, at least one filtering element is characterized by including at least one of a first determining element associated with a spacing between predetermined positions for at least one human tissue with position adjustment and a second determining element associated with a predetermined thickness for at least one human tissue with position adjustment.

[0012] According to the present embodiment, the recommended implant list information includes specification information for one or more prefabricated implants filtered according to specification information for prefabricated implants stored in a prefabricated implant library using at least one filtering element, and the one or more prefabricated implants on the recommended implant list information are arranged in order according to predetermined weights.

[0013] A customized implant recommendation device based on hybrid modeling according to the present embodiment includes: a simulation module that generates first 3D image information that includes a plurality of human tissues associated with a surgical site for plastic surgery and is implemented to enable shape adjustment for the plurality of human tissues; a data processing module that generates second 3D image information through data processing for position adjustment of at least one human tissue based on the first 3D image information; a position adjustment module that performs position adjustment of at least one human tissue based on the second 3D image information; and an implant recommendation module that generates recommended implant list information by performing filtering on a pre-stored implant library based on the second 3D image information reflecting the position adjustment and at least one preset filtering element. Effects of the invention

[0014] According to the present embodiment, a customized implant recommendation device and a method of operation for the same may be provided, which are implemented to recommend a customized implant according to the adjusted shape of a specific human tissue (e.g., cartilage) by applying a hybrid modeling capable of adjusting a specific human tissue (e.g., cartilage).

[0015] Accordingly, by more accurately implementing a function to recommend customized implants for patients based on simulation results of multiple human tissues associated with the affected area (e.g., nose) after plastic surgery, it becomes possible to establish a more sophisticated surgical plan for plastic surgery and to easily predict the surgical outcome. Brief explanation of the drawing

[0016] Figure 1 is an example diagram showing first 3D image information according to an existing file format. FIG. 2 is an example diagram showing second 3D image information to which hybrid modeling according to the present embodiment is applied. FIG. 3 is a block diagram for a customized implant recommendation device based on hybrid modeling according to the present embodiment. FIG. 4 is a conceptual diagram showing specification information for prefabricated implants stored in an implant library according to the present embodiment. FIG. 5 is a flowchart illustrating the operation method of a customized implant recommendation device based on hybrid modeling according to the present embodiment. FIG. 6 is an illustrative diagram for explaining an example of a filtering operation by a customized implant recommendation device based on hybrid modeling according to the present embodiment. FIG. 7 is an illustrative diagram showing another example of a filtering operation by a customized implant recommendation device based on hybrid modeling according to the present embodiment. FIG. 8 is a conceptual diagram illustrating data processing for position adjustment according to the present embodiment. FIG. 9 is an example diagram showing first 3D image information prior to data processing for position adjustment according to the present embodiment. FIG. 10 is an example diagram showing a unit cell for data processing for position adjustment according to the present embodiment. FIG. 11 is an example diagram illustrating the process of performing data processing for position adjustment using a unit cell according to the present embodiment. FIG. 12 is a block diagram illustrating a computing environment including a computing device suitable for use in an exemplary embodiment. Specific details for implementing the invention

[0017] The aforementioned characteristics and the detailed description below are all exemplary matters to aid in the explanation and understanding of this specification. That is, this specification is not limited to such embodiments and may be embodied in other forms. The following embodiments are merely examples to fully disclose this specification and are intended to convey this specification to those skilled in the art to which this specification pertains.

[0018] Therefore, if there are multiple methods for implementing the components of this specification, it is necessary to make clear that the implementation of this specification is possible using any specific of these methods or any of those identical thereto.

[0019] Where in this specification it is mentioned that a configuration includes specific elements, or that a process includes specific steps, it means that other elements or steps may be additionally included.

[0020] That is, the terms used in this specification are intended only to describe specific embodiments and are not intended to limit the concept of this specification. Furthermore, the examples described to aid in understanding the invention also include complementary embodiments.

[0021] The terms used in this specification have the meanings generally understood by those skilled in the art to which this specification pertains. Generally, the terms used should be interpreted in a consistent sense according to the context of this specification.

[0022] Furthermore, terms used in this specification should not be interpreted in an overly idealistic or formal sense unless their meaning is clearly defined. Embodiments of this specification are described below with reference to the attached drawings.

[0023] The image referred to in this specification means an image obtained by measuring three-dimensional data based on a separate input source (e.g., infrared, visible light, laser, ultrasound, radiation, etc.). That is, image data can be obtained based on values ​​measured along the path of reflection after irradiating the input source.

[0024] Meanwhile, the medical image mentioned in this specification is obtained using a medical imaging device, and 3D image information can be realized in a simulation by stacking cross-sections of a plurality of medical images.

[0025] Meanwhile, in this specification, medical image can be understood as a standardized image that complies with the Digital Imaging and Communications in Medicine (DICOM) standard.

[0026] Here, the medical imaging device may include a Computed Tomography (hereinafter 'CT') device (or MRI device).

[0027] In addition, in this specification, each human tissue within a medical image can be classified based on a density value (gray scale value) corresponding to the type of human tissue. That is, a unique value based on the unit of the density value (i.e., gray scale value) can be pre-set for each human tissue within a medical image.

[0028] Furthermore, the 3D image information mentioned in this specification may be selectively implemented to represent only the human tissue to be represented on the 3D image using a density value (gray scale value) corresponding to each human tissue.

[0029] Figure 1 is an example diagram showing first 3D image information according to an existing file format.

[0030] Referring to FIG. 1 (a), the first 3D image information (1) according to the existing file format may include multiple human tissues associated with the affected area (e.g., nose) for plastic surgery.

[0031] For example, based on the first 3D image information (1), among the multiple human tissues associated with the affected area (e.g., nose) for plastic surgery, a first human tissue (bone) corresponding to the nasal bone and a second human tissue (cartilage, hereinafter 'CTL') corresponding to the nasal cartilage can be implemented.

[0032] In other words, the file format of the first 3D image information (1) may be the STL (Standard Triangulated Language) file format, which is commonly used to save data 3D modeled for CAD (Computer Aided Design) or 3D printing as a standard format file.

[0033] For example, the surface of a plurality of human tissues represented according to the first 3D image information (1) can be implemented as a geometric structure using triangles, which are the basic units for the STL file format.

[0034] More specifically, the surface of a plurality of human tissues represented according to the first 3D image information (1) can be represented in the form of a tri-angle mesh with three vertices and one vector as basic units.

[0035] Referring to Fig. 1 (b), since the first 3D image information (1) follows the STL file format expressed in the form of a tri-angle mesh, there is a limitation in that only shape adjustments are implemented based on the first 3D image information (1), such that the second human tissue (CTL) corresponding to the nasal cartilage is deformed (m) to be raised convexly upward on the surface or deformed to be sunken inward.

[0036] FIG. 2 is an example diagram showing second 3D image information to which hybrid modeling according to the present embodiment is applied.

[0037] Referring to FIG. 2(a), the second 3D image information (2) according to the present embodiment can be understood as having a hybrid modeling applied that allows for position adjustment of at least one human tissue associated with a surgical site (e.g., nose) for plastic surgery.

[0038] For example, based on the second 3D image information (2) according to the present embodiment, among a plurality of human tissues associated with a surgical site (e.g., nose) for plastic surgery, a first human tissue (bone) corresponding to the nasal bone and a second human tissue (CTL_1d) corresponding to the nasal cartilage can be implemented according to hybrid modeling.

[0039] Here, the second human tissue (CTL_1d) can be understood as being associated with nasal cartilage (e.g., Lateral C region).

[0040] For example, the first human tissue (bone) can be implemented based on the STL file format commonly used in CAD (Computer Aided Design) or 3D printing.

[0041] For example, the second human tissue (CTL_1d) can be represented in the form of a one-dimensional element (1D element) with two nodes and one beam as basic units.

[0042] Specifically, based on the second 3D image information (2), the second human tissue (CTL_1d) corresponding to the nasal cartilage can be represented in the simulation using a one-dimensional element (1D element).

[0043] According to the present embodiment, since multiple nodes of the second human tissue (CTL_1d) represented by a one-dimensional element (1D element) are movable (i.e., moving), the shape of the affected area (e.g., nose) on the second 3D image information (2) can be changed to a shape desired by the patient.

[0044] In other words, the second human tissue (CTL_1d) corresponding to the nasal cartilage implemented in the second 3D image information (2) can be implemented so that not only the shape but also the position can be adjusted.

[0045] Ultimately, through the application of the hybrid modeling mentioned in this specification, a first human tissue (bone) capable of only shape adjustment and a second human tissue (CTL_1d) capable of both shape adjustment and position adjustment can be implemented on a single second 3D image information (2).

[0046] For a clear and concise understanding of the present embodiment, a horizontal distance (L) between predetermined positions (e.g., P1, P2 in FIG. 2(a)) and a vertical distance (d) from the horizontal distance (L) to the surface of the nose skin of the simulation may be defined.

[0047] In addition, a horizontal distance (L') between predetermined positions (e.g., P1', P2' in FIG. 2(b)) and a vertical distance (d') from the horizontal distance (L') to the surface of the nose skin of the simulation can be defined after movement (i.e., movement) is applied to at least one human tissue (e.g., cartilage).

[0048] In order to provide a customized implant according to the characteristics of the patient mentioned in this specification, in order to implement movement (i.e., movement) of at least one human tissue (e.g., cartilage) among a plurality of human tissues associated with the patient's affected area, position adjustment on second 3D image information implemented through simulation is essential.

[0049] FIG. 3 is a block diagram for a customized implant recommendation device based on hybrid modeling according to the present embodiment.

[0050] Referring to FIGS. 1 to 3, the customized implant recommendation device (300) according to the present embodiment may include a simulation module (310), a data processing module (320), a position adjustment module (330), and an implant recommendation module (340).

[0051] The simulation module (310) of FIG. 3 can be implemented to acquire multiple medical images from a CT device or MRI device that is pre-linked with a customized implant recommendation device (300).

[0052] Here, the medical image may be pixel data obtained from a CT image associated with the affected area for plastic surgery (e.g., the patient's nose). Additionally, the medical image may be understood as being collected or transmitted from the patient or the hospital.

[0053] Additionally, the simulation module (310) of FIG. 3 may be implemented to generate first 3D image information (1st 3D Image Information) including multiple human tissues associated with a surgical site (e.g., a patient's nose) for plastic surgery using one or more preset boundary values ​​based on multiple medical imaging images (e.g., images within a CT image).

[0054] According to the present embodiment, one or more preset threshold values ​​can be understood as values ​​that are set differently for each of the multiple human tissues associated with the affected area for plastic surgery (e.g., the patient's nose).

[0055] For example, one or more preset boundary values ​​can be used for boundary work using partial 2D images for multiple medical images acquired from a CT device or MRI device.

[0056] In addition, among the values ​​classified through the boundary process, the brightness values ​​selected by the operator for 3D image information can be converted into an STL file.

[0057] The first 3D image information according to one embodiment seen in this specification may be understood as corresponding to a 3D rendering image implemented in a simulation by integrating one or more STL files selected by an operator.

[0058] For example, the first 3D image information may include multiple 3D objects corresponding to multiple human tissues.

[0059] That is, it will be understood that multiple 3D objects corresponding to multiple human tissues (e.g., cartilage, bone, skin) associated with a surgical site for plastic surgery (e.g., the patient's nose) can be shaped in a simulation based on the first 3D image information.

[0060] Additionally, the simulation module (310) of FIG. 3 can implement (or represent) one or more human tissues (e.g., cartilage, bone, skin) associated with a specific human tissue based on pixel data of a plurality of medical images using pre-set formula information associated with the human tissue, so that they can be identified in a simulation based on the first 3D image information.

[0061] In this case, one or more human tissues (e.g., cartilage, bone, skin) associated with the affected area for plastic surgery (e.g., the patient's nose) implemented based on the first 3D image information may only be shape-adjusted as in Fig. 1 (b) (e.g., 'm' in Fig. 1 (b)).

[0062] Meanwhile, the simulation module (310) according to the present embodiment can generate first 3D image information based on formula information predetermined for a plurality of medical imaging images and a plurality of human tissues.

[0063] Meanwhile, artificial intelligence deep learning techniques such as a deep neural network technique or a convolutional neural network technique may be applied to the simulation module (310) according to the present embodiment.

[0064] Accordingly, it can be implemented so that a first 3D image information representing multiple human tissues is automatically generated by a simulation module (310) based on multiple medical image images collected from a pre-linked CT device or MRI device.

[0065] The data processing module (320) of FIG. 3 can generate second 3D image information through data processing for position adjustment of at least one human tissue based on first 3D image information.

[0066] For example, the data processing module (320) of FIG. 3 can perform data processing on at least one human tissue (e.g., CTL of FIG. 1(a)) implemented in the form of a tri-angle mesh based on the first 3D image information.

[0067] Accordingly, at least one human tissue implemented in the form of a tri-angle mesh (e.g., CTL of FIG. 1(a)) can be converted into at least one human tissue implemented in the form of a 1D element with two nodes and one beam as basic units (e.g., CTL_1d of FIG. 2(a)).

[0068] The second 3D image information mentioned in this specification can be implemented in a form where human tissue associated with nasal cartilage (e.g., CTL_1d in FIG. 2(a)) converted into a 1D element form according to the application of hybrid modeling and human tissue associated with nasal bone (e.g., bone in FIG. 2(a)) in the form of a conventional tri-angle mesh coexist in the simulation.

[0069] The position adjustment module (330) of FIG. 3 can perform position adjustment for at least one human tissue (e.g., cartilage) converted into a one-dimensional element (1D element) based on the second 3D image information.

[0070] For example, it will be understood that among a plurality of 3D objects corresponding to a plurality of human tissues (e.g., cartilage, bone, skin) associated with a surgical site for plastic surgery (e.g., the patient's nose), at least one object corresponding to a human tissue (e.g., cartilage) converted into a 1D element form can be implemented to allow not only shape adjustment but also position adjustment in a simulation based on the second 3D image information.

[0071] According to the present embodiment, a second position (P2 in FIG. 2(a)) associated with the tip of the nose of at least one human tissue (e.g., CTL_1d in FIG. 2(a)) can be converted into a one-dimensional element (1D element) and then moved to a second position (P2' in FIG. 2(b) to satisfy the patient's plastic surgery plan (i.e., moving).

[0072] For example, the second 3D image information may include at least one human tissue (e.g., CTL_1d' in FIG. 2(b)) that has been moved (i.e., moved) to a second position (P2' in FIG. 2(b)) to satisfy the patient's plastic surgery plan.

[0073] The implant recommendation module (340) of FIG. 3 may be implemented to determine at least one filtering element based on second 3D image information including at least one human tissue (e.g., CTL_1d' of FIG. 2(b)) that reflects position adjustment to satisfy the plastic surgery plan.

[0074] For example, at least one filtering element may include a first determination element associated with a gap (e.g., L' in FIG. 2(b)) between predetermined positions (e.g., P1', P2' in FIG. 2(b)) that reflect positional adjustment for at least one human tissue (e.g., cartilage, CTL_1d' in FIG. 2(b)) converted into a one-dimensional element (1D element) based on second 3D image information.

[0075] For reference, the first determining factor is described in more detail with reference to Fig. 6, which will be described later.

[0076] Additionally, at least one filtering element may include a second determining element associated with a predetermined thickness (e.g., d' in FIG. 2(b)) that reflects positional adjustment for at least one human tissue (e.g., cartilage, CTL_1d' in FIG. 2(b)) converted into a one-dimensional element (1D element) based on the second 3D image information.

[0077] For reference, the second determining factor is explained in more detail with reference to Fig. 7, which will be described later.

[0078] Furthermore, at least one filtering element may include a third determining element associated with the shape (i.e., curvature) of the affected area, which reflects the position adjustment of at least one human tissue (e.g., cartilage, CTL_1d' in FIG. 2(b)) converted into a one-dimensional element (1D element) based on the second 3D image information.

[0079] For reference, the third determining factor is explained in more detail with reference to Fig. 7, which will be described later.

[0080] Additionally, the implant recommendation module (340) may be implemented to be linked with a separately constructed implant library (not shown), or so that the implant library (not shown) is stored and managed in a separate storage space of the implant recommendation module (340).

[0081] Here, the implant library may refer to a pre-built database (Database; hereinafter 'DB') containing specification information on prefabricated implants associated with the affected area (e.g., the patient's nose).

[0082] According to the present embodiment, the implant recommendation module (340) may be implemented to perform filtering on a pre-stored implant library based on at least one filtering element.

[0083] For example, the specification information for prefabricated implants may include length information, thickness information, and shape information. For reference, the implant library is described in more detail with reference to Fig. 4, which will be described later.

[0084] Additionally, the implant recommendation module (340) can generate recommended implant list information for a surgical site (e.g., a patient's nose) for plastic surgery by performing filtering on a library of implants stored in advance based on at least one filtering element.

[0085] For example, the recommended implant list information may include specification information for one or more prefabricated implants filtered according to specification information for prefabricated implants stored in the implant library using at least one filtering element.

[0086] In this case, it will be understood that one or more ready-made implants in the recommended implant list information may be sorted in order according to predetermined weights.

[0087] FIG. 4 is a conceptual diagram showing specification information for prefabricated implants stored in an implant library according to the present embodiment.

[0088] Referring to FIGS. 1 to 4, a plurality of specification information for a plurality of implants (410 to 440) may be stored in advance in the implant library (400) according to the present embodiment.

[0089] For example, the first specification information for the first implant (410) may include model code information (M1) for identifying the user's implant, first length information (A) corresponding to 53 mm, first thickness information (B) corresponding to 4 mm, first width information (D) corresponding to 9 mm, and first line information (L_l1) associated with the back of the implant that comes into contact with the patient's affected area (e.g., nose).

[0090] For example, the second specification information for the second implant (420) may include model code information (M2) for identifying the user's implant, second length information (A) corresponding to 52 mm, second thickness information (B) corresponding to 5 mm, second width information (D) corresponding to 10 mm, and second line information (L_l2) associated with the back of the implant that comes into contact with the patient's affected area (e.g., nose).

[0091] For example, the third specification information for the third implant (430) may include model code information (M3) for identifying the user's implant, third length information (A) corresponding to 51 mm, third thickness information (B) corresponding to 5 mm, third width information (D) corresponding to 9 mm, and third line information (L_l3) associated with the back of the implant that comes into contact with the patient's affected area (e.g., nose).

[0092] For example, the fourth specification information for the fourth implant (440) may include model code information (M4) for identifying the user's implant, fourth length information (A) corresponding to 50 mm, fourth thickness information (B) corresponding to 4 mm, fourth width information (D) corresponding to 9 mm, and fourth line information (L_l4) associated with the back of the implant that comes into contact with the patient's affected area (e.g., nose).

[0093] A customized implant recommendation device according to the present embodiment (e.g., 300 of FIG. 3) can be implemented to add or update specification information for ready-made implants stored in an implant library.

[0094] When a filtering operation is performed by a customized implant recommendation device (e.g., 300 in FIG. 3) according to the present embodiment, at least one of length information, thickness information, width information, and line information corresponding to the specification information for prefabricated implants stored in the implant library may be used.

[0095] Hereinafter, for the sake of a clear and concise description of the present specification, it may be assumed that at least one of length information, thickness information, and line information among the specification information for ready-made implants is used in the filtering operation by the customized implant recommendation device (e.g., 300 of FIG. 3) according to the present embodiment.

[0096] FIG. 5 is a flowchart illustrating the operation method of a customized implant recommendation device based on hybrid modeling according to the present embodiment.

[0097] Referring to FIGS. 1 to 5, in step S510, the customized implant recommendation device based on hybrid modeling according to the present embodiment can generate first 3D image information (1st 3D Image Information) including a plurality of human tissues associated with a surgical site (e.g., nose) for plastic surgery.

[0098] Here, a plurality of human tissues may correspond to a first human tissue corresponding to the nasal bone (e.g., bone in FIG. 2(a)) and a second human tissue corresponding to the nasal cartilage (e.g., CTL_1d in FIG. 2(a)).

[0099] For example, the first 3D image information can be represented in the form of a tri-angle mesh with three vertices and one vector as basic units. That is, the first 3D image information can be implemented to enable only shape adjustment for multiple human tissues.

[0100] In step S520, the customized implant recommendation device based on hybrid modeling according to the present embodiment can generate second 3D image information (2nd 3D Image Information) through data processing for position adjustment of at least one human tissue based on first 3D image information.

[0101] Here, at least one human tissue may correspond to a second human tissue in the form of a tri-angle mesh corresponding to nasal cartilage (e.g., CTL_1d in Fig. 2(a)).

[0102] For example, at least one human tissue can be converted from a tri-angle mesh form into a 1D element form with two nodes and one beam as basic units through data processing.

[0103] It will be understood that the second 3D image information according to the present embodiment is generated based on the aforementioned hybrid modeling in which a first human tissue capable of only shape adjustment (e.g., bone in FIG. 2(b)) and a second human tissue capable of both shape adjustment and position adjustment (e.g., CTL_1d in FIG. 2(b)) are implemented on a single second 3D image information (e.g., 2 in FIG. 2).

[0104] However, it will be understood that data processing for position adjustment of at least one human tissue mentioned in this specification is not limited to a method of converting into a one-dimensional element (1D element) form, and that various methods, including 3D environment processing methods mainly used in games or animations, may be applied to data processing for position adjustment of at least one human tissue.

[0105] In step S530, the customized implant recommendation device based on hybrid modeling according to the present embodiment can perform position adjustment for at least one human tissue based on second 3D image information.

[0106] For example, at least one human tissue (e.g., CTL_1d' in FIG. 2(b) according to the present embodiment) can be implemented in a simulation by moving (i.e., moving) from a second position (P2 in FIG. 2(b)) before surgery to a second position (P2' in FIG. 2(b)) to satisfy the patient's plastic surgery plan.

[0107] In step S540, the customized implant recommendation device based on hybrid modeling according to the present embodiment may be implemented to determine at least one filtering element based on second 3D image information (e.g., 2 in FIG. 2(b)) including at least one human tissue (e.g., CTL_1d' in FIG. 2(b)) that reflects movement adjustment (i.e., moving).

[0108] For example, at least one filtering element may include a first determining element associated with the interval (e.g., L' in FIG. 2(b)) between predetermined positions (e.g., P1', P2' in FIG. 2(b)) according to at least one human tissue (e.g., cartilage, CTL_1d' in FIG. 2(b)) in the form of a one-dimensional element (1D element) that reflects position adjustment.

[0109] Here, the first position included in the predetermined positions (P1 in FIG. 2(a), P1' in FIG. 2(b)) corresponds to the Nasion portion of the nose, and the second position (P2 in FIG. 2(a), P2' in FIG. 2(b)) may correspond to the Tip portion of the nose.

[0110] For example, at least one filtering element may include a second determining element associated with a predetermined thickness (e.g., d' in FIG. 2(b)) according to at least one human tissue (e.g., cartilage, CTL_1d' in FIG. 2(b)) in which position adjustment is reflected in the form of a one-dimensional element (1D element).

[0111] For example, at least one filtering element may include a third determining element associated with the shape (i.e., curvature) of the affected area according to at least one human tissue (e.g., cartilage, CTL_1d' in FIG. 2(b)) in which position adjustment is reflected in the form of a one-dimensional element (1D element).

[0112] For example, at least one filtering element mentioned in the present embodiment may be implemented to include only one of the first to third determination elements, include the first determination element and the second determination element, or include all of the first to third determination elements.

[0113] In step S550, the customized implant recommendation device based on hybrid modeling according to the present embodiment can perform filtering on a pre-stored implant library based on at least one filtering element.

[0114] Accordingly, information on a list of recommended implants for the affected area for plastic surgery (e.g., the patient's nose) can be generated.

[0115] For example, the recommended implant list information may include specification information for one or more prefabricated implants filtered according to specification information for prefabricated implants stored in the implant library using at least one filtering element.

[0116] In this case, it will be understood that one or more recommended implant information items may display one or more prefabricated implants aligned according to predetermined weights.

[0117] According to the present specification, by more accurately implementing a function to recommend a customized implant for a patient based on simulation results of multiple human tissues associated with the affected area (e.g., nose) after plastic surgery, it becomes possible to establish a more sophisticated surgical plan for plastic surgery and also facilitate the prediction of surgical results.

[0118] FIG. 6 is an illustrative diagram for explaining an example of a filtering operation by a customized implant recommendation device based on hybrid modeling according to the present embodiment.

[0119] Referring to FIGS. 1 to 6, the second 3D image information (6) of FIG. 6 may correspond to the second 3D image information including at least one human tissue (e.g., CTL_1d' of FIG. 2(b)) that has position adjustments reflected to satisfy the plastic surgery plan.

[0120] Meanwhile, the recommended implant list information (600) of FIG. 6 may correspond to the result of performing filtering on a pre-stored implant library using a first determination factor associated with the interval (L') between predetermined positions (P1', P2') according to at least one human tissue (CTL_1d') that reflects position adjustment.

[0121] For example, when the interval (L') between predetermined positions (P1', P2') associated with the first determination factor is set to '50 mm', existing implant models that exceed a preset threshold value (e.g., ±1 mm) based on '50 mm' can be filtered out in the recommended implant list information (600).

[0122] For example, among the first to fourth implants (e.g., 410 to 440 in FIG. 4) mentioned through FIG. 4, the first implant (e.g., 410 in FIG. 4) and the second implant (e.g., 420 in FIG. 4) corresponding to a pre-set implant model that exceeds a threshold value (e.g., ±1mm) based on the interval (L'=50mm) between predetermined positions (P1', P2') can be filtered from the recommended implant list information (600).

[0123] That is, the recommended implant list information (600) may include one or more prefabricated implants (I_r1~I_r6) that are within a threshold value (e.g., ±1mm) set based on the interval (L'=50mm) between predetermined positions (P1', P2') by referring to specification information regarding prefabricated implants stored in the implant library.

[0124] In this case, thickness information (B1~B6) can be pre-mapped to each of one or more prefabricated implants (I_r1~I_r6) of Fig. 6.

[0125] FIG. 7 is an illustrative diagram showing another example of a filtering operation by a customized implant recommendation device based on hybrid modeling according to the present embodiment.

[0126] Referring to FIGS. 1 to 7, the recommended implant list information (700) of FIG. 7 may correspond to the result of filtering using a second determination element associated with a predetermined thickness (e.g., d' in FIG. 2(b)) according to at least one human tissue (CTL_1d' in FIG. 2(b)) with position adjustment reflected, for a pre-stored implant library or list information filtered according to a first determination element (e.g., 600 in FIG. 6).

[0127] For example, the recommended implant list information (700) of FIG. 7 may include specification information for one or more prefabricated implants (I_r1 to I_r3) that do not exceed a predetermined thickness (e.g., d' in FIG. 2(b)) according to at least one human tissue (e.g., cartilage, CTL_1d' in FIG. 2(b)) that satisfies the first determining factor and reflects position adjustment.

[0128] Furthermore, in order to derive a prefabricated implant having the most suitable curvature for the shape (i.e., curvature) information (LINE_S) of the affected area according to at least one human tissue (CTL_1d') reflecting positional and shape adjustments, one or more shape information (L_l1~L_l3) corresponding to the bottom surface of the implant may be pre-mapped to each of one or more prefabricated implants (I_r1~I_r3).

[0129] In this case, the shape (i.e., curvature) information (LINE_S) of the affected area corresponding to at least one human tissue (CTL_1d') with position and shape adjustments, and one or more shape information (L_l1~L_l3) corresponding to the bottom surface of each of one or more prefabricated implants (I_r1~I_r3) can be obtained using a widely known Boolean algorithm.

[0130] For example, the first shape information (L_l1) can be understood as shape information suitable for cases where the patient's glabella needs to be raised through plastic surgery.

[0131] For example, the second shape information (L_l2) can be understood as shape information suitable for cases where both the glabella and the tip of the nose of a patient need to be raised through plastic surgery.

[0132] For example, third shape information (L_l3) can be understood as shape information suitable for cases where the tip of the patient's nose needs to be raised through plastic surgery.

[0133] According to the present embodiment, a customized implant recommendation device based on hybrid modeling can individually compare shape information (i.e., curvature) (LINE_S) of a lesion according to at least one human tissue (CTL_1d') that reflects position adjustment and shape adjustment, and one or more shape information (L_l1~L_l3) included in list information (e.g., 700) filtered through a first determination element and a second determination element.

[0134] Ultimately, it will be understood that the recommended implant list information according to the present specification may include specification information regarding a prefabricated implant having the highest degree of correspondence with the shape information (i.e., curvature) (LINE_S) of the affected area for at least one human tissue (CTL_1d') that reflects positional and shape adjustments among one or more shape information (L_l1~L_l3).

[0135] In this case, it will be understood that one or more ready-made implants on the recommended implant list information may be sorted in order according to predetermined weights (length, thickness, shape).

[0136] FIG. 8 is a conceptual diagram illustrating data processing for position adjustment according to the present embodiment.

[0137] Referring to FIGS. 1 to 8, the first cross-section (810) of the first 3D image information may be divided into a first region (R1) associated with a solid region and a second region (R2) associated with a hollow region.

[0138] For example, the first region (R1) of FIG. 8 corresponds to a spherical shape (900) as described in FIG. 9, and can be represented in the form of a tri-angle mesh with three vertices and one vector as basic units.

[0139] For example, the second region (R2) of Fig. 8 corresponds to an empty region, but depending on the implementation method, it may be represented as a filled region.

[0140] Meanwhile, the second 3D image information (820) according to the present embodiment can be obtained by using a unit cell with respect to the first 3D image information (810) to perform data processing for position adjustment associated with at least one human tissue.

[0141] For example, at least one human tissue represented in the form of a tri-angle mesh on the first 3D image information can be converted into a 1D element with two nodes and one beam as basic units through the aforementioned data processing.

[0142] In other words, the second 3D image information may include at least one human tissue that has been converted into a 1D element form with two nodes and one beam as basic units through the aforementioned data processing.

[0143] FIG. 9 is an example diagram showing first 3D image information prior to data processing for position adjustment according to the present embodiment.

[0144] Referring to FIG. 9, it will be understood that the spherical surface (900) corresponding to the solid region of FIG. 8 described above can be represented in the form of a tri-angle mesh with three vertices and one vector as basic units.

[0145] FIG. 10 is an example diagram showing a unit cell for data processing for position adjustment according to the present embodiment.

[0146] Referring to FIG. 10(a), the unit cell according to the present embodiment can be represented in the form of a one-dimensional element (1D element) having two nodes (N1, N1') and one beam (B1) as basic units.

[0147] For example, a unit cell according to the present embodiment can be implemented through a connection relationship using a plurality of nodes and a plurality of beams according to the form of a 1D element.

[0148] Referring to FIG. 10(b), based on the connection relationship using a plurality of nodes and a plurality of beams according to FIG. 10(a), a unit cell can be represented in a three-dimensional shape as in FIG. 10(b).

[0149] However, it will be understood that the unit cell mentioned in this specification can be implemented in various shapes through various connection relationships using multiple nodes and multiple beams, not only the shape of FIG. 10 (b) according to the connection relationship of FIG. 10 (a).

[0150] FIG. 11 is an example diagram illustrating the process of performing data processing for position adjustment using a unit cell according to the present embodiment.

[0151] Here, the volume region (1100_S) of the 3D rendering image according to the first 3D image information of FIG. 11 (a) can be associated with a solid region represented in the form of a tri-angle mesh mentioned in FIG. 8.

[0152] Referring to FIG. 11 (a), the volume region (1100_S) of the 3D rendering image corresponding to the first 3D image information of FIG. 11 (a) can be replaced by a unit cell corresponding to FIG. 10 described above.

[0153] Referring to FIG. 11 (b), the volume region (1100_S') of the 3D rendering image corresponding to the first 3D image information replaced by a unit cell can be represented according to a one-dimensional element (1D element) expressed by a connection relationship using a plurality of nodes and a plurality of beams.

[0154] Ultimately, the volume region (1100_S') of the 3D rendering image corresponding to the first 3D image information replaced by the unit cell may correspond to the second human tissue (e.g., CTL_1d in FIG. 2(a)) represented by a 1D element, the shape of the affected area (e.g., nose) which can be changed into a shape desired by the patient.

[0155] FIG. 12 is a block diagram illustrating a computing environment including a computing device suitable for use in an exemplary embodiment.

[0156] Referring to FIGS. 1 to 12, in the illustrated embodiments, each component may have different functions and capabilities in addition to those described below, and may include additional components in addition to those not described below.

[0157] The illustrated computing environment (10) includes a computing device (12). In one embodiment, the computing device (12) may correspond to a custom implant recommendation device (e.g., 300 of FIG. 3) mentioned herein.

[0158] The computing device (12) includes at least one processor (14), a computer-readable storage medium (16), and a communication bus (18). The processor (14) can enable the computing device (12) to operate according to the exemplary embodiment mentioned above.

[0159] For example, the processor (14) may execute one or more programs stored in a computer-readable storage medium (16). The one or more programs may include one or more computer-executable instructions, and the computer-executable instructions may be configured to cause the computing device (12) to perform operations according to an exemplary embodiment when executed by the processor (14).

[0160] A computer-readable storage medium (16) is configured to store computer-executable instructions or program code, program data and / or other suitable forms of information. A program (20) stored in the computer-readable storage medium (16) includes a set of instructions executable by a processor (14).

[0161] In one embodiment, the computer-readable storage medium (16) may be a memory (volatile memory such as random access memory, non-volatile memory, or a suitable combination thereof), one or more magnetic disk storage devices, optical disk storage devices, flash memory devices, other forms of storage media that can be accessed by the computing device (12) and store desired information, or a suitable combination thereof.

[0162] The communication bus (18) interconnects various other components of the computing device (12), including the processor (14) and the computer-readable storage medium (16).

[0163] The computing device (12) may also include one or more input / output interfaces (22) and one or more network communication interfaces (26) that provide interfaces for one or more input / output devices (24). The input / output interfaces (22) and network communication interfaces (26) are connected to a communication bus (18).

[0164] The input / output device (24) may be connected to other components of the computing device (12) through the input / output interface (22). An exemplary input / output device (24) may include input devices such as a pointing device (mouse or trackpad, etc.), a keyboard, a touch input device (touchpad or touchscreen, etc.), a voice or sound input device, various types of sensor devices and / or shooting devices, and / or output devices such as a display device, a printer, a speaker and / or a network card.

[0165] An exemplary input / output device (24) may be included inside the computing device (12) as a component constituting the computing device (12), or it may be connected to a customized implant recommendation device (e.g., 300 in FIG. 3) based on a pre-established communication protocol as a separate device distinct from the computing device (12).

[0166] Although embodiments of the present invention have been described above with reference to the attached drawings, those skilled in the art will understand that the present invention may be implemented in other specific forms without changing its technical concept or essential features. Therefore, the embodiments described above should be understood as illustrative in all respects and not restrictive.

[0167] Although specific embodiments have been described in the detailed description of this specification, various modifications are possible within the scope of this specification. Therefore, the scope of this specification should not be limited to the embodiments described above, but should be defined by the claims set forth below as well as equivalents to the claims of this invention. Explanation of the symbols

[0168] 300: Customized implant recommendation device 310: Simulation Module 320: Data processing module 330: Position adjustment module 340: Implant Recommendation Module

Claims

Claim 1 A method of operation for a customized implant recommendation device based on hybrid modeling, comprising: generating first 3D image information including a plurality of human tissues associated with a surgical site for plastic surgery, wherein the plurality of human tissues include a first human tissue corresponding to a nasal bone and a second human tissue corresponding to nasal cartilage, wherein the first 3D image information is implemented to enable shape adjustment of the plurality of human tissues, and wherein the first 3D image information follows an STL (Standard Triangulated Language) file format in which the first human tissue and the second human tissue are both expressed in a tri-angle mesh form having three vertices and one vector as basic units; and generating second 3D image information through data processing for position adjustment of the second human tissue based on the first 3D image information, wherein the data processing defines a volume region associated with a solid region of the second human tissue among the 3D rendering images corresponding to the first 3D image information as a connection relationship of a plurality of nodes and a plurality of beams. The method comprises the step of converting the second human tissue from the triangle mesh form to a one-dimensional element form by replacing it with a unit cell, wherein the one-dimensional element form is defined with two nodes and one beam as basic units, and wherein the second 3D image information is implemented such that the first human tissue, which maintains the triangle mesh form and allows only shape adjustment, and the second human tissue, which is converted to the one-dimensional element form and allows both shape adjustment and position adjustment, coexist on a single 3D image information;A step of performing the position adjustment for the second human tissue based on the second 3D image information, wherein the position adjustment is performed by moving a node at a second position corresponding to the tip of the nose among a plurality of nodes constituting the second human tissue converted into the form of a one-dimensional element to a position that satisfies the plastic surgery plan; A method comprising the step of generating recommended implant list information by performing filtering on a pre-stored implant library based on second 3D image information reflecting the position adjustment and at least one preset filtering element, wherein the at least one filtering element includes a first determination element associated with the distance between a first position corresponding to the nasion of the nose and a second position corresponding to the tip of the nose for the second human tissue reflecting the position adjustment, and a second determination element associated with the vertical distance from the second human tissue reflecting the position adjustment to the surface of the nasal skin, and the step of generating the recommended implant list information comprises: a step of performing a first filtering to exclude pre-fabricated implants having length information exceeding a preset threshold value based on the distance according to the first determination element among the specification information of pre-fabricated implants pre-stored in the implant library; and a step of performing a second filtering to exclude pre-fabricated implants having thickness information exceeding the vertical distance according to the second determination element among the pre-fabricated implants filtered in the first filtering. Claim 2 The method according to claim 1, wherein the step of generating the first 3D image information comprises: a step of performing a boundarying operation using a partial 2D image with respect to a plurality of medical image images obtained from a pre-linked CT device or MRI device, using one or more threshold values ​​set differently for each of the plurality of human tissues; and a step of converting selected brightness values ​​among the values ​​classified through the boundarying operation into an STL file, and integrating one or more converted STL files to implement a 3D rendering image in a simulation. Claim 3 delete Claim 4 The method according to claim 1, wherein the at least one filtering element further includes a third determining element associated with the curvature of the affected area according to the second human tissue reflecting the position adjustment and the shape adjustment, and each of the one or more prefabricated implants that are secondarily filtered has one or more shape information corresponding to the bottom surface of the one or more prefabricated implants pre-mapped thereto, and the step of generating the recommended implant list information further includes: a step of obtaining curvature information of the affected area according to the third determining element from the second human tissue reflecting the position adjustment and the shape adjustment; and a step of individually comparing the one or more shape information pre-mapped to each of the one or more prefabricated implants with the curvature information to derive specification information regarding the prefabricated implant having the shape information with the highest match rate with the curvature information among the one or more shape information. Claim 5 A method according to claim 1, wherein the recommended implant list information includes specification information for one or more prefabricated implants filtered according to specification information for prefabricated implants stored in the implant library using the at least one filtering element, and wherein the one or more prefabricated implants are sorted in order according to predetermined weights on the recommended implant list information. Claim 6 A simulation module that generates first 3D image information including a plurality of human tissues associated with a surgical site for plastic surgery and is implemented to enable shape adjustment of the plurality of human tissues; a data processing module that generates second 3D image information through data processing for position adjustment of a second human tissue based on the first 3D image information; and a position adjustment module that performs the position adjustment of the second human tissue based on the second 3D image information. The method includes an implant recommendation module that generates recommended implant list information by performing filtering on a pre-stored implant library based on second 3D image information reflecting the position adjustment and at least one preset filtering element, wherein the plurality of human tissues includes a first human tissue corresponding to the nasal bone and the second human tissue corresponding to the nasal cartilage, and the first 3D image information follows an STL (Standard Triangulated Language) file format in which both the first human tissue and the second human tissue are expressed in a tri-angle mesh form with three vertices and one vector as basic units, and the data processing module converts the second human tissue from the tri-angle mesh form to a 1D element form by replacing the volume region associated with the solid region of the second human tissue among the 3D rendering images corresponding to the first 3D image information with a unit cell defined by the connection relationship of a plurality of nodes and a plurality of beams. The above one-dimensional element shape is defined with two nodes and one beam as basic units, and the above second 3D image information is the above first human tissue in which the above tri-angle mesh shape is maintained and only shape adjustment is possible, andThe second human tissue, which is converted into the above-mentioned one-dimensional element form and capable of both shape adjustment and position adjustment, is implemented to coexist on a single 3D image information; the position adjustment module performs the position adjustment by moving a node at a second position corresponding to the tip of the nose among a plurality of nodes constituting the second human tissue converted into the above-mentioned one-dimensional element form to a position that satisfies the plastic surgery plan; the at least one filtering element includes a first determination element associated with the gap between a first position corresponding to the nasion of the nose and the second position corresponding to the tip of the nose for the second human tissue reflecting the position adjustment, and a second determination element associated with the vertical gap from the second human tissue reflecting the position adjustment to the surface of the nasal skin; the implant recommendation module performs a first filtering by excluding pre-fabricated implants having length information exceeding a preset threshold value based on the gap according to the first determination element from the specification information of pre-fabricated implants pre-stored in the implant library; and among the pre-fabricated implants filtered in the first step, the second A customized implant recommendation device based on hybrid modeling that performs secondary filtering to exclude prefabricated implants having thickness information exceeding the vertical spacing according to the determining factor. Claim 7 A customized implant recommendation device based on hybrid modeling according to claim 6, wherein the simulation module is implemented to perform a boundarying operation using partial 2D images with respect to a plurality of medical imaging images obtained from a pre-linked CT device or MRI device, using one or more threshold values ​​set differently for each of the plurality of human tissues, convert selected brightness values ​​among the values ​​classified through the boundarying operation into STL files, and integrate one or more converted STL files to implement a 3D rendering image in the simulation. Claim 8 A customized implant recommendation device based on hybrid modeling, wherein, in claim 7, the unit cell is represented as a three-dimensional shape through a connection relationship using the plurality of nodes and the plurality of beams according to the one-dimensional element shape, and the volume region is replaced by the plurality of unit cells. Claim 9 A customized implant recommendation device based on hybrid modeling according to claim 8, wherein the at least one filtering element further includes a third determining element associated with the curvature of the affected area according to the second human tissue reflecting the position adjustment and the shape adjustment, and each of the one or more prefabricated implants that are secondarily filtered has one or more shape information corresponding to the bottom surface of the one or more prefabricated implants pre-mapped, and the implant recommendation module is implemented to obtain curvature information of the affected area according to the third determining element from the second human tissue reflecting the position adjustment and the shape adjustment, and to individually compare the one or more shape information pre-mapped to each of the one or more prefabricated implants with the curvature information, and to derive specification information regarding the prefabricated implant having the shape information with the highest match rate with the curvature information among the one or more shape information. Claim 10 A customized implant recommendation device based on hybrid modeling according to claim 6, wherein the recommended implant list information includes specification information for one or more prefabricated implants filtered according to specification information for prefabricated implants stored in the implant library using the at least one filtering element, and wherein the one or more prefabricated implants are sorted in order according to predetermined weights on the recommended implant list information.

Images