Method and apparatus for providing treatment recommendation information for breast implant

Abstract

According to some embodiments of the present disclosure, a method for providing treatment recommendation information for a breast implant inserted into the body is disclosed, which is performed by a computing device. The method may include: a step of determining at least one of a side effect item or a severity item related to said side effect item based on analysis information regarding a breast implant inserted into the body—wherein the side effect item is determined as one or two items corresponding to said analysis information from a predetermined list of side effect classifications, and the severity item is determined as one item corresponding to said analysis information from a predetermined list of severity classifications—; and a step of determining treatment recommendation information from a predetermined list of treatment classifications based on at least some of said side effect item and said severity item.

Description

Technology Field

[0001] The present invention relates to a medical information provision system, and more specifically, to a method and apparatus for providing appropriate treatment recommendation information through systematic decision-making according to the type and severity of side effects occurring in breast implants inserted into the body. Background Technology

[0002] Implants are devices inserted into the body for medical or cosmetic purposes. Various types of implants exist, including breast implants, joint replacement implants, and dental implants. These implants may be inserted to assist a patient's physical function or to improve their appearance. However, implanted implants can cause various side effects over time. For example, implant rupture, deformation, inflammatory reactions within tissues, and capsular contracture are reported as typical side effects. If these side effects are not detected early, they can have a serious impact on the patient's health.

[0003] Regular checkups are essential after the insertion of implants. Ultrasound is one of the primary diagnostic methods used for this purpose. Ultrasound has the advantage of providing real-time images without radiation exposure. Therefore, it is widely used as a non-invasive method to check the condition of the implants.

[0004] Even if side effects such as implant rupture or capsular thickening are detected through ultrasound, determining the appropriate treatment is another complex issue. The appropriate treatment can be determined by comprehensively considering various factors, such as the type and severity of the side effect, as well as combinations of multiple side effects. For example, even for the same side effect of capsular thickening, treatment options can vary widely, ranging from observation to complex reconstructive surgery, depending on the patient's symptoms and the severity of the condition. When treatment relies solely on the clinician's subjective experience due to the absence of standardized guidelines, this can lead to inconsistencies in treatment outcomes.

[0005] Therefore, there is a demand for methods and devices that can more precisely monitor the condition of implants inserted into the body, detect potential side effects early, and assist in treatment. Prior art literature

[0006] Republic of Korea Registered Patent 10-2535865 The problem to be solved

[0007] The present disclosure is conceived in response to the aforementioned background technology and aims to provide a method and apparatus for providing appropriate treatment recommendation information through systematic decision-making according to the type and severity of side effects occurring in breast implants inserted into the body. means of solving the problem

[0008] A method for providing treatment recommendation information for a breast implant inserted into the body, performed by a computing device for realizing the aforementioned task, is disclosed. The method may include: a step of determining at least one of a side effect item or a severity item related to said side effect item based on analysis information regarding a breast implant inserted into the body—wherein the side effect item is determined as one or two items corresponding to said analysis information from a predetermined list of side effect classifications, and the severity item is determined as one item corresponding to said analysis information from a predetermined list of severity classifications—; and a step of determining treatment recommendation information from a predetermined list of treatment classifications based on at least some of said side effect item and said severity item.

[0009] Alternatively, the step of determining treatment recommendation information from a predetermined treatment classification list based on at least some of the above side effect items and the above severity items may include: determining a grid distinguished by the side effect item among a plurality of grids included in a decision matrix—the decision matrix includes a plurality of grids distinguished by columns and rows composed of items included in the above side effect classification list, each of the plurality of grids includes one or more items included in the above treatment classification list, and some of the one or more items included in the above treatment classification list are conditionally associated with one item included in the above severity classification list related to the side effect item—; and determining one of the one or more treatment items included in the determined grid as treatment recommendation information.

[0010] Alternatively, the step of determining one of the one or more treatment items included in the determined grid as treatment recommendation information may include: determining as treatment recommendation information an item corresponding to a side effect item determined based on the analysis information and a severity item related to the side effect item among the one or more treatment items included in the determined grid.

[0011] Alternatively, the above-determined list of adverse event classifications may include at least one of folding (F), fluid collection (FC), thickened capsule (TC), unspecific subcapsular deposit (USD), rupture (Rupture 1, R1) in which no snowstorm sign is observed on ultrasound and silicone has not leaked into the capsule, rupture (Rupture 2, R2) in which a snowstorm sign is observed on ultrasound and silicone has leaked into the capsule, and calcified capsule.

[0012] Alternatively, the above-determined severity classification list may include at least one of Minor, Severe, and Symptomatic.

[0013] Alternatively, the above-determined list of treatment classifications may include at least one of follow-up ('F / U'), differential diagnosis of anaplastic large cell lymphoma (BIA-ALCL) through cytology and culture, revision without capsulectomy, revision with capsulectomy, capsulotomy, and manual reduction.

[0014] Alternatively, some of the items included in the above-determined treatment classification list may form a hierarchy in the order of observation, capsulotomy, revision without capsulectomy, and revision with capsulectomy, depending on the above-determined side effect item and the above-determined severity item.

[0015] Alternatively, determining a differential diagnosis of anaplastic large cell lymphoma (BIA-ALCL) through cytology and culture using the above treatment recommendation information may be performed when the cell type of the breast implant is a macrotexture type.

[0016] Alternatively, determining the passive reduction based on the above treatment recommendation information may be performed when the cell type of the breast implant is a smooth type.

[0017] Alternatively, the step of determining at least one of a side effect item or a severity item related to the side effect item based on analysis information regarding the breast implant inserted into the body may include: determining Severe as a severity item related to the folding (F) in response to identifying a folding (F) that is multiple, symptomatic, or accompanied by pain in the palpable shell from the analysis information.

[0018] Alternatively, the step of determining at least one of a side effect item or a severity item related to the side effect item based on analysis information regarding the breast implant inserted into the body may include: determining the seroma (FC) as the side effect item and determining "Severe" as the severity item related to the seroma (FC) in response to identifying from the analysis information a seroma (FC) in which the fluid volume exceeds 30cc, is accompanied by clinical symptoms in the breast, or has not been self-limited for more than 3 months;

[0019] Alternatively, the step of determining at least one of a side effect item or a severity item related to the side effect item based on analysis information regarding the breast implant inserted into the body may include: determining the capsule thickening (TC) as the side effect item and determining severe as the severity item related to the capsule thickening (TC) in response to identifying from the analysis information that a patient exhibiting clinical symptoms has all of hardness, pain, and breast shape change, or that the total thickness of the capsule exceeds 1 mm.

[0020] Alternatively, the step of determining one of the one or more treatment items included in the determined grid as treatment recommendation information may further include: a step of additionally determining explantation as treatment recommendation information among revisions with capsulectomy in response to determining revision with capsulectomy as treatment recommendation information according to the side effect item of capsule thickening (TC) and the severity item of severe (Severe).

[0021] Alternatively, the step of determining at least one of a side effect item or a severity item related to the side effect item based on analysis information regarding the breast implant inserted into the body may include: determining capsular calcification (CC) as the side effect item and determining severe as the severity item related to the capsular calcification (CC) in response to identifying from the analysis information a condition in which the condition and type of the implant shell cannot be confirmed due to capsular calcification (CC);

[0022] Alternatively, the above decision matrix may be Table 1 below.

[0023] [Table 1]

[0024]

[0025] Here, 'F' stands for Folding, 'FC' for Fluid Collection, 'TC' for Thickened Capsule, 'USD' for Unspecific Subcapsular Deposit, 'R1' for a rupture where no 'snowstorm sign' is observed on ultrasound indicating no silicone leakage into the capsule, 'R2' for a rupture where a 'snowstorm sign' is observed on ultrasound indicating silicone leakage into the capsule, 'CC' for Calcified Capsule, 'F / U' for Follow-up, 'Opt. 1' for differential diagnosis of Anaplastic Large Cell Lymphoma (BIA-ALCL) through cytology and culture, and 'Opt. 2' is revision without capsulectomy, 'Opt. 3' is revision with capsulectomy, 'Opt. 4' is capsulotomy, 'Opt. 5' is manual reduction, 'severe' is severe, and 'Symptomatic' is symptomatic.

[0026] A computer program stored on a computer-readable storage medium for solving the problem described above is disclosed. The computer program includes instructions to cause one or more processors to perform a method of providing treatment recommendation information for a breast implant inserted into the body, and the method may include: a step of determining at least one of a side effect item or a severity item related to said side effect item based on analysis information regarding a breast implant inserted into the body—said that the side effect item is determined as one or two items corresponding to said analysis information from a predetermined list of side effect classifications, and said severity item is determined as one item corresponding to said analysis information from a predetermined list of severity classifications—; and a step of determining treatment recommendation information from a predetermined list of treatment classifications based on at least some of said side effect item and said severity item.

[0027] A computing device is disclosed for performing a method of providing treatment recommendation information for a breast implant inserted into the body to solve the problem described above. The computing device comprises: a processor; and the processor may perform: an operation of determining at least one of a side effect item or a severity item related to said side effect item based on analysis information regarding a breast implant inserted into the body - said side effect item is determined as one or two items corresponding to said analysis information from a predetermined list of side effect classifications, and said severity item is determined as one item corresponding to said analysis information from a predetermined list of severity classifications -; and an operation of determining treatment recommendation information from a predetermined list of treatment classifications based on at least some of said side effect item and said severity item. Effects of the invention

[0028] The present disclosure may provide a method and apparatus for providing appropriate treatment recommendation information through systematic decision-making according to the type and severity of side effects occurring in breast implants inserted into the body. Brief explanation of the drawing

[0029] FIG. 1 is a block diagram of a computing device for performing a method of providing treatment recommendation information for a breast implant inserted into the body according to some embodiments of the present disclosure. FIG. 2 illustrates an exemplary structure of an artificial intelligence-based model according to some embodiments of the present disclosure. FIG. 3 is a schematic diagram of a medical information analysis system according to some embodiments of the present disclosure. FIG. 4 illustrates an exemplary determination matrix according to some embodiments of the present disclosure. FIG. 5 is a flowchart of a method for providing treatment recommendation information for breast implants inserted into the body according to some embodiments of the present disclosure. FIG. 6 is a brief and general schematic diagram of an exemplary computing environment in which embodiments of the present disclosure may be implemented. Specific details for implementing the invention

[0030] Various embodiments and / or aspects are now disclosed with reference to the drawings. For illustrative purposes, numerous specific details are disclosed in the following description to aid in a general understanding of one or more aspects. However, it will be apparent to those skilled in the art that these aspects may be practiced without such specific details. The following description and the accompanying drawings describe specific exemplary aspects of one or more aspects in detail. However, these aspects are exemplary, and some of the various methods in the principles of the various aspects may be used, and the descriptions are intended to include all such aspects and their equivalents. Specifically, terms such as “exemplary,” “example,” “aspect,” and “example” as used herein may not be interpreted as implying that any described aspect or design is superior or advantageous over other aspects or designs.

[0031] Hereinafter, identical or similar components are assigned the same reference numeral regardless of drawing symbols, and redundant descriptions thereof are omitted. Furthermore, in describing the embodiments disclosed in this specification, detailed descriptions of related prior art are omitted if it is determined that such detailed descriptions may obscure the essence of the embodiments disclosed in this specification. Additionally, the attached drawings are intended only to facilitate understanding of the embodiments disclosed in this specification, and the technical concept disclosed in this specification is not limited by the attached drawings.

[0032] The terms used herein are for describing the embodiments and are not intended to limit the disclosure. In this specification, the singular form includes the plural form unless specifically stated otherwise in the text. As used herein, "comprises" and / or "comprising" do not exclude the presence or addition of one or more other components in addition to the components mentioned.

[0033] Unless otherwise defined, all terms used in this specification (including technical and scientific terms) may be used in a meaning commonly understood by those skilled in the art to which this disclosure pertains. Additionally, terms defined in commonly used dictionaries are not to be interpreted ideally or excessively unless explicitly and specifically defined otherwise.

[0034] Furthermore, the term "or" is intended to mean an implicit "or" rather than an exclusive "or." That is, unless otherwise specified or evident from the context, "X uses A or B" is intended to mean one of the natural implicit substitutions. In other words, if X uses A; if X uses B; or if X uses both A and B, "X uses A or B" may apply to any of these cases. Additionally, the term "and / or" as used herein should be understood to refer to and include all possible combinations of one or more of the enumerated related items.

[0035] And, the term “at least one of A or B” should be interpreted to mean “a case including only A,” “a case including only B,” or “a combination of A and B.”

[0036] Those skilled in the art should recognize that the various exemplary logical blocks, configurations, modules, circuits, means, logics, and algorithmic steps described in connection with the embodiments disclosed herein may be implemented in electronic hardware, computer software, or a combination of both. To clearly exemplify the interchangeability of hardware and software, various exemplary components, blocks, configurations, means, logics, modules, circuits, and steps have been generally described above in terms of their functionality. Whether such functionality is implemented in hardware or software depends on the specific application and design constraints imposed on the overall system. Skilled technicians may implement the described functionality in various ways for each specific application. However, such decisions regarding implementation should not be construed as going beyond the scope of this disclosure.

[0037] The description of the presented embodiments is provided to enable those skilled in the art to use or practice the present invention. Various modifications to these embodiments will be apparent to those skilled in the art. The general principles defined herein may be applied to other embodiments without departing from the scope of the present disclosure. Thus, the present invention is not limited to the embodiments presented herein. The present invention should be interpreted in the broadest possible scope consistent with the principles and novel features presented herein.

[0038] And, terms such as “~ etc.” as in “A, B, etc.” should be interpreted to mean “cases including only A,” “cases including only B,” or “cases composed of A and B.”

[0039] The purpose and effects of the present disclosure, and the technical configurations for achieving them, will become clear by referring to the embodiments described in detail below in conjunction with the accompanying drawings. In describing the present disclosure, if it is determined that a detailed description of known functions or configurations might unnecessarily obscure the essence of the present disclosure, such detailed description will be omitted. Furthermore, the terms described below are defined considering their functions in the present disclosure, and these may vary depending on the intentions or practices of the user or operator.

[0040] However, the present disclosure is not limited to the embodiments disclosed below but may be implemented in various different forms. These embodiments are provided merely to make the present disclosure complete and to fully inform those skilled in the art of the scope of the disclosure, and the present disclosure is defined only by the scope of the claims. Therefore, such definitions should be based on the content throughout this specification.

[0041] FIG. 1 is a block diagram of a computing device for performing a method of providing treatment recommendation information for a breast implant inserted into the body according to some embodiments of the present disclosure.

[0042] As illustrated in FIG. 1, the computing device (100) may include a processor (110), memory (130), and a network unit (150). The configuration of the computing device (100) illustrated in FIG. 1 is merely a simplified example. In some embodiments of the present disclosure, the computing device (100) may include other configurations for performing the computing environment of the computing device (100), and only some of the disclosed configurations may constitute the computing device (100).

[0043] The processor (110) may be composed of one or more cores and may include processors for data analysis and processing, deep learning, such as a central processing unit (CPU) of a computing device, a general purpose graphics processing unit (GPGPU), or a tensor processing unit (TPU). The processor (110) may perform data conversion, computation, generation, etc., to read a computer program stored in memory (130) and perform a method of providing treatment recommendation information for a breast implant inserted into the body according to some embodiments of the present disclosure. For example, the processor (110) may implement a medical information analysis system (1000) and its components that provide treatment recommendation information for a breast implant inserted into the body. The processor (110) may perform steps for performing a method of providing treatment recommendation information for a breast implant inserted into the body. According to some embodiments of the present disclosure, the processor (110) may perform computations for learning a neural network using training data. For example, the processor (110) may implement an image processing model, an image classification model, an image segmentation model, an object detection model, a natural language processing model, a large-scale language model, etc., for performing a method of providing treatment recommendation information for breast implants inserted into the body according to some embodiments of the present disclosure. The processor (110) may perform calculations for learning a neural network, such as processing input data for learning in deep learning (DL), extracting features from input data, calculating errors, and updating the weights of the neural network using backpropagation. At least one of the CPU, GPGPU, and TPU of the processor (110) may process operations for performing a method of providing treatment recommendation information for breast implants inserted into the body.For example, a CPU and a GPGPU can together process computations to perform a method for providing treatment recommendation information for breast implants inserted into the body. Additionally, in some embodiments of the present disclosure, processors of a plurality of computing devices can be used together to process data transformation, computation, generation, learning of network functions, and data classification using network functions to perform a method for providing treatment recommendation information for breast implants inserted into the body. Furthermore, a computer program executed on a computing device according to some embodiments of the present disclosure may be a CPU, GPGPU, or TPU executable program.

[0044] According to some embodiments of the present disclosure, the memory (130) may store any form of information generated or determined by the processor (110) and any form of information received by the network unit (150). For example, the memory (130) may store data generated during the process of performing a method of providing treatment recommendation information for a breast implant inserted into the body by the processor (110). Additionally, the memory (130) may store data received from the outside during the process of performing a method of providing treatment recommendation information for a breast implant inserted into the body by the processor (110). However, not limited thereto, the memory (130) may store various information for performing a method of providing treatment recommendation information for a breast implant inserted into the body according to some embodiments of the present disclosure.

[0045] According to some embodiments of the present disclosure, the memory (130) may include at least one type of storage medium among a flash memory type, a hard disk type, a multimedia card micro type, a card type memory (e.g., SD or XD memory), RAM (Random Access Memory), SRAM (Static Random Access Memory), ROM (Read-Only Memory), EEPROM (Electrically Erasable Programmable Read-Only Memory), PROM (Programmable Read-Only Memory), magnetic memory, a magnetic disk, and an optical disk. The computing device (100) may operate in conjunction with web storage that performs the storage function of the memory (130) on the internet. The description of the memory described above is merely an example and the present disclosure is not limited thereto.

[0046] A network unit (150) according to some embodiments of the present disclosure may use any known wired or wireless communication system.

[0047] The network unit (150) can transmit and receive information, user interfaces, etc., processed by the processor (110) through communication with other terminals. For example, the network unit (150) can provide a user interface generated by the processor (110) to a client (e.g., a user terminal). In addition, the network unit (150) can receive external input from a user authorized as a client and transmit it to the processor (110). At this time, the processor (110) can process operations such as outputting, modifying, changing, or adding information provided through the user interface based on the external input from the user received from the network unit (150).

[0048] Specifically, for example, the network unit (150) may transmit and receive various information to perform a method of providing treatment recommendation information for breast implants inserted into the body according to some embodiments of the present disclosure. For example, the network unit (150) may receive one or more medical data or analysis information stored in a database. In addition, the network unit (150) may transmit some data generated during the process of performing the method of providing treatment recommendation information for breast implants inserted into the body described below to the outside. For example, the network unit (150) may transmit treatment recommendation information generated based on analysis information to the outside.

[0049] Meanwhile, a computing device (100) according to some embodiments of the present disclosure may include a server as a computing system that transmits and receives information through communication with a client. In this case, the client may be any type of terminal capable of accessing the server. For example, the computing device (100) which is the server may receive a query from a user terminal and generate a single information processing result corresponding to the query. In this case, the computing device (100) which is the server may provide a user interface including the processing result to the user terminal. In this case, the user terminal may output the user interface received from the computing device (100) which is the server and receive or process information through interaction with the user.

[0050] In an additional embodiment, the computing device (100) may include any type of terminal that receives data resources generated from any server and performs additional information processing.

[0051] FIG. 2 illustrates an exemplary structure of an artificial intelligence-based model according to some embodiments of the present disclosure.

[0052] Throughout this specification, artificial intelligence model, artificial intelligence-based model, computational model, neural network, network function, and neural network may be used interchangeably.

[0053] A neural network can be composed of a set of interconnected computational units that may generally be referred to as nodes. These nodes may also be referred to as neurons. A neural network is composed of at least one node. The nodes (or neurons) constituting neural networks may be interconnected by one or more links.

[0054] In a neural network, one or more nodes connected via links can form relative input and output node relationships. The concepts of input and output nodes are relative; any node in an output node relationship with respect to one node may be in an input node relationship with respect to another node, and vice versa. As described above, the input node versus output node relationship can be generated based on links. One or more output nodes may be connected to a single input node via links, and vice versa.

[0055] In a relationship between an input node and an output node connected through a single link, the value of the output node's data can be determined based on the data input to the input node. Here, the link interconnecting the input node and the output node may have a weight. The weight may be variable and may be varied by a user or an algorithm to enable the neural network to perform the desired function. For example, if one or more input nodes are interconnected to a single output node by respective links, the output node's value can be determined based on the values ​​input to the input nodes connected to the output node and the weights set on the links corresponding to each input node.

[0056] As described above, a neural network consists of one or more nodes interconnected through one or more links, forming input-output node relationships within the network. The characteristics of a neural network can be determined by the number of nodes and links within the network, the relationships between the nodes and links, and the weight values ​​assigned to each link. For example, if two neural networks exist with the same number of nodes and links but different weight values ​​for the links, the two neural networks may be recognized as different from each other.

[0057] A neural network can be composed of a set of one or more nodes. A subset of nodes constituting a neural network can form a layer. Some of the nodes constituting a neural network can form a layer based on their distances from an initial input node. For example, a set of nodes with a distance of n from an initial input node can form n layers. The distance from the initial input node can be defined by the minimum number of links that must be traversed to reach that node from the initial input node. However, this definition of a layer is arbitrary for illustrative purposes, and the degree of a layer within a neural network can be defined in a way different from that described above. For example, a layer of nodes may be defined by its distance from a final output node.

[0058] In one embodiment of the present disclosure, a set of neurons or nodes may be defined by the expression a layer.

[0059] Initial input nodes may refer to one or more nodes within a neural network to which data is directly input without passing through links in their relationships with other nodes. Alternatively, in terms of link-based relationships between nodes within the neural network, they may refer to nodes that do not have other input nodes connected by links. Similarly, final output nodes may refer to one or more nodes within a neural network that do not have output nodes in their relationships with other nodes. Furthermore, hidden nodes may refer to nodes constituting the neural network that are neither initial input nodes nor final output nodes.

[0060] A neural network according to one embodiment of the present disclosure may have the number of nodes in the input layer equal to the number of nodes in the output layer, and may be a neural network in which the number of nodes decreases and then increases again as it progresses from the input layer to the hidden layer. Additionally, a neural network according to another embodiment of the present disclosure may have the number of nodes in the input layer less than the number of nodes in the output layer, and may be a neural network in which the number of nodes increases as it progresses from the input layer to the hidden layer. Additionally, a neural network according to yet another embodiment of the present disclosure may have the number of nodes in the input layer greater than the number of nodes in the output layer, and may be a neural network in which the number of nodes decreases as it progresses from the input layer to the hidden layer. A neural network according to yet another embodiment of the present disclosure may be a neural network in which the above-described neural networks are combined.

[0061] An artificial intelligence-based model according to one embodiment of the present disclosure may include a deep neural network (DNN). A deep neural network may refer to a neural network that includes a plurality of hidden layers in addition to an input layer and an output layer. By using a deep neural network, latent structures of data can be identified. That is, latent structures of photos, text, video, voice, protein sequence structures, gene sequence structures, peptide sequence structures, music (e.g., what objects are in the photo, what is the content and emotion of the text, what is the content and emotion of the voice, etc.), and / or binding affinity between peptides and MHCs can be identified. Deep neural networks may include convolutional neural networks (CNN), recurrent neural networks (RNN), autoencoders, restricted Boltzmann machines (RBM), deep belief networks (DBN), Q networks, U networks, Siamese networks, Generative Adversarial Networks (GAN), Transformers, etc. The description of deep neural networks described above is merely illustrative and the present disclosure is not limited thereto.

[0062] The artificial intelligence-based model of the present disclosure may be represented by a network structure of any structure described above, including an input layer, a hidden layer, and an output layer.

[0063] A neural network that can be used in an artificial intelligence-based model of the present disclosure may be trained in at least one of supervised learning, unsupervised learning, semi-supervised learning, transfer learning, active learning, or reinforcement learning. Training of a neural network may be a process of applying knowledge to the neural network to perform a specific operation.

[0064] Neural networks can be trained to minimize the error in their output. The training process involves repeatedly inputting training data into the network, calculating the error between the network's output and the target for the training data, and updating the weights of each node by backpropagating the error from the output layer to the input layer in a direction that reduces the error. In supervised learning, training data is used where the correct answer is labeled for each data point (i.e., labeled training data), whereas in unsupervised learning, the correct answer may not be labeled for each training data point. For instance, in the case of supervised learning for data classification, the training data may consist of data where each training point is labeled with a category. Labeled training data is input into the neural network, and the error can be calculated by comparing the network's output (category) with the labels of the training data. As another example, in the case of unsupervised learning for data classification, the error can be calculated by comparing the input training data with the neural network's output. The calculated error is backpropagated in the neural network (i.e., from the output layer to the input layer), and through backpropagation, the connection weights of each node in each layer of the neural network can be updated. The amount of change in the connection weights of each node being updated can be determined by the learning rate. The neural network's calculation of the input data and the backpropagation of the error can constitute a learning cycle (epoch). The learning rate can be applied differently depending on the number of iterations of the neural network's learning cycle. For example, a high learning rate can be used in the early stages of training to quickly achieve a certain level of performance and increase efficiency, while a low learning rate can be used in the later stages to improve accuracy.

[0065] In the training of neural networks, the training data is generally a subset of the real-world data (i.e., the data intended to be processed using the trained neural network). Consequently, a training cycle may exist where errors decrease on the training data but increase on the real-world data. Overfitting is a phenomenon where the network learns excessively on the training data, leading to increased errors on the real-world data. For example, a neural network trained on yellow cats might fail to recognize cats when seeing anything other than yellow, which can be considered a type of overfitting. Overfitting can act as a cause for increased errors in machine learning algorithms. Various optimization methods can be used to prevent this overfitting. To prevent overfitting, methods such as increasing the training data, regularization, dropout (which disables some nodes in the network during training), and the use of batch normalization layers can be applied.

[0066] A computer-readable medium storing a data structure according to one embodiment of the present disclosure is disclosed. The aforementioned data structure may be stored in a storage unit in the present disclosure, executed by a processor, and transmitted and received by a communication unit.

[0067] A data structure can refer to the organization, management, and storage of data that enables efficient access and modification of data. A data structure can refer to the organization of data for solving specific problems (e.g., data analysis, data retrieval, data storage, data modification). A data structure may also be defined by physical or logical relationships between data elements designed to support specific data processing functions. Logical relationships between data elements may include connections between user-defined data elements. Physical relationships between data elements may include actual relationships between data elements physically stored on a computer-readable storage medium (e.g., a permanent storage device). Specifically, a data structure may include sets of data, relationships between data, and functions or instructions applicable to the data. Through an effectively designed data structure, a computing device can perform operations while minimizing the use of the device's resources. Specifically, through an effectively designed data structure, a computing device can increase the efficiency of operations, reading, insertion, deletion, comparison, exchange, and retrieval.

[0068] Data structures can be classified into linear and non-linear data structures based on their form. A linear data structure is one where only one piece of data is connected to the next. Linear data structures can include lists, stacks, queues, and deques. A list can refer to a set of data that maintains an internal order. Lists can include linked lists. A linked list is a data structure where data is connected in a line, with each piece of data possessing a pointer. In a linked list, the pointer can contain information regarding the connection to the next or previous data. Depending on its form, a linked list can be represented as a singly linked list, a doubly linked list, or a circular linked list. A stack is a data arrangement structure that allows for restricted access to data. A stack can be a linear data structure where data can be processed (e.g., insertion or deletion) only at one end. Data stored in a stack can be a Last-In, First-Out (LIFO) data structure, meaning that the later an item is entered, the sooner it is retrieved. A queue is a data sequence structure that allows for limited access to data; unlike a stack, it can be a FIFO (First in First Out) data structure where data stored later is retrieved later. A deque is a data structure that can process data at both ends.

[0069] Non-linear data structures can be structures where multiple data are connected after a single data. Non-linear data structures may include graph data structures. A graph data structure can be defined by vertices and edges, and an edge may include a line connecting two different vertices. Graph data structures may include tree data structures. A tree data structure may be a data structure where there is only one path connecting two different vertices among the multiple vertices included in the tree. In other words, it may be a data structure that does not form a loop in a graph data structure.

[0070] Throughout this specification, the terms artificial intelligence-based model, computational model, neural network, network function, and neural network may be used interchangeably. Hereinafter, they will be described uniformly as neural network. A data structure may include a neural network. Furthermore, a data structure including a neural network may be stored on a computer-readable medium. A data structure including a neural network may also include data preprocessed for processing by the neural network, data input to the neural network, weights of the neural network, hyperparameters of the neural network, data obtained from the neural network, activation functions associated with each node or layer of the neural network, loss functions for learning the neural network, etc. A data structure including a neural network may include any of the components disclosed above. That is, a data structure including a neural network may be configured to include all or any combination thereof, such as data preprocessed for processing by the neural network, data input to the neural network, weights of the neural network, hyperparameters of the neural network, data obtained from the neural network, activation functions associated with each node or layer of the neural network, and loss functions for learning the neural network. In addition to the configurations described above, a data structure including a neural network may include any other information that determines the characteristics of the neural network. Furthermore, the data structure may include any form of data used or generated during the computational process of the neural network, and is not limited to the foregoing. A computer-readable medium may include a computer-readable recording medium and / or a computer-readable transmission medium. A neural network may be composed of a set of interconnected computational units that may generally be referred to as nodes. These nodes may also be referred to as neurons. A neural network is composed of at least one node.

[0071] A data structure may include data input to a neural network. A data structure including data input to a neural network may be stored on a computer-readable medium. Data input to a neural network may include training data input during the neural network learning process and / or input data input to a neural network after training is complete. Data input to a neural network may include pre-processed data and / or data subject to pre-processing. Pre-processing may include a data processing process for inputting data into a neural network. Accordingly, a data structure may include data subject to pre-processing and data generated by pre-processing. The aforementioned data structure is merely an example, and the present disclosure is not limited thereto.

[0072] The data structure may include weights of the neural network. (In this specification, weights and parameters may be used interchangeably.) The data structure including the weights of the neural network may be stored on a computer-readable medium. The neural network may include multiple weights. The weights may be variable and may be varied by a user or an algorithm to enable the neural network to perform a desired function. For example, if one or more input nodes are interconnected to a single output node by respective links, the output node may determine the data value output from the output node based on values ​​input to the input nodes connected to the output node and weights set on the links corresponding to each input node. The aforementioned data structure is merely an example and the present disclosure is not limited thereto.

[0073] As an example rather than a limitation, weights may include weights that vary during the neural network learning process and / or weights for which neural network learning is completed. Weights that vary during the neural network learning process may include weights at the start of the learning cycle and / or weights that vary during the learning cycle. Weights for which neural network learning is completed may include weights for which the learning cycle is completed. Accordingly, a data structure containing the weights of a neural network may include a data structure containing weights that vary during the neural network learning process and / or weights for which neural network learning is completed. Therefore, the weights and / or combinations of each weight described above are included in the data structure containing the weights of a neural network. The aforementioned data structure is merely an example and the present disclosure is not limited thereto.

[0074] Data structures containing the weights of a neural network may be stored on a computer-readable storage medium (e.g., memory, hard disk) after undergoing a serialization process. Serialization may be a process of converting a data structure into a form that can be stored on the same or different computing devices and later reconstructed for use. A computing device may serialize the data structure to transmit and receive data over a network. A serialized data structure containing the weights of a neural network may be reconstructed on the same or different computing devices through deserialization. Data structures containing the weights of a neural network are not limited to serialization. Furthermore, data structures containing the weights of a neural network may include data structures designed to increase computational efficiency while minimizing the use of computing device resources (e.g., B-Tree, R-Tree, Trie, m-way search tree, AVL tree, Red-Black Tree in non-linear data structures). The foregoing is merely an example and the present disclosure is not limited thereto.

[0075] The data structure may include hyperparameters of the neural network. The data structure including the neural network hyperparameters may be stored on a computer-readable medium. The hyperparameters may be variables that are varied by the user. The hyperparameters may include, for example, a learning rate, a cost function, the number of learning cycle iterations, weight initialization (e.g., setting the range of weight values ​​subject to weight initialization), and the number of hidden units (e.g., the number of hidden layers, the number of nodes in the hidden layers). The aforementioned data structure is merely an example, and the present disclosure is not limited thereto.

[0076] An AI-based model according to one embodiment of the present disclosure may include a Large Language Model (LLM). In the present disclosure, a Large Language Model may refer to an AI-based model trained using a vast amount of training data to perform Natural Language Processing. The Large Language Model may include a transformer, a transformer encoder family model, and / or a transformer decoder family model. A transformer encoder family model may correspond to an AI model using a transformer encoder structure. A transformer decoder family model may correspond to an AI model using a transformer decoder structure.

[0077] In one embodiment, the transformer may be composed of an encoder that encodes input data and a decoder that decodes the encoded data. The transformer may have a structure that takes a series of input data as input, undergoes encoding and decoding steps, and outputs a series of output data. In one embodiment, the series of input data may be processed into a form that the transformer can compute. The process of processing the series of input data into a form that the transformer can compute may include a tokenizing process and an embedding process. The tokenizing process may refer to the process of dividing the series of input data into tokens of a certain unit. For example, the certain unit may include word units. The embedding process may refer to the process of converting at least one token tokenized from the series of input data into an embedding vector.

[0078] In one embodiment, the transformer can obtain an embedding vector to be input to an encoder by combining a token embedding vector that embeds at least one token corresponding to a series of input data, a segment embedding vector that distinguishes sentences containing the tokens for each token, and a position embedding vector that reflects the position of the token. The encoder family model and the decoder family model of the transformer can also obtain embedding vectors by performing the same method.

[0079] In one embodiment, for the transformer to encode and decode a series of input data, the encoder and decoder within the transformer may utilize an attention algorithm. An attention algorithm may refer to an algorithm that, for a given query, calculates similarity by applying a softmax function to an attention score obtained by matrix multiplying the query with a key, and calculates an attention value for the query by matrix multiplying the calculated similarity with a value.

[0080] In one embodiment, the self-attention algorithm may refer to an attention algorithm that uses a query, a key, and a value generated by multiplying the same embedding vector by a query weight, a key weight, and a value weight, respectively. The cross-attention algorithm may refer to an attention algorithm that uses a query generated by multiplying a first embedding vector by a query weight, and a key and a value generated by multiplying a second embedding vector by a key weight and a value weight, respectively. The query weight, the key weight, and the value weight may be trainable parameters that are updated through the training process of a large-scale language model.

[0081] In one embodiment, the encoder of the transformer may include an embedding layer, a self-attention layer that applies a self-attention algorithm to an embedding vector, a normalization layer, and a feed-forward neural network (FFN). Additionally, the encoder may have a form in which N unit structures including a self-attention layer, a normalization layer, and a feed-forward neural network are connected. The decoder of the transformer may include an embedding layer, a masked self-attention layer, a normalization layer, a cross-attention layer that applies a cross-attention algorithm, and a feed-forward neural network. Additionally, the decoder may have a form in which N unit structures including a masked self-attention layer, a normalization layer, a cross-attention layer, and a feed-forward neural network are connected. The masked self-attention layer may correspond to a layer that calculates an attention value for each of the sequences in which words are sequentially included among a plurality of words included in a series of input data.

[0082] A transformer may include additional components such as a linear layer and a softmax layer, in addition to an encoder and a decoder. The encoder family model and the decoder family model of a transformer may also each include the aforementioned additional components in addition to the encoder and decoder. A method for constructing a transformer using an attention algorithm may include the method disclosed in Vaswani et al., Attention Is All You Need, 2017 NIPS, which is incorporated herein by reference.

[0083] In one embodiment, attention layers such as a self-attention layer, a masked self-attention layer, and a cross-attention layer may correspond to a multi-head attention layer that includes a plurality of attention layers in parallel. The multi-head attention layer may concatenate the attention values ​​output from each of the plurality of attention layers and output an output attention value by matrix multiplying the concatenated matrix by an output weight. The output attention value output from the multi-head attention layer may have the same size as the attention value output from a single attention layer.

[0084] In one embodiment, the transformer may be trained through a Masked Language Model (MLM) process, a Next Sentence Prediction (NSP) process, etc. The MLM process may refer to a training process that predicts a masked word through a series of training data in which some words are masked. The NSP process may refer to a training process that determines whether two sentences are connected sentences in a series of training data containing any two sentences.

[0085] In one embodiment, a large-scale language model can process various data formats, such as natural language text as well as image data, audio data, and video data. The large-scale language model can embed data to convert data of various data formats into a series of computationally operable data. The large-scale language model can process additional data that represents the relative positional or topological relationships between a series of input data. Alternatively, a series of input data may be embedded by additionally reflecting vectors that represent the relative positional or topological relationships between the input data. In one example, the relative positional relationships between a series of input data may include, but are not limited to, word order within a natural language sentence, the relative positional relationships of each segmented image, and the temporal order of segmented audio waveforms. The process of adding information that represents the relative positional or topological relationships between a series of input data may be referred to as positional encoding.

[0086] An example of a large-scale language model for processing image data (Vision Transformer, ViT) is disclosed in 'Dosovitskiy, et al., AN IMAGE IS WORTH 16X16 WORDS: TRANSFORMERS FOR IMAGE RECOGNITION AT SCALE', which is incorporated herein by reference.

[0087] An artificial intelligence model according to one embodiment of the present disclosure may include a multimodal large-scale language model. A multimodal large-scale language model may refer to a large-scale language model capable of understanding and processing relationships between different data formats, such as natural language text data, image data, audio data, and video data. A multimodal language model may include a plurality of encoders that encode input data corresponding to each data format. A multimodal language model may be trained to calculate similarity between embedding vectors encoded by encoders of each data format through training data containing data of different data formats, such that similarity between identical pairs is calculated to be higher and similarity between different pairs is calculated to be lower.

[0088] An example of a large-scale multimodal language model that understands and processes the relationship between image data and natural language text data (Contrastive Language-Image Pre-training, CLIP) is disclosed in 'Alec Radford, et al., LEARNING TRANSFERABLE VISUAL MODELS FROM NATURAL LANGUAGE SUPERVISION', which is incorporated herein by reference.

[0089] FIG. 3 is a schematic diagram of a medical information analysis system (1000) according to some embodiments of the present disclosure. FIG. 4 illustrates an exemplary decision matrix according to some embodiments of the present disclosure.

[0090] Hereinafter, exemplary operations of a medical information analysis system (1000) and its components that provide treatment recommendation information for breast implants inserted into the body according to some embodiments of the present disclosure are described with reference to FIGS. 3 and 4.

[0091] A medical information analysis system (1000) according to some embodiments of the present disclosure can provide systematic treatment recommendation information based on analysis results of breast implants inserted into the body to help a clinician determine an appropriate treatment method. Specifically, the medical information analysis system (1000) can identify the type and condition of side effects using analysis information including medical-related images and text. The medical information analysis system (1000) can determine the type and condition of side effects as objective items according to a predefined list of side effect classifications and a list of severity classifications. Accordingly, the medical information analysis system (1000) according to some embodiments of the present disclosure can provide the effect of increasing the consistency and reliability of treatment planning by helping a clinician refer to objective criteria rather than relying solely on subjective experience.

[0092] In addition, the medical information analysis system (1000) can determine optimal treatment recommendation information by using side effect items (one or a combination of two) and severity items as input values ​​for a decision matrix. The medical information analysis system (1000) can use this decision matrix to perform systematic analysis not only for a single side effect but also for cases where two or more side effects occur in combination. Therefore, the medical information analysis system (1000) can efficiently support decision-making in complex clinical situations.

[0093] In addition, the medical information analysis system (1000) can determine treatment recommendation information using treatment items that are stratified according to the intensity of intervention, ranging from conservative treatment such as follow-up observation to active treatment such as reconstructive surgery accompanied by capsular removal. By determining treatment recommendation information using the stratified treatment items, the medical information analysis system (1000) can contribute to improving the quality of medical services and patient safety by providing clear treatment path guides to clinicians and reducing the risk of over- or under-treatment for patients.

[0094] In some examples, as illustrated in FIG. 3, the medical information analysis system (1000) may include an analysis information acquisition unit (200), a side effect and severity determination unit (300), and a treatment recommendation information generation unit (400). However, it is not limited thereto, and the medical information analysis system (1000) may further include other components for providing treatment recommendation information, and only some of the disclosed components may constitute the medical information analysis system (1000).

[0095] According to some embodiments of the present disclosure, the analysis information acquisition unit (200) may acquire analysis information regarding a breast implant inserted into the body. For example, the analysis information acquisition unit (200) may directly receive side effect items or severity items identified by a clinician. Additionally, the analysis information acquisition unit (200) may automatically analyze medical images, such as ultrasound images, or text information using an artificial intelligence (AI)-based analysis model. The analysis information acquisition unit (200) may acquire analysis information including quantitative data, such as the thickness of the capsule or the volume of body fluid, or data identifying side effect items and severity items, from the analysis results of the analysis model. Additionally, the analysis information acquisition unit (200) may acquire previously stored patient medical records or medical image data as analysis information by linking with an Electronic Medical Record (EMR), Picture Archiving and Communication System (PACS), or Digital Imaging and Communications in Medicine (DICOM) within the hospital. However, not limited to this, the analysis information acquisition unit (200) can acquire analysis information regarding breast implants inserted into the body in various ways.

[0096] According to some embodiments of the present disclosure, the side effect and severity determination unit (300) may determine at least one of a side effect item or a severity item related to a side effect item based on analysis information regarding a breast implant inserted into the body. Here, the side effect item may be determined as one or two items corresponding to the analysis information from a predetermined list of side effect classifications, and the severity item may be determined as one item corresponding to the analysis information from a predetermined list of severity classifications.

[0097] As described above, analysis information can take various forms in that it can be obtained in various ways. For example, analysis information may include data specifying the corresponding side effect item and severity item among the side effect classification list and severity classification list. In this case, the side effect and severity determination unit (300) can determine the information as a side effect item and / or severity item as is, without a separate processing and interpretation process.

[0098] As another example, the analysis information may include raw data such as ultrasound images, diagnostic findings in text form entered by a clinician, and quantitative measurements obtained from a Picture Archiving and Communication System (PACS). In this case, the side effect and severity determination unit (300) may selectively or in combination use an image analysis path or a text analysis path depending on the characteristics of the analysis information.

[0099] In some examples, the side effect and severity determination unit (300) may use an image analysis path to extract direct visual and quantitative information from medical images, such as ultrasound images. For example, the side effect and severity determination unit (300) may use the image analysis path to determine side effect items such as capsular thickening (TC), rupture (R1, R2), folding (F), and capsular calcification (CC), and / or severity items related to the side effect items, from the analysis information in the form of medical images. For example, the side effect and severity determination unit (300) may use an image segmentation model (e.g., U-Net) to extract image factors including capsular thickness, the presence and intensity of snowstorm signs, and the number and angle of shell folds from medical images. Then, the side effect and severity determination unit (300) may determine the side effect items and / or severity items by using the extracted image factors as input values ​​for a classification model (e.g., decision tree, random forest, etc.).

[0100] In some cases, the side effect and severity determination unit (300) may use a text analysis path to interpret unstructured text data, such as a clinician's diagnostic findings. For example, the side effect and severity determination unit (300) may determine severity items such as seroma (FC), folding (F), and capsular thickening (TC) through the text analysis path. For example, the side effect and severity determination unit (300) may use a natural language processing model (e.g., BERT) to extract a medical entity called "seroma" and a quantitative measurement value called "35cc" from text information such as "showing seroma findings, and the fluid volume is about 35cc." In this case, the side effect and severity determination unit (300) may determine seroma (FC) as a side effect item based on the extracted information and determine the severity item as severe according to the rule that the extracted value "35cc" exceeds the threshold "30cc".

[0101] In some examples, the side effect and severity determination unit (300) may use an image analysis path and a text analysis path in a cross-validation manner to ensure the reliability of the determination. Specifically, the side effect and severity determination unit (300) may determine at least one of the side effect item and the severity item related to the side effect item by comparing the image-based item-related information extracted through the image analysis path and the text-based item-related information extracted through the text analysis path, in response to the analysis information including both medical image and text information related to a single side effect item.

[0102] For example, assume that the analysis information includes an ultrasound image and a physician's opinion stating, "Capsular thickening was observed in the upper right breast of the patient. Capsular thickness was measured to be approximately 2 mm." In this case, the side effect and severity determination unit (300) can use an image analysis path to extract an image factor indicating that the capsule thickness is 1 mm from the ultrasound image as image-based item estimation information. Additionally, the side effect and severity determination unit (300) can use a text analysis path to extract a medical entity called "capsular thickness" and a quantitative measurement value called "2 mm" from the physician's opinion as text-based item estimation information.

[0103] The side effect and severity determination unit (300) can determine at least one of the side effect item or the severity item through cross-validation of image-based item estimation information and text-based item-related information. For example, both the image-based item estimation information and the text-based item estimation information may indicate that the film thickness is 2mm, which corresponds to severe (Severe) film thickening (TC), so that the two pieces of information may match each other. In this case, the side effect and severity determination unit (300) can determine film thickening (TC) as the side effect item and determine severe (Severe) as the severity item.

[0104] As another example, as in the example described above, image-based item estimation information indicates that the 'film thickness' is '1mm', corresponding to minor film thickening (TC), and text-based item estimation information indicates that the 'film thickness' is '2mm', corresponding to severe film thickening (TC), so the two pieces of information may not match. In this case, the side effect and severity determination unit (300) may determine the film thickening (TC) identified in common in both paths as a side effect item, but may defer the determination of the severity item with a different value.

[0105] Additionally, the side effect and severity determination unit (300) can generate notification information including confirmed item information, judgment reserved item information, judgment reserved detailed information, recommended action information, etc., based on cross-validation. For example, the side effect and severity determination unit (300) can generate the following notification information using a natural language processing model or a large-scale language model.

[0106] [Data discrepancy verification needed]

[0107] Confirmed Item: Side Effect - Capsule Thickening (TC)

[0108] Judgment Reserved: Severity

[0109] Details: The image analysis results (film thickness 1mm, mild) and the text analysis results (film thickness 2mm, severe) are inconsistent.

[0110] Recommended action: Verify the measurements from both data sources and confirm the final diagnosis regarding severity.

[0111] Therefore, the medical information analysis system (1000) can prevent the generation of treatment recommendation information based on incorrect information by using notification information. In addition, the medical information analysis system (1000) can provide a safety net that induces human expert intervention in the final decision by clearly communicating to the clinician uncertainties that the system cannot resolve. Thus, the medical information analysis system (1000) can improve patient safety through this safety net. Furthermore, the medical information analysis system (1000) can function as a reliable intelligent decision support tool rather than a simple automatic decision tool.

[0112] In some examples, the side effect and severity determination unit (300) may select a side effect item from a predefined list of side effect classifications for systematic decision-making. According to some embodiments of the present disclosure, the predefined list of side effect classifications may include at least one of folding (F), fluid collection (FC), thickened capsule (TC), unspecific subcapsular deposit (USD), rupture (Rupture 1, R1) in which no snowstorm sign is observed on ultrasound and silicone has not leaked into the capsule, rupture (Rupture 2, R2) in which a snowstorm sign is observed on ultrasound and silicone has leaked into the capsule, and calcified capsule. However, the predefined list of side effect classifications may include only some of the disclosed items or may include additional items. This list of side effect classifications can be composed of systematically defined items that comprehensively consider clinical importance, frequency of occurrence, and impact on treatment direction. Accordingly, the medical information analysis system (1000) can provide treatment recommendation information that can assist in the systematic treatment decision process by classifying complex symptoms into standardized items using the list of side effect classifications.

[0113] In some examples, the side effect and severity determination unit (300) may select a severity item from a predefined severity list for systematic decision-making. According to some embodiments of the present disclosure, the predefined severity classification list may include at least one of minor, severe, and symptomatic. However, it is not limited thereto, and the predefined severity list may include only some of the disclosed items or include additional items. Such a severity classification list may be composed of items for objectively evaluating the severity of a side effect based on data analyzing the correlation between quantitative data (e.g., body fluid volume, capsule thickness) and the presence or absence of clinical symptoms. Accordingly, the medical information analysis system (1000) can provide appropriate treatment recommendation information according to the severity of the same side effect using the severity list.

[0114] In some cases, the side effect and severity determination unit (300) may determine severity items for some side effect items. For example, the side effect and severity determination unit (300) may determine severity items for folding (F), seroma (FC), capsular thickening (TC), nonspecific subcapsular deposit (USD), and capsular calcification (CC). In some cases, the side effect and severity determination unit (300) may not determine severity items for some side effect items. For example, it may not determine severity items for rupture (R1 and R2). In this case, the side effect and severity determination unit (300) may determine treatment recommendation information based solely on the side effect item itself.

[0115] In some cases, the side effect and severity determination unit (300) may determine the severity item related to the side effect item according to predefined clinical criteria. The side effect and severity determination unit (300) may determine the severity item using a machine learning or deep learning model that has learned the predefined clinical criteria so as to make objective and consistent decisions based on data. Below, an exemplary embodiment in which the side effect and severity determination unit (300) determines the severity item related to the side effect item according to predefined clinical criteria is described.

[0116] According to some embodiments of the present disclosure, the side effect and severity determination unit (300) may determine severe as a severity item associated with the folding (F) in response to identifying from analysis information that the folding (F) is multiple, symptomatic, or painful.

[0117] Specifically, the side effect and severity determination unit (300) can identify whether the folding (F) is multiple, symptomatic, or accompanied by pain from the analysis information as a predefined clinical criterion. For example, the side effect and severity determination unit (300) can identify the folding and its number from medical images included in the analysis information using an object detection model. As another example, the side effect and severity determination unit (300) can identify the number of foldings from text information, such as diagnostic findings included in the analysis information. In this case, the side effect and severity determination unit (300) can determine "Severe" as the severity item related to the folding (F) when the number of foldings exceeds a pre-determined threshold. As another example, the side effect and severity determination unit (300) can use a natural language processing model to identify whether there is a keyword related to the folding (F), such as clinical 'pain' or 'palpable', from text information, such as diagnostic findings included in the analysis information. In this case, the side effect and severity determination unit (300) can determine 'Severe' as a severity item related to the folding (F) based on the identified keyword.

[0118] According to some embodiments of the present disclosure, the side effect and severity determination unit (300) may determine severe as a severity item related to the seroma (FC) in response to identifying from analysis information a seroma (FC) in which the fluid volume exceeds 30cc, is accompanied by clinical symptoms in the breast, or is self-limited for more than 3 months.

[0119] Specifically, the side effect and severity determination unit (300) can identify from analysis information regarding the seroma (FC) as a predefined clinical criterion whether the fluid volume exceeds 30cc, whether there are symptomatic symptoms in the breast, or whether there is no self-limiting for more than 3 months. For example, the side effect and severity determination unit (300) can use an image segmentation model to automatically segment the seroma area in a medical image and calculate its volume. If the volume exceeds a pre-determined threshold of '30cc', the side effect and severity determination unit (300) can determine 'Severe' as a severity item related to the seroma (FC). As another example, the side effect and severity determination unit (300) can use a natural language analysis model to detect keywords related to clinical symptoms from text information, such as medical opinions. In this case, the side effect and severity determination unit (300) can determine severe as a severity item related to the seroma (FC). As another example, the side effect and severity determination unit (300) can analyze in time whether there has been no improvement for more than 3 months by comparing the examination date information of the electronic medical record (EMR) using a large-scale language model. In this case, the side effect and severity determination unit (300) can determine severe as a severity item related to the seroma (FC).

[0120] According to some embodiments of the present disclosure, the side effect and severity determination unit (300) may determine severe as a severity item related to the thickening of the capsule (TC) in response to identifying from analysis information that a patient exhibiting clinical symptoms has all of hardness, pain, and breast shape change, or has a thickening of the capsule (TC) in which the total thickness of the capsule exceeds 1 mm.

[0121] Specifically, the side effect and severity determination unit (300) can identify whether a patient exhibiting clinical symptoms regarding capsular thickening (TC) from analysis information as a predefined clinical criterion has all of hardness, pain, and breast shape change, or whether the capsular thickness exceeds 1 mm. For example, the side effect and severity determination unit (300) can precisely measure the capsular thickness in mm units using an image segmentation model. If the capsular thickness exceeds a pre-determined threshold of '1 mm', the side effect and severity determination unit (300) can determine 'Severe' as a severity item related to capsular thickening (TC). As another example, the side effect and severity determination unit (300) can determine whether keywords related to clinical symptoms, hardness, pain, and breast shape change are all identified from text information using a natural language analysis model. In this case, the side effect and severity determination unit (300) can determine severe as a severity item related to capsular thickening (TC).

[0122] According to some embodiments of the present disclosure, the side effect and severity determination unit (300) may determine severe as a severity item related to the capsular calcification (CC) in response to identifying from the analysis information that the condition and type of the implant shell cannot be identified due to the capsular calcification (CC).

[0123] Specifically, the side effect and severity determination unit (300) can determine whether it is possible to identify a condition in which the condition and type of the implant shell cannot be identified due to capsular calcification (CC) as a predefined clinical criterion. For example, the side effect and severity determination unit (300) can use an image classification model to extract image characteristics such as the degree of posterior acoustic shadowing caused by capsular calcification or obscured boundary of the implant shell. If it is determined that the condition of the shell cannot be identified due to the ratio of posterior acoustic shadowing on the shell contour or the breakage or blurring of the implant shell boundary, the side effect and severity determination unit (300) can determine "Severe" as a severity item related to capsular calcification (CC).

[0124] According to some embodiments of the present disclosure, the treatment recommendation information generating unit (400) may determine treatment recommendation information from a predetermined treatment classification list based on at least some of the side effect items and severity items.

[0125] Specifically, when a side effect item and / or severity item is determined, the treatment recommendation information generating unit (400) can determine treatment recommendation information as a treatment item specified by the side effect item and / or severity item among a predetermined treatment classification list. In some examples, the treatment recommendation information generating unit (400) can determine treatment recommendation information using a decision matrix.

[0126] Referring to Fig. 4, an exemplary crystal matrix is ​​illustrated. Here, 'F' represents folding, 'FC' represents fluid collection, 'TC' represents thickened capsule, 'USD' represents unspecific subcapsular deposit, 'R1' represents a rupture where no 'snowstorm sign' is observed on ultrasound and silicone has not leaked into the capsule, 'R2' represents a rupture where a 'snowstorm sign' is observed on ultrasound and silicone has leaked into the capsule, 'CC' represents calcified capsule, 'F / U' represents follow-up, 'Opt. 1' represents differential diagnosis of anaplastic large cell lymphoma (BIA-ALCL) through cytology and culture tests, and 'Opt. '2' is revision without capsulectomy, 'Opt. 3' is revision with capsulectomy, 'Opt. 4' is capsulotomy, 'Opt. 5' is manual reduction, 'severe' is severe, and 'Symptomatic' may be symptomatic.

[0127] In some examples, the decision matrix may include multiple grids distinguished by columns and rows consisting of items included in the side effect classification list. As illustrated in FIG. 4, the decision matrix may have a structure in which seven items included in the predetermined side effect classification list are arranged in each column and row. Additionally, each of the multiple grids may include one or more items included in the treatment classification list. For example, referring to FIG. 4, a grid distinguished by 'row: F and column: FC' may include 'F / U', 'Opt. 4', and 'Opt1' among the treatment classification items.

[0128] In some cases, some of the items included in the treatment classification list may be conditionally associated with one of the items included in the severity classification list. For example, referring to FIG. 4, among ‘F / U’, ‘Opt. 4’, and ‘Opt1’ included in the grid distinguished by ‘Row: F and Column: FC’, ‘Opt. 4’ may be conditionally associated with Severe related to folding (F). As another example, referring to FIG. 4, ‘Opt. 5’ included in the grid distinguished by ‘Row: F and Column: USD’ may be conditionally associated with Symptomatic related to nonspecific subcapsular deposits (USD).

[0129] In some examples, the treatment recommendation information generation unit (400) can determine a grid distinguished by a side effect item among a plurality of grids included in a decision matrix. For example, when one item 'folding (F)' is determined as a side effect item based on analysis information, the treatment recommendation information generation unit (400) can determine a grid distinguished by 'row: F and column: F'. In another example, when two items 'folding (F)' and 'non-specific subcapsular precipitate (USD)' are determined as side effect items based on analysis information, the treatment recommendation information generation unit (400) can determine a grid distinguished by 'row: F and column: USD'.

[0130] When a grid is determined, the treatment recommendation information generation unit (400) can determine one of one or more treatment items included in the determined grid as treatment recommendation information.

[0131] Specifically, the treatment recommendation information generation unit (400) can determine as treatment recommendation information an item specified by analysis information among treatment items included in a grid distinguished by one or more side effect items. For example, referring to FIG. 4, a grid distinguished by 'row: F and column: USD' may include treatment items 'F / U', 'Opt. 4', and 'Opt. 5'. The treatment recommendation information generation unit (400) can determine as treatment recommendation information an item corresponding to a side effect item and a severity item determined based on analysis information among one or more treatment items included in the determined grid. For example, if the grid determined by the side effect item is a grid distinguished by 'row: F and column: USD', the side effect item determined based on the analysis information is 'folding (F)' and 'non-specific subcapsulated deposit (USD)', and the severity item associated with the side effect item 'folding (F)' and 'non-specific subcapsulated deposit (USD)' is all minor, the treatment recommendation information generation unit (400) can determine 'F / U' among 'F / U', 'Opt. 4', and 'Opt. 5' as the treatment recommendation information. As can be seen from this, for convenience, the severity item 'minor' may not be displayed associated with the side effect item on the determination matrix.

[0132] As another example, in the case where the grid determined by the side effect item is a grid distinguished by 'Row: F and Column: USD', the side effect items determined based on analysis information are 'Folding (F)' and 'Non-specific Subcapsulated Precipitate (USD)', the severity item related to the side effect item 'Folding (F)' is 'Severe', and the severity item related to the 'Non-specific Subcapsulated Precipitate (USD)' is 'Minor', the treatment recommendation information generation unit (400) can determine 'Opt. 4' among 'F / U', 'Opt. 4', and 'Opt. 5' as the treatment recommendation information. In other words, the treatment item 'Opt. Since 4' is conditionally associated with the severity item 'Severe' related to the side effect item 'Folding (F)', the treatment recommendation information generation unit (400) can determine the treatment item 'Opt. 4' corresponding to the side effect item 'Folding (F)' determined based on the analysis information and the severity item 'Severe' related to the side effect item 'Folding (F)' as treatment recommendation information.

[0133] As another example, in the case where the grid determined by the side effect item is a grid distinguished by 'Row: F and Column: USD', the side effect item determined based on the analysis information is 'Folding (F)' and 'Non-specific Subcapsulated Precipitate (USD)', the severity item related to the side effect item 'Folding (F)' is 'Minor', and the severity item related to the 'Non-specific Subcapsulated Precipitate (USD)' is 'Symptomatic', the treatment recommendation information generation unit (400) can determine 'Opt. 5' among 'F / U', 'Opt. 4', and 'Opt. 5' as the treatment recommendation information. In other words, the treatment item 'Opt. Since 5' is conditionally associated with the severity item 'Symptomatic' related to the side effect item 'Non-specific Subcapsulated Precipitate (USD)', the treatment recommendation information generation unit (400) can determine the treatment item 'Opt. 5' corresponding to the side effect item 'Non-specific Subcapsulated Precipitate (USD)' determined based on analysis information and the severity item 'Symptomatic' related to the side effect item 'Non-specific Subcapsulated Precipitate (USD)' as treatment recommendation information.

[0134] In some examples, the treatment recommendation information generation unit (400) may select a treatment item determined as treatment recommendation information from a predefined treatment list for systematic decision-making. For example, a plurality of grids may include only the items included in the predefined treatment list as treatment items. According to some embodiments of the present disclosure, the predefined treatment classification list may include at least one of follow-up ('F / U'), differential diagnosis of anaplastic large cell lymphoma (BIA-ALCL) through cytology and culture, revision without capsulectomy, revision with capsulectomy, capsulotomy, and manual reduction. However, it is not limited thereto, and the predefined treatment classification list may include only some of the disclosed items or include additional items. This treatment classification list maps the most appropriate treatment based on a combination of specific side effect items and severity items, and can be composed by synthesizing clinical guidelines, academic literature, and expert opinions. Accordingly, the medical information analysis system (1000) can use this treatment classification list to provide objective and standardized treatment recommendation information for specific clinical situations.

[0135] According to some embodiments of the present disclosure, some of the items included in a predetermined treatment classification list may form a hierarchy in the order of observation, capsulotomy, revision without capsulectomy, and revision with capsulectomy, according to side effect items and severity items.

[0136] This hierarchical structure can be predefined based on the clinical intervention level or invasiveness of each treatment item. For example, 'observation,' which involves watching the condition without any treatment, can be set as level 1, the lowest intervention level; 'capsular incision,' which involves cutting only a part of the capsule, can be set as level 2; 'reconstruction without capsular incision,' which involves replacing / removing only the implant while leaving the capsule intact, can be set as level 3; and 'reconstruction with capsular incision,' which involves removing the entire capsule, can be set as level 4, the highest intervention level. The treatment recommendation information generation unit (400) can select the most appropriate treatment item at the level within the hierarchical structure using a decision matrix, based on the side effect item and severity item determined by the side effect and severity determination unit (300). For example, the treatment recommendation information generation unit (400) can recommend the lowest level, 'observation,' for 'minor' conditions and recommend a higher level of surgical treatment according to the severity for 'severe' conditions. The medical information analysis system (1000) can use this hierarchical treatment information provision method to present clear and logical treatment paths to clinicians, ranging from conservative methods to the most aggressive methods, thereby assisting in systematic decision-making. Additionally, the medical information analysis system (1000) can function as an intelligent decision support tool that systematically links the severity of side effects with the intensity of treatment intervention to reduce the risk of under- or over-treatment, and consequently improves treatment consistency and patient safety.

[0137] In some embodiments of the present invention, the treatment recommendation information generation unit (400) may determine treatment recommendation information by additionally considering the physical characteristics of the implant itself inserted into the body, as well as side effects or severity. For example, the treatment recommendation information generation unit (400) may ultimately determine treatment recommendation information by considering the shell type. Specifically, for example, determining a differential diagnosis of anaplastic large cell lymphoma (BIA-ALCL) through cytology and culture tests as treatment recommendation information may be performed when the cell type of the breast implant is a macrotexture type. Additionally, determining manual reduction surgery as treatment recommendation information may be performed when the cell type of the breast implant is a smooth type. Accordingly, the treatment recommendation information generation unit (400) may reflect the clinical fact that a specific treatment may be recommended or, conversely, contraindicated depending on the type of implant. The treatment recommendation information generation unit (400) can provide more precise and safe customized treatment recommendation information by including the unique characteristics of the implant in the judgment logic.

[0138] In some cases, the treatment recommendation information generation unit (400) can determine the treatment recommendation information by further subdividing it after determining the treatment guidelines, taking into account the patient's prognosis or additional risk factors. For example, the treatment recommendation information generation unit (400) can determine the revision with capsulectomy as treatment recommendation information based on the side effect item of capsule thickening (TC) and the severity item of severe, and additionally determine the removal of the implant (explantation) among the revisions with capsulectomy as treatment recommendation information. By utilizing this multi-stage decision logic, the treatment recommendation information generation unit (400) can identify the safest and most effective treatment method by considering in-depth clinical knowledge and prognosis, such as "inserting a new implant increases the risk of recurrence." Through this, the medical information analysis system (1000) can support more precise and personalized treatment by reducing the risk of unnecessary re-operation and maximizing long-term treatment success rates and patient safety.

[0139] FIG. 5 is a flowchart of a method for providing treatment recommendation information for breast implants inserted into the body according to some embodiments of the present disclosure.

[0140] According to some embodiments of the present disclosure, a method for providing treatment recommendation information for a breast implant inserted into the body may include the step (S100) of determining at least one of a side effect item or a severity item related to said side effect item based on analysis information regarding the breast implant inserted into the body. Here, said side effect item may be determined as one or two items corresponding to said analysis information from a predetermined list of side effect classifications, and said severity item may be determined as one item corresponding to said analysis information from a predetermined list of severity classifications.

[0141] According to some embodiments of the present disclosure, a method for providing treatment recommendation information for a breast implant inserted into the body may include the step (S200) of determining treatment recommendation information from a predetermined treatment classification list based on at least some of the side effect items and the severity items.

[0142] Alternatively, the step (S200) of determining treatment recommendation information from a predetermined treatment classification list based on at least some of the side effect items and severity items may include: determining a grid distinguished by the side effect item among a plurality of grids included in a decision matrix—the decision matrix includes a plurality of grids distinguished by columns and rows composed of items included in the side effect classification list, and each of the plurality of grids includes one or more items included in the treatment classification list, and some of the one or more items included in the treatment classification list are conditionally associated with one item included in the severity classification list related to the side effect item—, and determining one of the one or more treatment items included in the determined grid as treatment recommendation information.

[0143] Alternatively, the step of determining one of the one or more treatment items included in the determined grid as treatment recommendation information may include: determining as treatment recommendation information an item corresponding to a side effect item determined based on the analysis information and a severity item related to the side effect item among the one or more treatment items included in the determined grid.

[0144] Alternatively, the above-determined list of adverse event classifications may include at least one of folding (F), fluid collection (FC), thickened capsule (TC), unspecific subcapsular deposit (USD), rupture (Rupture 1, R1) in which no snowstorm sign is observed on ultrasound and silicone has not leaked into the capsule, rupture (Rupture 2, R2) in which a snowstorm sign is observed on ultrasound and silicone has leaked into the capsule, and calcified capsule.

[0145] Alternatively, the above-determined severity classification list may include at least one of Minor, Severe, and Symptomatic.

[0146] Alternatively, the above-determined list of treatment classifications may include at least one of follow-up ('F / U'), differential diagnosis of anaplastic large cell lymphoma (BIA-ALCL) through cytology and culture, revision without capsulectomy, revision with capsulectomy, capsulotomy, and manual reduction.

[0147] Alternatively, some of the items included in the above-determined treatment classification list may form a hierarchy in the order of observation, capsulotomy, revision without capsulectomy, and revision with capsulectomy, depending on the above-determined side effect item and the above-determined severity item.

[0148] Alternatively, determining a differential diagnosis of anaplastic large cell lymphoma (BIA-ALCL) through cytology and culture using the above treatment recommendation information may be performed when the cell type of the breast implant is a macrotexture type.

[0149] Alternatively, determining the passive reduction based on the above treatment recommendation information may be performed when the cell type of the breast implant is a smooth type.

[0150] Alternatively, the step (S100) of determining at least one of a side effect item or a severity item related to the side effect item based on analysis information regarding the breast implant inserted into the body may include the step of determining Severe as a severity item related to the folding (F) in response to identifying from the analysis information a folding (F) that is multiple, symptomatic on a palpable shell, or accompanied by pain.

[0151] Alternatively, the step (S100) of determining at least one of a side effect item or a severity item related to the side effect item based on analysis information regarding the breast implant inserted into the body may include, in response to identifying a seroma (FC) from the analysis information in which the fluid volume exceeds 30cc, is accompanied by clinical symptoms in the breast (symptomatic), or has no self-limited status for more than 3 months, determining the seroma (FC) as the side effect item and determining severe as the severity item related to the seroma (FC).

[0152] Alternatively, the step (S100) of determining at least one of a side effect item or a severity item related to the side effect item based on analysis information regarding the breast implant inserted into the body may include determining the capsule thickening (TC) as the side effect item and determining severe as the severity item related to the capsule thickening (TC) in response to identifying a patient exhibiting clinical symptoms from the analysis information having all of hardness, pain, and breast shape change, or capsule thickening (TC) in which the total thickness of the capsule exceeds 1 mm.

[0153] Alternatively, the step of determining one of the one or more treatment items included in the determined grid as treatment recommendation information may further include the step of additionally determining explantation as treatment recommendation information among revisions with capsulectomy in response to determining revision with capsulectomy as treatment recommendation information according to the side effect item of capsule thickening (TC) and the severity item of severe.

[0154] Alternatively, the step (S100) of determining at least one of a side effect item or a severity item related to said side effect item based on analysis information regarding the breast implant inserted into the body may include, in response to identifying from said analysis information a condition in which the condition and type of the implant shell cannot be confirmed due to capsule calcification (CC), determining capsule calcification (CC) as the side effect item and determining severe as the severity item related to said capsule calcification (CC).

[0155] Alternatively, the above decision matrix may be Table 1 below.

[0156]

[0157] Here, 'F' stands for Folding, 'FC' for Fluid Collection, 'TC' for Thickened Capsule, 'USD' for Unspecific Subcapsular Deposit, 'R1' for a rupture where no 'snowstorm sign' is observed on ultrasound indicating no silicone leakage into the capsule, 'R2' for a rupture where a 'snowstorm sign' is observed on ultrasound indicating silicone leakage into the capsule, 'CC' for Calcified Capsule, 'F / U' for Follow-up, 'Opt. 1' for differential diagnosis of Anaplastic Large Cell Lymphoma (BIA-ALCL) through cytology and culture, and 'Opt. 2' is revision without capsulectomy, 'Opt. 3' is revision with capsulectomy, 'Opt. 4' is capsulotomy, 'Opt. 5' is manual reduction, 'severe' is severe, and 'Symptomatic' is symptomatic.

[0158] The steps of the method for providing recommended treatment information for breast implants inserted into the body described above are presented for illustrative purposes only, and some steps may be omitted or additional steps may be added. Additionally, the aforementioned steps may be performed in any order.

[0159] FIG. 6 is a brief and general schematic diagram of an exemplary computing environment in which embodiments of the present disclosure may be implemented.

[0160] Although the present disclosure has been described as generally being implementable by a computing device, a person skilled in the art will be well aware that the present disclosure may be implemented in combination with computer-executable instructions and / or other program modules that can be executed on one or more computers and / or as a combination of hardware and software.

[0161] Generally, a program module includes routines, programs, components, data structures, etc., that perform a specific task or implement a specific abstract data type. Furthermore, a person skilled in the art will be well aware that the method of the present disclosure can be implemented in other computer system configurations, including single-processor or multi-processor computer systems, minicomputers, mainframe computers, as well as personal computers, handheld computing devices, microprocessor-based or programmable consumer electronics, etc. (each of which may be connected to and operated with one or more associated devices).

[0162] The embodiments described in this disclosure may also be implemented in a distributed computing environment in which tasks are performed by remote processing devices connected via a communication network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.

[0163] Computers typically include various computer-readable media. Any medium accessible by a computer may be a computer-readable medium, and such computer-readable media include volatile and non-volatile media, transitory and non-transitory media, and removable and non-removable media. By example, but not limiting, computer-readable media may include computer-readable storage media and computer-readable transmission media. Computer-readable storage media include volatile and non-volatile media, transitory and non-transitory media, and removable and non-removable media implemented by any method or technique for storing information such as computer-readable instructions, data structures, program modules, or other data. Computer-readable storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, DVD (digital video disk) or other optical disk storage devices, magnetic cassettes, magnetic tapes, magnetic disk storage devices or other magnetic storage devices, or any other media that can be accessed by a computer and used to store desired information.

[0164] Computer-readable transmission media typically include all information transmission media that implement computer-readable instructions, data structures, program modules, or other data, etc., on a modulated data signal, such as a carrier wave or other transport mechanism. The term modulated data signal means a signal in which one or more of the characteristics of the signal are set or modified to encode information within the signal. By example, not limiting, computer-readable transmission media include wired media, such as wired networks or direct-wired connections, and wireless media, such as acoustic, RF, infrared, and other wireless media. Any combination of the media described above is also considered to be within the scope of computer-readable transmission media.

[0165] An exemplary environment for implementing various aspects of the present disclosure, including a computer (1102), is shown, wherein the computer (1102) includes a processing unit (1104), system memory (1106), and a system bus (1108). The system bus (1108) connects system components, including system memory (1106) (but not limited thereto), to the processing unit (1104). The processing unit (1104) may be any processor among various commercial processors. Dual processors and other multiprocessor architectures may also be used as the processing unit (1104).

[0166] The system bus (1108) may be any of several types of bus structures that can be additionally interconnected to a local bus using any of the memory bus, peripheral bus, and various commercial bus architectures. System memory (1106) includes read-only memory (ROM) (1110) and random access memory (RAM) (1112). The basic input / output system (BIOS) is stored in non-volatile memory (1110), such as ROM, EPROM, EEPROM, etc., and this BIOS includes basic routines that help transfer information between components within the computer (1102) at times such as during startup. The RAM (1112) may also include high-speed RAM, such as static RAM, for caching data.

[0167] The computer (1102) also includes an internal hard disk drive (HDD) (1114) (e.g., EIDE, SATA)—this internal hard disk drive (1114) may also be configured for external use within a suitable chassis (not shown)—a magnetic floppy disk drive (FDD) (1116) (e.g., for reading from or writing to a removable diskette (1118)), and an optical disk drive (1120) (e.g., for reading from a CD-ROM disk (1122) or reading from or writing to other high-capacity optical media such as a DVD). The hard disk drive (1114), the magnetic disk drive (1116), and the optical disk drive (1120) may each be connected to the system bus (1108) by a hard disk drive interface (1124), a magnetic disk drive interface (1126), and an optical drive interface (1128). The interface (1124) for implementing an external drive includes at least one or both of USB (Universal Serial Bus) and IEEE 1394 interface technologies.

[0168] These drives and associated computer-readable media provide non-volatile storage of data, data structures, computer-executable instructions, etc. In the case of a computer (1102), the drives and media correspond to storing any data in a suitable digital format. Although the description of computer-readable media above refers to HDDs, removable magnetic disks, and removable optical media such as CDs or DVDs, a person skilled in the art will know that other types of computer-readable media, such as zip drives, magnetic cassettes, flash memory cards, cartridges, etc., may also be used in exemplary operating environments and that any of these media may contain computer-executable instructions for performing the methods of the present disclosure.

[0169] A number of program modules, including an operating system (1130), one or more application programs (1132), other program modules (1134), and program data (1136), may be stored in the drive and RAM (1112). All or part of the operating system, application, module and / or data may also be cached in RAM (1112). It will be well known that the present disclosure may be implemented in various commercially available operating systems or combinations of operating systems.

[0170] The user can input commands and information into the computer (1102) through one or more wired / wireless input devices, such as a pointing device like a keyboard (1138) and a mouse (1140). Other input devices (not shown) may include a microphone, an IR remote control, a joystick, a game pad, a stylus pen, a touch screen, etc. These and other input devices are often connected to the processing unit (1104) via an input device interface (1142) connected to the system bus (1108), but may also be connected via other interfaces such as a parallel port, an IEEE 1394 serial port, a game port, a USB port, an IR interface, etc.

[0171] A monitor (1144) or other type of display device is also connected to the system bus (1108) via an interface such as a video adapter (1146). In addition to the monitor (1144), the computer generally includes other peripheral output devices (not shown), such as speakers, a printer, and so on.

[0172] The computer (1102) may operate in a networked environment using a logical connection to one or more remote computers, such as remote computer(s) (1148), via wired and / or wireless communication. The remote computer(s) (1148) may be a workstation, a computing device computer, a router, a personal computer, a portable computer, a microprocessor-based entertainment device, a peer device, or other conventional network node, and generally include many or all of the components described for the computer (1102), but for brevity, only the memory storage device (1150) is illustrated. The illustrated logical connection includes a wired / wireless connection to a local area network (LAN) (1152) and / or a larger network, e.g., a wide area network (WAN) (1154). Such LAN and WAN networking environments are common in offices and companies and facilitate enterprise-wide computer networks, such as intranets, all of which can be connected to a global computer network, e.g., the Internet.

[0173] When used in a LAN networking environment, the computer (1102) is connected to a local network (1152) via a wired and / or wireless communication network interface or adapter (1156). The adapter (1156) may facilitate wired or wireless communication to the LAN (1152), and the LAN (1152) may also include a wireless access point installed therein to communicate with the wireless adapter (1156). When used in a WAN networking environment, the computer (1102) may include a modem (1158), be connected to a communication computing device on the WAN (1154), or have other means to establish communication through the WAN (1154), such as through the Internet. The modem (1158), which may be an internal or external and a wired or wireless device, is connected to the system bus (1108) via a serial port interface (1142). In a networked environment, the program modules described for the computer (1102) or parts thereof may be stored in a remote memory / storage device (1150). It will be well known that the illustrated network connection is exemplary and that other means of establishing a communication link between computers may be used.

[0174] The computer (1102) operates to communicate with any wireless device or object that is deployed and operated via wireless communication, for example, a printer, scanner, desktop and / or portable computer, PDA (portable data assistant), communication satellite, any equipment or place associated with a wireless detectable tag, and a telephone. This includes at least Wi-Fi and Bluetooth wireless technologies. Accordingly, the communication may be a predefined structure as in a conventional network, or simply ad hoc communication between at least two devices.

[0175] Wi-Fi (Wireless Fidelity) enables connectivity to the Internet and other sources without wires. Wi-Fi is a wireless technology, similar to a cell phone, that allows devices, such as computers, to transmit and receive data indoors and outdoors—that is, anywhere within the coverage area of ​​a base station. Wi-Fi networks use a wireless technology called IEEE 802.11 (a, b, g, etc.) to provide secure, reliable, and high-speed wireless connections. Wi-Fi can be used to connect computers to each other, to the Internet, and to wired networks (using IEEE 802.3 or Ethernet). Wi-Fi networks can operate in unlicensed 2.4 and 5 GHz wireless bands, for example, at data rates of 11 Mbps (802.11a) or 54 Mbps (802.11b), or in products that include both bands (dual band).

[0176] Those skilled in the art of the present disclosure will understand that information and signals may be represented using any various different technologies and techniques. For example, data, instructions, commands, information, signals, bits, symbols, and chips that may be referenced in the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields or particles, or any combination thereof.

[0177] Those skilled in the art will understand that the various exemplary logic blocks, modules, processors, means, circuits, and algorithm steps described in connection with the embodiments disclosed herein may be implemented by electronic hardware, various forms of programs or design code (referred to herein as software for convenience), or a combination of all such. To clearly illustrate this interoperability between hardware and software, various exemplary components, blocks, modules, circuits, and steps have been generally described above in relation to their functions. Whether such functions are implemented as hardware or software depends on the design constraints imposed on the specific application and the overall system. Those skilled in the art may implement the functions described in various ways for each specific application, but such implementation decisions should not be interpreted as being outside the scope of this disclosure.

[0178] The various embodiments presented herein may be implemented as methods, devices, or articles manufactured using standard programming and / or engineering techniques. The term "article manufactured" includes a computer program, a carrier, or a medium accessible from any computer-readable storage device. For example, computer-readable storage media include, but are not limited to, magnetic storage devices (e.g., hard disks, floppy disks, magnetic strips, etc.), optical discs (e.g., CDs, DVDs, etc.), smart cards, and flash memory devices (e.g., EEPROMs, cards, sticks, key drives, etc.). Additionally, the various storage media presented herein include one or more devices and / or other machine-readable media for storing information.

[0179] It should be understood that the specific order or hierarchy of steps in the presented processes is an example of exemplary approaches. It should be understood that the specific order or hierarchy of steps in the processes may be rearranged within the scope of this disclosure based on design priorities. The appended method claims provide elements of various steps in a sample order, but do not imply being limited to the specific order or hierarchy presented.

[0180] Description of the presented embodiments is provided so that a person skilled in the art may use or practice the present disclosure. Various modifications to these embodiments will be apparent to a person skilled in the art, and the general principles defined herein may be applied to other embodiments without departing from the scope of the present disclosure. Thus, the present disclosure is not limited to the embodiments presented herein, but should be interpreted in the broadest possible scope consistent with the principles and novel features presented herein.

Claims

Claim 1 A method for providing treatment recommendation information for breast implants inserted into the body, performed by a computing device, comprising: a step of determining at least one of a side effect item or a severity item related to said side effect item based on analysis information regarding breast implants inserted into the body, wherein said side effect item is determined as one or two items corresponding to said analysis information from a predetermined list of side effect classifications, and said severity item is determined as one item corresponding to said analysis information from a predetermined list of severity classifications; and a step of determining treatment recommendation information from a predetermined list of treatment classifications based on at least some of said side effect item and said severity item. A method comprising, wherein the predetermined list of treatment classifications includes at least one of follow-up ('F / U'), differential diagnosis of anaplastic large cell lymphoma (BIA-ALCL) through cytology and culture, revision without capsulectomy, revision with capsulectomy, capsulotomy, and manual reduction. Claim 2 The method according to claim 1, wherein the step of determining treatment recommendation information from a predetermined treatment classification list based on at least some of the side effect items and the severity items comprises: a step of determining a grid distinguished by the side effect item among a plurality of grids included in a decision matrix, wherein the decision matrix comprises a plurality of grids distinguished by columns and rows composed of items included in the side effect classification list, and each of the plurality of grids comprises one or more items included in the treatment classification list, wherein some of the one or more items included in the treatment classification list are conditionally associated with one item included in the severity classification list related to the side effect item; and a step of determining one of the one or more treatment items included in the determined grid as treatment recommendation information. Claim 3 In paragraph 2, the step of determining one of the one or more treatment items included in the determined grid as treatment recommendation information comprises: the step of determining one of the one or more treatment items included in the determined grid as treatment recommendation information, and an item corresponding to a side effect item determined based on the analysis information and a severity item related to the side effect item. Claim 4 The method according to claim 1, wherein the predetermined adverse effect classification list comprises at least one of folding (F), fluid collection (FC), thickened capsule (TC), unspecific subcapsular deposit (USD), rupture (Rupture 1, R1) in which silicone has not leaked into the capsule as no snowstorm sign is observed on ultrasound, rupture (Rupture 2, R2) in which silicone has leaked into the capsule as a snowstorm sign is observed on ultrasound, and calcified capsule. Claim 5 A method according to claim 1, wherein the predetermined severity classification list includes at least one of minor, severe, and symptomatic. Claim 6 delete Claim 7 A method according to claim 1, wherein some of the items included in the predetermined treatment classification list form a hierarchy in the order of observation, capsulotomy, revision without capsulectomy, and revision with capsulectomy, according to the side effect item and the severity item. Claim 8 A method according to claim 1, wherein determining a differential diagnosis of anaplastic large cell lymphoma (BIA-ALCL) through cytology and culture using the treatment recommendation information is performed when the cell type of the breast implant is a macrotexture type. Claim 9 A method according to claim 1, wherein determining the passive reduction surgery based on the treatment recommendation information is performed when the cell type of the breast implant is a smooth type. Claim 10 A method for providing treatment recommendation information for breast implants inserted into the body, performed by a computing device, comprising: a step of determining at least one of a side effect item or a severity item related to said side effect item based on analysis information regarding breast implants inserted into the body, wherein said side effect item is determined as one or two items corresponding to said analysis information from a predetermined list of side effect classifications, and said severity item is determined as one item corresponding to said analysis information from a predetermined list of severity classifications; and a step of determining treatment recommendation information from a predetermined list of treatment classifications based on at least some of said side effect item and said severity item. A method comprising: a step of determining at least one of a side effect item or a severity item related to said side effect item based on analysis information regarding said breast implant inserted into the body, wherein, in response to identifying from said analysis information a folding (F) that is multiple, symptomatic on a palpable shell, or accompanied by pain, determines "severe" as a severity item related to said folding (F). Claim 11 A method for providing treatment recommendation information for breast implants inserted into the body, performed by a computing device, comprising: a step of determining at least one of a side effect item or a severity item related to said side effect item based on analysis information regarding breast implants inserted into the body, wherein said side effect item is determined as one or two items corresponding to said analysis information from a predetermined list of side effect classifications, and said severity item is determined as one item corresponding to said analysis information from a predetermined list of severity classifications; and a step of determining treatment recommendation information from a predetermined list of treatment classifications based on at least some of said side effect item and said severity item. A method comprising: a step of determining at least one of a side effect item or a severity item related to the side effect item based on analysis information regarding a breast implant inserted into the body, wherein, in response to identifying from the analysis information a seroma (FC) in which the fluid volume exceeds 30cc, is accompanied by clinical symptoms in the breast (symptomatic), or has not self-limited for more than 3 months, determining the seroma (FC) as the side effect item and determining "Severe" as the severity item related to the seroma (FC); Claim 12 A method for providing treatment recommendation information for breast implants inserted into the body, performed by a computing device, comprising: a step of determining at least one of a side effect item or a severity item related to said side effect item based on analysis information regarding breast implants inserted into the body, wherein said side effect item is determined as one or two items corresponding to said analysis information from a predetermined list of side effect classifications, and said severity item is determined as one item corresponding to said analysis information from a predetermined list of severity classifications; and a step of determining treatment recommendation information from a predetermined list of treatment classifications based on at least some of said side effect item and said severity item. A method comprising: a step of determining at least one of a side effect item or a severity item related to the side effect item based on analysis information regarding a breast implant inserted into the body, wherein, in response to identifying from the analysis information that a patient exhibiting clinical symptoms has all of Hardness, Pain, and Breast Shape Change, or has Capsular Thickness (TC) in which the total thickness of the capsule exceeds 1 mm, determining Capsular Thickness (TC) as the side effect item and determining Severe as the severity item related to the Capsular Thickness (TC); Claim 13 A method for providing treatment recommendation information for breast implants inserted into the body, performed by a computing device, comprising: a step of determining at least one of a side effect item or a severity item related to said side effect item based on analysis information regarding breast implants inserted into the body, wherein said side effect item is determined as one or two items corresponding to said analysis information from a predetermined list of side effect classifications, and said severity item is determined as one item corresponding to said analysis information from a predetermined list of severity classifications; and a step of determining treatment recommendation information from a predetermined list of treatment classifications based on at least some of said side effect item and said severity item. The step of determining treatment recommendation information from a predetermined treatment classification list based on at least some of the side effect items and severity items comprises: determining a grid distinguished by the side effect item among a plurality of grids included in a decision matrix - the decision matrix comprises a plurality of grids distinguished by columns and rows composed of items included in the side effect classification list, each of the plurality of grids comprises one or more items included in the treatment classification list, and some of the one or more items included in the treatment classification list are conditionally associated with one item included in the severity classification list related to the side effect item -; and determining one of the one or more treatment items included in the determined grid as treatment recommendation information; and the step of determining one of the one or more treatment items included in the determined grid as treatment recommendation information comprises: determining an item corresponding to the side effect item determined based on the analysis information and the severity item related to the side effect item among the one or more treatment items included in the determined grid as treatment recommendation information;A method comprising: a step of determining one of one or more treatment items included in the determined grid as treatment recommendation information, wherein, in response to determining Revision with capsulectomy as treatment recommendation information according to a side effect item of capsular thickening (TC) and a severity item of severe, further comprising the step of additionally determining explantation as treatment recommendation information among Revision with capsulectomy. Claim 14 A method for providing treatment recommendation information for a breast implant inserted into the body, performed by a computing device, comprising: a step of determining at least one of a side effect item or a severity item related to said side effect item based on analysis information regarding the breast implant inserted into the body, wherein said side effect item is determined as one or two items corresponding to said analysis information from a predetermined list of side effect classifications, and said severity item is determined as one item corresponding to said analysis information from a predetermined list of severity classifications -; and a step of determining treatment recommendation information from a predetermined list of treatment classifications based on at least some of said side effect item and said severity item; wherein the step of determining at least one of a side effect item or a severity item related to said side effect item based on analysis information regarding the breast implant inserted into the body comprises: a step of determining said side effect item as a capsular calcification (CC) in response to identifying from said analysis information a condition in which the condition and type of the implant shell cannot be verified due to said capsular calcification (CC), and determining said severity item as a severity item related to said capsular calcification (CC). Claim 15 A method for providing treatment recommendation information for breast implants inserted into the body, performed by a computing device, comprising: a step of determining at least one of a side effect item or a severity item related to said side effect item based on analysis information regarding breast implants inserted into the body, wherein said side effect item is determined as one or two items corresponding to said analysis information from a predetermined list of side effect classifications, and said severity item is determined as one item corresponding to said analysis information from a predetermined list of severity classifications; and a step of determining treatment recommendation information from a predetermined list of treatment classifications based on at least some of said side effect item and said severity item. The method comprises: determining treatment recommendation information from a predetermined treatment classification list based on at least some of the side effect items and severity items, wherein the step of determining a grid distinguished by the side effect item among a plurality of grids included in a decision matrix—wherein the decision matrix includes a plurality of grids distinguished by columns and rows composed of items included in the side effect classification list, wherein each of the plurality of grids includes one or more items included in the treatment classification list, and some of the one or more items included in the treatment classification list are conditionally associated with one item included in the severity classification list related to the side effect item—; and determining one of the one or more treatment items included in the determined grid as treatment recommendation information; and the step of determining one of the one or more treatment items included in the determined grid as treatment recommendation information, wherein the step of determining an item corresponding to the side effect item determined based on the analysis information and the severity item related to the side effect item among the one or more treatment items included in the determined grid as treatment recommendation information; wherein the decision matrix is ​​Table 1 below. [Table 1] Here, 'F' stands for Folding, 'FC' for Fluid Collection, 'TC' for Thickened Capsule, 'USD' for Unspecific Subcapsular Deposit, 'R1' for a rupture where no 'snowstorm sign' is observed on ultrasound indicating no silicone leakage into the capsule, 'R2' for a rupture where a 'snowstorm sign' is observed on ultrasound indicating silicone leakage into the capsule, 'CC' for Calcified Capsule, 'F / U' for Follow-up, 'Opt. 1' for differential diagnosis of Anaplastic Large Cell Lymphoma (BIA-ALCL) through cytology and culture, and 'Opt. 2' is revision without capsulectomy, 'Opt. 3' is revision with capsulectomy, 'Opt. 4' is capsulotomy, 'Opt. 5' is manual reduction, 'severe' is severe, and 'Symptomatic' is symptomatic. Claim 16 A computer program stored on a computer-readable storage medium, wherein the computer program includes instructions to cause one or more processors to perform a method of providing treatment recommendation information for a breast implant inserted into the body, the method comprising: a step of determining at least one of a side effect item or a severity item related to said side effect item based on analysis information regarding a breast implant inserted into the body, wherein said side effect item is determined as one or two items corresponding to said analysis information from a predetermined list of side effect classifications, and said severity item is determined as one item corresponding to said analysis information from a predetermined list of severity classifications -; and a step of determining treatment recommendation information from a predetermined list of treatment classifications based on at least some of said side effect item and said severity item; A computer program stored on a computer-readable storage medium, comprising at least one of the following: follow-up ('F / U'), differential diagnosis of anaplastic large cell lymphoma (BIA-ALCL) through cytology and culture, revision without capsulectomy, revision with capsulectomy, capsulotomy, and manual reduction. Claim 17 A computing device for performing a method of providing treatment recommendation information for a breast implant inserted into the body, wherein the computing device comprises: a processor; and the processor performs the operation of determining at least one of a side effect item or a severity item related to said side effect item based on analysis information regarding a breast implant inserted into the body - said side effect item is determined as one or two items corresponding to said analysis information from a predetermined list of side effect classifications, and said severity item is determined as one item corresponding to said analysis information from a predetermined list of severity classifications -; A computing device that performs the operation of determining treatment recommendation information from a predetermined list of treatment classifications based on at least some of the above side effect items and above severity items, wherein the predetermined list of treatment classifications includes at least one of follow-up ('F / U'), differential diagnosis of anaplastic large cell lymphoma (BIA-ALCL) through cytology and culture, revision without capsulectomy, revision with capsulectomy, capsulotomy, and manual reduction.

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