Artificial intelligence-based bone quality determination system using drilling temperature of implant surgical kit

Abstract

The present invention relates to a bone quality determination system and, more specifically, to an artificial intelligence-based bone quality determination system using a drilling temperature of an implant surgical kit, wherein information values including temperature, torque, and air pressure of a drill are detected when drilling an implantation hole in a target bone for implantation, by using a plurality of sensor units installed on a handpiece or a drill, and the detected information values are input into a calculation unit so that the bone density of the target bone for implantation is determined according to an algorithm of the calculation unit. The bone quality determination system of the present invention comprises a handpiece including a plurality of sensor units on a drill, and comprises: a drilling step of carrying out initial drilling to form an implantation hole of an implant in a target bone for implantation by using the drill installed on the handpiece; a measurement step of detecting information values including temperature, torque, air pressure, RPM, and penetration rate of the drill measured by using the plurality of sensor units installed on the handpiece in the drilling step; and a bone density determination step of inputting, into a calculation unit of a main server, the information values detected in the measurement step, and calculating the bone density of the target bone for implantation according to an algorithm trained by the calculation unit.

Description

An AI-based bone quality assessment system using the drilling temperature of an implant surgical kit.

[0001] The present invention relates to a bone quality determination system, and more particularly, to an artificial intelligence-based bone quality determination system using drilling temperature of an implant surgical kit, which detects information values ​​including temperature, torque, air pressure, etc. of a drill when drilling a hole for implantation into a bone target for implantation using a plurality of sensor units installed in a handpiece or a drill, inputs the detected information values ​​into a calculation unit, and determines the bone density of the bone target for implantation according to an algorithm of the calculation unit.

[0002]

[0003] In general, when performing an implant to replace a damaged tooth or bone, in most cases a fixture, which is a screw-shaped fixture that acts as an artificial tooth root, is used. The fixture is implanted into the jawbone, etc., and used as an artificial tooth root.

[0004] At this time, in order to implant the fixture into the jawbone, etc., a handpiece equipped with a drill is used to cut the alveolar bone vertically. The size of the fixture to be implanted into the alveolar bone is determined based on the bone density of the alveolar bone, and the strength and diameter of the surgical kit taper drill must be used differently according to the fixture size, so measuring the bone density of the alveolar bone can be said to be an important task for the practitioner.

[0005] However, it is difficult for the surgeon to accurately measure the bone density of the alveolar bone, and therefore, most implant surgeons currently rely on experience to learn and recognize factors such as resistance and vibration transmitted to the hand during drilling, and determine the drill diameter by establishing the surgical method based on the surgeon's qualitative experience and judgment, such as estimating the bone density.

[0006] However, since bone density estimation based on personal experience is based on the experience of the practitioner, it can cause a large error compared to the expected insertion torque during actual implant insertion, and practitioners with little experience have difficulty using this method. In addition, because it relies on experience, accuracy is reduced, and there are problems such as damage to the surrounding bone due to excessively strong drilling, or side effects occurring due to prolonged procedure time.

[0007] Meanwhile, during implant surgery, the bone into which the implant is to be placed must be damaged as little as possible to ensure initial stability after implant placement. However, the cutting heat generated from the drill destroys the surrounding bone tissue, causing the bone to lose its regenerative ability and hindering osseointegration, ultimately leading to implant failure.

[0008] In a study on this issue, Eriksson, Albrektsson, et al. reported that bone damage occurred when the threshold temperature was maintained at 47℃ for 1 minute. Accordingly, the cutting heat generated during alveolar bone drilling needs to be managed below the threshold temperature.

[0009] That is, the conventional implant bone density measurement method has the following problems: First, it relies only on the cutting force torque during drilling, and thus, an error may occur in the torque generated during actual implant drilling due to a qualitative judgment of bone quality based on the operator's experience, and there is a problem that operators with little surgical experience have difficulty applying a safe drilling torque.

[0010] Second, the fixture implantation work based on the experience of the practitioner is not only relatively less accurate, but also the drilling work by an inexperienced practitioner can easily cause excessive torque, which can cause excessive cutting heat generation and necrosis of the living cell tissue around the fixture.

[0011] Third, there is a problem in that the operator cannot detect the cutting heat generated during the drilling process, making it impossible to control and manage the cutting heat below the threshold temperature.

[0012] As a prior art for solving the above problem, domestic patent registration No. 10-0939227 has been disclosed.

[0013] The above prior art relates to a bone density measurement method for an implant for correcting teeth using a handpiece provided with a fixture that is implanted in the alveolar bone and used as a fixing source during orthodontic treatment, and a drill that forms an implantation groove for implanting the fixture, the method comprising: a step of inserting a measuring tap provided in the handpiece during a drilling operation for forming the implantation groove; a step of forming a screw thread in the implantation groove using the measuring tap; a step of calculating a torque value from an electrical measurement value generated when the screw thread is formed using a torque sensor provided in the handpiece; and a step of calculating bone density information using the detected torque value.

[0014] However, when determining bone density using torque in the same manner as the above-mentioned prior art, there is a problem that accurate measurement is impossible due to the patient's movement or vibration, and even if bone density is calculated using torque, there is a problem that the strength and diameter of the drill according to the calculated bone density are ultimately determined by the operator's experience, there is a problem that a large error may occur in the measured torque depending on the pressure the operator applies to the handpiece or drill during the procedure, and there is a problem that the drilling temperature cannot be controlled or managed below the threshold temperature.

[0015] Therefore, by using multiple sensor units installed in the handpiece to detect information values ​​including temperature, torque, air pressure, RPM, and cutting depth speed of the guide drill when drilling a hole in the target bone for implant, and extracting the detected information values ​​as feature values ​​and learning them as input values ​​and corresponding output values ​​to the calculation unit, the bone density of the target bone for implant is determined according to the learning algorithm of the calculation unit to provide visual guidance to the operator, and quantitatively improving the qualitative implant placement process based on the operator's experience, the need for an artificial intelligence-based bone quality determination system using the drilling temperature of the implant surgical kit has arisen, which can reduce dependence on the individual operator's skill level.

[0016]

[0017] The present invention is to solve the above problems, and its purpose is an artificial intelligence learning method that detects information values ​​including temperature, torque, air pressure, RPM, and cutting depth speed of a guide drill when drilling an implant hole in a bone target for implant using a plurality of sensor units installed in a handpiece and a drill, and matches the detected feature values ​​to input and output values ​​of a calculation unit. The learned calculation unit's algorithm determines the bone density of the implant target bone according to the procedure, provides visual guidance to the practitioner, and is a technology that quantitatively improves the qualitative implant placement process based on the practitioner's experience, thereby reducing dependence on the individual practitioner's skill, and providing an artificial intelligence-based bone quality determination system using the drilling temperature of an implant surgical kit to dental hospitals that lack skilled practitioners.

[0018] In addition, the purpose is to provide an artificial intelligence-based bone quality determination system using the drilling temperature of an implant surgical kit capable of controlling the drilling temperature of an implant taper drill that can manage cutting heat so that the threshold temperature is 47℃ or lower by displaying the temperature of the taper drill used during the procedure in real time using the temperature sensing technology installed in the drill.

[0019] In addition, another object of the present invention is to provide an artificial intelligence-based bone quality determination system using the drilling temperature of an implant surgical kit, which determines the size of a fixture to be implanted in an implant hole after determining the bone quality by AI based on the temperature measured through feature point analysis, and determines the diameter of a taper drill used in the implant hole accordingly by AI, thereby determining the size of the fixture by quantitative analysis by AI rather than by the user's experience.

[0020] In addition, the purpose of the present invention is to provide an artificial intelligence-based bone quality determination system using the drilling temperature of an implant surgical kit that controls the frictional heat during the drilling process by measuring the cutting temperature when machining an implant hole with a taper drill using a handpiece, thereby minimizing destruction of surrounding bone tissue, expanding the ability to regenerate the implant target bone, and reducing the occurrence of fine particles, thereby promoting osseointegration of the implanted implant and increasing the success rate of implant surgery.

[0021]

[0022] In order to achieve the above-mentioned task, the present invention is characterized by comprising a bone quality determination system including a handpiece having a plurality of sensor units on a drill, the system comprising: a drilling step of performing initial drilling to form an implantation hole in a bone for implantation using a drill installed in the handpiece; a measuring step of detecting information values ​​including temperature, torque, air pressure, RPM, and cutting depth speed of the drill measured using a plurality of sensor units installed in the handpiece in the drilling step; and a bone density determination step of inputting the information values ​​detected in the measuring step into a calculation unit of a main server, and calculating the bone density of the bone for implantation according to an algorithm learned by the calculation unit.

[0023] In addition, the main server is characterized by including a database that receives information values ​​from the handpiece and stores them in the form of relational data, or receives and stores arbitrary vector data through an external terminal connected to the main server by wire or wirelessly, and a calculation unit that calculates bone density using the information values ​​stored in the database.

[0024] In addition, the main server is characterized by further including a verification unit that compares the bone density calculated by the bone quality determination system with the bone density actually measured in the implant target bone to calculate an error value, and compensates (Reward) the weight of the calculation unit in the direction in which the calculated error value decreases.

[0025] In addition, the operation unit inputs the drilling temperature measured by the sensor unit into the feature point extraction unit when initially drilling the implant target bone using a guide drill, and determines the bone density of the implant target bone as one of Soft-Bone, Normal-Bone, and Hard-Bone based on the pattern of the feature points extracted by the feature point extraction unit.

[0026] In addition, the above-described operation unit is characterized by displaying a preset color according to the bone density determined on the CT scan image of the implant target bone and displaying it on a monitor or external terminal.

[0027] In addition, the main server is characterized in that it stores the CT image of the implant target bone, which is colored according to the bone density and bone density determined by the calculation unit, as relational data in a database together with patient identification information including at least one selected from the patient classification number and resident registration number of the subject of the CT image.

[0028] In addition, the operation unit performs implant surgery using the drill on a plurality of artificial bones set to an arbitrary bone density so that the drill cutting heat becomes lower than a threshold temperature of 47°C, and vector data including the measured drilling temperature is input, and is characterized in that it is learned using an artificial neural network (ANN) method.

[0029] In addition, the above operation unit performs implant surgery using the drill on a plurality of artificial bones set to an arbitrary bone density so that the drill cutting heat becomes lower than a threshold temperature of 47℃, and vector data including the measured drilling temperature is input, and is characterized in that it is learned using the SVM (Support Vector Machine) method.

[0030] In addition, based on the bone density determined by the bone quality determination system, the size of the fixture to be implanted in the implantation hole is determined, and the diameter of the drill to be used for precision machining the implantation hole among the drills stored in the database is determined together according to the bone density.

[0031] In addition, the present invention is characterized in that it outputs CT scan images to a monitor or external terminal connected to the main server via wired or wireless means, and displays bone density and the number or name of the drill used for each treatment step on the CT scan images.

[0032] In addition, when displaying the number or name of the drill determined by bone density on the CT scan image, the drill image pre-stored in the database is displayed on the monitor or external terminal for each treatment step.

[0033]

[0034] According to the present invention, by using a plurality of sensor units installed in a handpiece and a guide drill, information values ​​including temperature, torque, air pressure, RPM, and cutting depth speed of a guide drill are detected when drilling an implant hole in an artificial bone for implant, and the detected information values ​​are input to a calculation unit, and the bone quality of the implant target bone is determined according to an algorithm of the calculation unit and provided as visual guidance to the operator, so that a qualitative implantation process based on the operator's experience can be quantitatively improved, thereby reducing dependence on the individual skill of the operator.

[0035] In addition, by using a temperature sensor installed in the drill to display the temperature of the taper drill used during the procedure in real time, it is possible to manage cutting heat so that the threshold temperature is below 47℃.

[0036] In addition, the size of the fixture to be implanted in the implantation hole is determined by AI after determining the bone quality based on the temperature measured through feature point analysis, and the diameter of the taper drill used in the implantation hole is determined accordingly, so that the size of the fixture can be determined by quantitative analysis by AI rather than the user's experience.

[0037] In addition, when machining a taper drill implant hole using a handpiece, the frictional heat during the drilling process can be controlled by measuring the cutting temperature, thereby minimizing destruction of surrounding bone tissue, expanding the implant target bone regeneration ability, and reducing the occurrence of fine particles, thereby promoting osseointegration of the implanted implant, thereby increasing the success rate of implant surgery.

[0038] In addition, the main server is characterized by including a verification unit that compares the bone density calculated by the bone quality determination system with the bone density directly measured from the implant target bone to correct the error value, and compensates (Reward) the weight of the calculation unit in the direction of reducing the calculated error value, thereby improving the artificial intelligence performance.

[0039]

[0040] Figure 1 is a conceptual diagram of an artificial intelligence-based bone quality determination system using the drilling temperature of an implant surgical kit according to one embodiment of the present invention.

[0041] Figure 2 is a conceptual diagram of an artificial intelligence-based bone quality determination system using the drilling temperature of an implant surgical kit according to one embodiment of the present invention.

[0042] Figure 3 is an example of vector data of one embodiment of the present invention.

[0043] Figure 4 is an example of a bone quality determination sequence of an artificial intelligence-based bone quality determination system using the drilling temperature of an implant surgical kit according to one embodiment of the present invention.

[0044] Figure 5 is an example of drilling in an implant target bone according to one embodiment of the present invention.

[0045] Figure 6 is an example of a surgical sequence according to the diameter of a tapered drill according to bone quality determination of one embodiment of the present invention.

[0046]

[0047] Referring to the attached drawings below, a detailed description will be given of an artificial intelligence-based bone quality determination system using drilling temperature of an implant surgical kit according to a preferred embodiment of the present invention and a fixture size determination method using the same.

[0048] It is to be understood that the terms used herein are not intended to limit the technology described in this document to a particular embodiment, but rather to encompass various modifications, equivalents, and alternatives of the embodiments.

[0049] The drawings used to explain the present invention may contain exaggerated or omitted parts of the expressions shown to easily understand the features of the invention, and this does not limit the specific embodiments of the invention.

[0050] Before explaining the present invention, the drawings and description of the present invention have been illustrated and described based on a dental implant implanted in the alveolar bone (111), but this is only an example to help the practitioner understand, and is not necessarily limited thereto, so it is desirable to understand that it is a technology that can be applied to various types of implants such as spinal implants and knee implants.

[0051] Additionally, it is preferable to understand that the term 'bone density' used in the description of the present invention is used in the same meaning as the term 'bone quality'.

[0052] As illustrated in FIGS. 1 to 5, the bone quality determination system (1) of the present invention comprises a handpiece (10) having a drill (15) equipped with a plurality of sensor units (16), a drilling step (S10) for performing initial drilling to form an implantation hole in an implant artificial bone (artificial bone) using the drill (15) installed in the handpiece (10); a measuring step (S20) for detecting information values ​​including temperature, torque, air pressure, RPM, and cutting depth speed of the drill (15) measured using the plurality of sensor units (16) installed in the handpiece in the drilling step (S10); and a bone density determination step (S30) for inputting the information values ​​detected in the measuring step (S20) into the calculation unit (21) of the main server (20), and calculating the bone density of the implant target bone according to an algorithm learned by the calculation unit (21).

[0053] The above handpiece (10) is structured so that a drill (15), tap, or driver can be detached, and specifically, the handpiece (10) is formed so that it can be detached and mounted in a socket-type or coupling-type manner, and information values ​​including temperature, torque, air pressure, RPM, cutting depth speed, etc. of the drill (15) detected by the sensor unit (16) provided in the drill (15) are transmitted as signals and transmitted in real time to the main server (20) through a wired or wireless communication unit that is provided in or connected to the handpiece (10). That is, information values ​​including temperature change patterns, RPM change patterns, and cutting depth speed change patterns that change over time are collected together.

[0054] At this time, by recording the time at which each information value is transmitted to the main server (20), a temperature change graph according to time can be created during initial drilling by the guide drill (151), or a temperature change graph according to cutting depth can be created using the temperature and cutting depth among the information values ​​transmitted to the main server (20), and the graph thus created is input to the feature point extraction unit (25) to be described later to extract feature points at any point in time, and it is desirable to understand that the feature points thus extracted can be utilized as vector data (Vactor-Data) used for learning by the operation unit (21) so that the operation unit (21) can determine the tendency of the temperature change pattern according to bone density and calculate the bone density according to the temperature change pattern.

[0055] Here, the sensor for measuring temperature among the sensor units (16) provided in the drill (15) may have a structure in which an insulated thermocouple is inserted into the inside of the drill (15) to prevent electric shock accidents, etc., considering that it is used inside the oral cavity. The drill (15) may be divided into a guide drill (151) used for initial drilling and a tapered drill (152) that processes a planting hole of a predetermined size along a center hole processed by the guide drill (151) for fixture planting.

[0056] In addition, the sensor measuring the RPM of the drill (15) may be a light reflective sensor installed on the output shaft inside the handpiece (10), and since the cutting depth speed of the drill (15) means the speed at which a predetermined unit of depth is cut during drilling, the cutting depth may be measured using a depth sensor of the light output type, but may also be measured in a manner that calculates the amount of change in cutting depth per unit time.

[0057] In addition, since the temperature of the drill (15) measured by the sensor unit (16) is proportionally correlated with the torque, air pressure or RPM of the drill (15), it is preferable to understand that when the temperature of the drill (15) is used as a parameter in the following description, the temperature can be replaced with torque, air pressure or RPM or used as reference data. Here, it is preferable to understand that the parameter means an information value input to the feature point extraction unit (25) described later.

[0058] The above main server (20) includes a database (22) that receives information values ​​from the handpiece (10) and stores them in the form of relational data, or receives and stores arbitrary vector data (Vactor-Data) through an external terminal (30) connected to the main server by wire or wirelessly, and a calculation unit (21) that calculates bone density using the information values ​​stored in the database (22).

[0059] Here, the external terminal (30) means a terminal that exists separately from the system of the present invention, such as a PC, laptop, or smartphone that is capable of Internet communication and equipped with an input device, and it is preferable to understand that the 'arbitrary vector data' input through the external terminal (30) is comprised of demonstration data including the bone density of the artificial bone (110) and the temperature, torque, air pressure, RPM, cutting depth speed, etc. measured by the sensor unit (16) when drilling the artificial bone (110) using the drill (15) while a skilled operator drills a plurality of artificial bones (110) set to an arbitrary bone density, or actual data including the temperature, torque, air pressure, RPM, cutting depth speed, etc. measured by the sensor unit (16) during an actual procedure.

[0060] In addition, the vector data (Vactor-Data) as described above may include data on a demonstration procedure of direct implant placement on artificial bone (110), and may include all information values ​​including drill diameter, drill designation, drill image, drill temperature, drill RPM, drill cutting depth speed, and set bone density of artificial bone (110) for each procedure step used in the implant placement procedure.

[0061] That is, the learning data or vector data (Vactor-Data) learned by the operation unit (21) of the present invention can be defined as a set or data of feature points extracted through the feature point extraction unit (25) from information values ​​detected from data obtained when the cutting heat of the drill (15) is lower than the threshold temperature of 47°C during implant placement into artificial bone (110), and the cutting heat of the drill (15) is measured by a temperature sensor provided inside the drill (15) and displayed on an external terminal (30). However, when the temperature of the drill (15) measured by the temperature sensor exceeds the preset threshold temperature (47°C), a notification is generated on the external terminal (30), and the cutting heat of the drill (15) is controlled and managed to be lower than the threshold temperature during implant placement.

[0062] The above operation unit (21) is configured to perform operations according to a set process by referencing data stored in the database (22) of the main server (20) or data input through an external terminal (30), and it is desirable to understand it as a configuration capable of arithmetic operations and logical operations.

[0063] At this time, the above-mentioned calculation unit (21) basically performs deep learning algorithm learning including an artificial neural network (ANN), DQN (Deep-Q-Network), etc. to calculate the bone density of the implant target bone using the input feature points according to the purpose, but is not necessarily limited thereto, and any one of the machine learning type methods including supervised learning, unsupervised learning, reinforcement learning, etc. may be selected and implemented. However, the description of the present invention is based on the supervised learning method, and accordingly, it is preferable to understand that the vector data used as learning data includes target data.

[0064] That is, the above operation unit (21) has a learning algorithm that extracts the detected information value as a feature value and learns the input value and the corresponding output value to the operation unit.

[0065] In addition, it is preferable that the operation unit (21) includes at least one accumulation device (211) so that the calculation value generated during the operation process can be temporarily stored, and a backup structure is configured so that the calculation value temporarily stored in the accumulation device (211) can be stored in the database (22) as needed.

[0066] In addition, the main server (20) may further include a feature point extraction unit (25) that generates a graph using one or more values ​​selected from among information values ​​including temperature, torque, air pressure, RPM, cutting depth speed, etc. stored in the database (22), and extracts feature points from the generated graph, and a verification unit (28) that verifies the accuracy of the bone density calculated by the calculation unit (21) and compensates for an error value in the feature points or information values ​​input to the calculation unit (21) according to the verified result.

[0067] The above-mentioned feature point extraction unit (25) can utilize various analysis techniques to extract arbitrary feature points from a graph generated using information values, and for example, it can select and use at least one of the following algorithms: a unit extraction method that divides the cutting depth into predetermined units and extracts feature points located at intersections, a MIN-MAX extraction method that extracts the minimum and maximum values ​​of an arbitrary section, a maximum value extraction method that extracts the maximum value in the entire section, a peak extraction method that extracts extreme values ​​in the entire section, a kurtosis extraction method that extracts a protrusion pattern of a graph, a skew extraction method that extracts the degree of distortion of a graph, and an RMS (Root Mean Square) extraction method that extracts the root mean square of the input information values.

[0068] As an example, as illustrated in FIG. 3, the feature point extraction unit (25) may display the cutting depth on the horizontal axis and the drill temperature on the vertical axis by referring to the information values ​​recorded in the vector data (Vactor-Data), and may display the temperature change pattern for each cutting depth in the form of a graph by connecting the drill temperature values ​​for each cutting depth with a line segment, and may also match the measured bone density to each graph so that the operation unit (21) learns which bone density a specific temperature change pattern for each cutting depth corresponds to when learning.

[0069] Here, as shown in FIG. 3, there is a tendency for the measured drill temperature to increase as the bone density increases (i.e., Soft → Hard). This is because the closer the bone density is to hard bone, the greater the cutting resistance, which increases the drill temperature. Therefore, the bone density can be classified according to the drill temperature. If this is implemented using artificial intelligence through the above method, the artificial intelligence that receives the temperature measured during initial drilling using the guide drill (151) can quantitatively determine the bone density.

[0070] In addition, the feature point extraction unit (25) refers to the cutting depth and the temperature of the drill (15) from a plurality of vector data (Vactor-Data) arbitrarily selected from among the vector data (Vactor-Data) stored in the database (22), and generates a graph in which the cutting depth is displayed on the horizontal axis and the temperature of the drill (15) is displayed on the vertical axis, and extracts the highest temperature of the same section from each generated graph and defines it as a feature point of each vector data (Vactor-Data), and then displays it on the same plane and can be defined as learning data.

[0071] Here, the points displayed on the plane in the above learning data are the maximum temperature values ​​measured in the same section, i.e., the same cutting depth section, in each vector data, and the operation unit (21) repeatedly learns the learning data so that the reference values ​​for distinguishing between soft bones, normal bones, and hard bones can be set in the direction in which the maximum margin remains according to the distribution of the feature points displayed on the plane in the learning data.

[0072] Here, in the embodiment of the present invention, bone density is classified by a three-stage classification method consisting of Soft-Bone, Normal-Bone, and Hard-Bone, but this can be set differently depending on the learning data input to the artificial intelligence and the designated classification criteria, and it is desirable to understand that it can be classified in more detail (for example, Soft-Bone can be classified again into three stages such as low-soft, middle-soft, and high-soft) depending on the artificial intelligence learning process.

[0073] The above verification unit (28) compares the bone density produced by the bone quality determination system (1) with the bone density measured from the implant target bone (111) in a conventional manner to calculate an error value, and compensates the calculated error value to the calculation unit (21). In other words, the verification unit (28) is configured to verify whether the bone quality determination function of the calculation unit (21) that learned the learning data is operating accurately, and to perform a procedure for correcting it if it is not operating properly.

[0074] Alternatively, if an error occurs between the actual bone density and the bone density predicted by the bone quality determination system (1), the bone density determination process by the bone quality determination system (1) may be performed in reverse order, and the actual bone density may be input into the target data, thereby updating the variables of each node constituting the artificial neural network to correct for the error, using the error backpropagation method.

[0075] In addition, a preset color and symbol may be displayed and synthesized according to the determined bone density on the CT image of the implant target bone, and the CT synthesized image with the color and symbol displayed according to the determined bone density may be displayed on an external terminal (30) including a monitor or smartphone connected to the main server (20) by wire or wirelessly.

[0076] Here, it is preferable that the operation unit (21) refers to the accumulated information of the CT scan image so that the bone density determined by the bone quality determination system of the present invention is displayed in color at the correct location.

[0077] The CT composite image with color displayed according to bone density in this way is linked to the previously taken CT image and stored in a database (22), and the CT composite image with color displayed and the previously taken CT image can be displayed simultaneously on a monitor or external terminal (30) according to the operator's needs.

[0078] In addition, the main server (20) may store the CT composite image of the implant target bone, which is colored according to the bone density and bone density determined by the calculation unit (21), in a database (22) as relational data together with patient identification information including at least one selected from the patient classification number and resident registration number of the implant target (hereinafter referred to as “patient”).

[0079]

[0080] As shown in Fig. 4, the bone density determination system (1) configured as described above can be operated in any one of the methods described below to determine bone density. Specifically, as an example, the operation unit (21) may be artificial intelligence (AI) learned in an artificial neural network (ANN) manner using a plurality of vector data (Vactor-Data) input through an external terminal (30), and among the information values ​​of each vector data (Vactor-Data), at least one information value selected from among a temperature change pattern, an RPM change pattern, and a cutting depth speed change pattern is input to the feature point extraction unit (25) to extract feature points, and the extracted feature points are learned in a manner that they match with target data (e.g., bone density of artificial bone (110) or bone density of an actual implant target bone) included in the corresponding vector data (Vactor-Data). That is, the operation unit (21) is learned in a manner that matches the pattern of the extracted feature points with an arbitrary bone density, so that when a feature point pattern of an implant target bone of a specific bone density is input, it is determined as a bone density that matches vector data having a similar pattern among the learned vector data.

[0081] Thereafter, when the operator performs initial drilling on the implant target bone using the guide drill (151) installed on the handpiece (10), the temperature, torque, air pressure, RPM, and cutting depth speed of the guide drill (151) during drilling are respectively measured by the sensor unit (16), and when the temperature, torque, air pressure, RPM, and cutting depth speed measured by the sensor unit (16) are input into the feature point extraction unit (25), the operation unit (21) refers to the pattern of the feature points extracted by the feature point extraction unit (25) and the artificial intelligence learning result, and determines the bone density of the implant target bone as one of Soft-Bone, Normal-Bone, and Hard-Bone and outputs the target result value.

[0082] As another example, the above-mentioned operation unit (21) may be artificial intelligence (AI) learned in the SVM (Support Vector Machine) method using a plurality of vector data (Vactor-Data) input through an external terminal (30), and the feature points extracted using the information value of each vector data (Vactor-Data) are extracted in the form of coordinates, and according to the distribution of these feature points, the operation unit (21) first sets a standard value for distinguishing between soft bones, normal bones, and hard bones in the direction in which the maximum margin remains.

[0083] Here, the meaning that the operation unit (21) sets the reference value in the direction in which the maximum margin remains is that, for example, in a situation in which the feature point coordinates of arbitrary vector data that should actually be determined to be soft bone are located close to the feature point coordinates of vector data determined to be normal bone, even if the reference value is set such that the bone density of arbitrary vector data corresponding to soft bone is incorrectly determined to be normal bone, the reference value can be set in the direction in which the margin is set to the maximum so that even if new types of vector data or actual measurement data are input, the feature points can be extracted and the bone density can be stably determined, thereby increasing universality.

[0084] Thereafter, when the operator performs initial drilling on the implant target bone using the guide drill (151) installed on the handpiece (10), the temperature, torque, air pressure, RPM, and cutting depth speed of the guide drill (151) during drilling are respectively measured by the sensor unit (16), and when the temperature, torque, air pressure, RPM, and cutting depth speed measured by the sensor unit (16) are input to the feature point extraction unit (25), the feature point extraction unit (25) extracts the coordinates of the feature points, and determines the bone as one of Soft-Bone, Normal-Bone, and Hard-Bone according to the coordinate positions of the extracted feature points.

[0085] When the bone density of the implant target bone is determined through any one of the above methods, the calculation unit (21) determines the size of the fixture to be implanted in the implantation hole based on the determined bone density, and displays this on an external terminal (30) connected wirelessly or wired so that the operator can check it.

[0086] Here, as shown in FIGS. 5 and 6, the content displayed on the external terminal (30) may be at least one selected from among a CT image of a patient that has been previously photographed, a CT composite image in which bone density is displayed in different colors on the CT image according to the bone density determined by the calculation unit (21), a fixture size determined by the calculation unit (21), a drill name and image required for processing an implantation hole in which a fixture of the size determined by the calculation unit (21) is implanted, etc.

[0087] Specifically, the size of the implant fixture is determined according to the bone density determined by the bone quality determination system (1) of the present invention, and the calculation unit (21) displays the guide drill (151) and the taper drill (152) required for implantation hole processing according to the determined fixture size at each treatment stage. In the case of implanting a ø4.5 mm fixture, as shown in FIG. 6, Softbone may be guided to sequentially use the guide drill (151), which is the No. 1 drill (15), and the taper drill (152), which is the No. 3 drill (15), of ø4.0 mm.

[0088] The determination of the diameter of the guide drill (151) and the taper drill (152) is performed by the calculation unit (21), and calls the vector data (Vactor-Data) having the bone density determined by the bone quality determination system (1) of the present invention as the target data, and when compared with the feature points of the information value used in determining the bone density of the patient, the diameter and usage order of the drill (15) used during the procedure are called from the vector data (Vactor-Data) having the smallest feature point error so as to guide the operator, and since the data learned by the bone quality determination system (1) is the vector data (Vactor-Data) of the procedure in which the cutting heat is drilled at a threshold temperature of 47℃ or lower, the determination of the diameter and usage order of the guide drill (151) and the taper drill (152) made by the calculation unit (21) of the bone quality determination system (1) can help an inexperienced operator control and manage the cutting heat at a threshold temperature of 47℃ or lower during implant surgery. It is.

[0089] Additionally, a display table (Display-Table) may be provided in the system so that the content to be displayed on the external terminal (30) can be selected according to the needs of the operator.

[0090] When implant placement in the target bone is completed through the above process, the bone density determined by the bone quality determination system (1) may be stored in a database (22), and a verification procedure of the verification unit (28) may be performed on the determined bone density.

[0091] Specifically, the verification unit (28) calls the patient's CT scan image stored in the database (22) and the patient's bone density determined by the bone quality determination system (1), and calculates an error value by comparing the bone density measured using the patient's CT scan image with the bone density determined by the bone quality determination system (1).

[0092] Here, the calculated error value is referenced when rewarded to the operation unit (21) or the feature point extraction unit (25), and specifically, the verification unit (28) calculates a loss function (LF) using the error value in the form of a 'mean square error' or a 'root mean square error', so that the loss function (LF) can be accumulated even if the error value is calculated as a positive or negative number, and changes the weight in the direction in which the loss function (LF) decreases so that accurate bone quality determination can be performed, thereby correcting and compensating the feature points input to the operation unit (21) or the feature point extraction unit (25), and if necessary, the margin for each bone density classification can be adjusted by reflecting the weight to the reference value set as a vector data learning result.

[0093] In addition, based on the bone density determined by the bone quality determination system (1), the size of the fixture to be implanted in the implantation hole is determined, and the diameter of the drill to be used for precision machining the implantation hole among the drills stored in the database (22) can also be determined together according to the bone density.

[0094] Here, the selection of the drill diameter is determined by comparing the measured bone density of the target data included in the vector data (Vactor-Data) with the bone density determined by the bone quality determination system (1), and by referring to the target data with the smallest error value or vector distance between the feature points used for determining the bone density among the target data, and recommending the drill (15) diameter for each treatment step used in the target data.

[0095] In addition, since the temperature of the drill (15) of the diameter used in the arbitrary bone density is stored together as data, the operation unit (21) of the bone quality determination system (1) determines that there is a problem with the life of the drill (15) or the drill (15) blade when the temperature of the drill (15) of the diameter used in the bone density during the procedure is different from the temperature of the drill (15) of the stored data by a predetermined error value or more, and can notify the operator of this by sending a notification to an external terminal (30).

[0096] This prevents excessive torque from being applied to the drill (15) by an inexperienced operator, thereby increasing the lifespan of the drill (15). In addition, if an excessive error in the drill cutting temperature is detected, it is determined that there is an abnormality in the drill lifespan and this is notified to the operator, thereby preventing damage to the patient's alveolar bone (111) due to using a drill (15) whose lifespan has expired.

[0097] In addition, a CT scan image may be output to an external terminal (30) including a monitor or smartphone connected to the main server (20) via wired or wireless means, and bone density and the number or name of the drill used for each treatment step may be displayed on the CT scan image.

[0098]

[0099] *At this time, when the number or name of the drill selected according to the fixture size is displayed on the CT scan image, the drill image previously stored in the database (22) can be displayed on an external terminal (30) including a monitor or smartphone for each treatment step, and the operator can select the drill necessary for implant placement by referring to the displayed drill number, drill name, drill image, etc.

[0100]

[0101] The embodiments described above may be implemented using hardware components, software components, or a combination of hardware components and software components. For example, the devices, methods, and components described in the embodiments may be implemented using one or more general-purpose computers or special-purpose computers, such as, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable gate array (FPGA), a programmable logic unit (PLU), a microprocessor, or any other device capable of executing instructions and responding to them. The processing device may execute an operating system (OS) and one or more software applications running on the operating system. The processing device may also access, store, manipulate, process, and generate data in response to the execution of the software. For ease of understanding, the processing device is sometimes described as being used alone; however, those skilled in the art will appreciate that the processing device may include multiple processing elements or multiple types of processing elements. For example, the processing device may include multiple processors, or one processor and one controller. Additionally, other processing configurations, such as parallel processors, are also possible.

[0102] The method according to the embodiment may be implemented in the form of program commands that can be executed through various computer means and recorded on a computer-readable medium. The computer-readable medium may include program commands, data files, data structures, etc., alone or in combination. The program commands recorded on the medium may be those specially designed and configured for the embodiment or may be those known and available to those skilled in the art of computer software. Examples of the computer-readable recording medium include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical media such as CDROMs and DVDs, magneto-optical media such as floptical disks, and hardware devices specially configured to store and execute program commands, such as ROMs, RAMs, and flash memories. Examples of the program commands include not only machine language codes generated by a compiler, but also high-level language codes that can be executed by a computer using an interpreter, etc. The hardware devices described above may be configured to operate as one or more software modules to perform the operations of the embodiment, and vice versa.

[0103] Software may include a computer program, code, instructions, or a combination of one or more of these, and may configure a processing device to perform a desired operation or may independently or collectively configure a processing device. Software or data may be permanently or temporarily embodied in any type of machine, component, physical device, virtual equipment, computer storage medium or device, or transmitted signal wave for interpretation by the processing device or for providing instructions or data to the processing device. Software may also be distributed over networked computer systems and stored or executed in a distributed manner. Software and data may be stored on one or more computer-readable recording media.

[0104] In the above content, the present invention has been described based on the drawings, and is not limited to the part described based on the drawings, and various changes can be made within the scope that does not depart from the gist of the present invention in the technical field to which the present invention belongs through the part described based on the drawings.

[0105]

[0106] The artificial intelligence-based bone quality determination system using the drilling temperature of the implant surgical kit according to the present invention can contribute to quantitatively improving the qualitative implant placement process based on the experience of the practitioner by determining the bone quality of the implant target bone and visually guiding the practitioner accordingly.

Claims

1. In a bone quality determination system including a handpiece having multiple sensor parts on a drill, The above bone quality determination system is, Drilling step for forming an implant hole in the target bone using a drill installed in the handpiece; A measuring step for detecting information values ​​including temperature, torque, air pressure, RPM, and cutting depth speed of the drill measured using a plurality of sensor units installed in the handpiece in the above drilling step; An artificial intelligence-based bone quality determination system using drilling temperature of an implant surgical kit, characterized in that the information value detected in the above measurement step is input into the calculation unit of the main server, and a bone density determination step is performed to calculate the bone density of the implant target bone according to the algorithm learned by the calculation unit.

2. In paragraph 1, The above main server is, An artificial intelligence-based bone quality determination system using drilling temperature of an implant surgical kit, characterized in that it includes a database that receives information values ​​from the handpiece and stores them in the form of relational data, or receives and stores arbitrary vector data through an external terminal connected to the main server by wire or wirelessly, and a calculation unit that calculates bone density using the information values ​​stored in the database.

3. In paragraph 2, The above main server is, An artificial intelligence-based bone quality determination system using drilling temperature of an implant surgical kit, characterized in that it further includes a verification unit that compares the bone density produced by the bone quality determination system with the bone density actually measured in the implant target bone to calculate an error value, and compensates (Reward) the weight of the calculation unit in the direction in which the calculated error value decreases.

4. In paragraph 3, The above operation unit, An artificial intelligence-based bone quality determination system using drilling temperature of an implant surgical kit, characterized in that when initially drilling an implant target bone using a guide drill, a drilling temperature measured by a sensor unit is input to a feature point extraction unit, and the bone density of the implant target bone is determined as one of soft bone, normal bone, and hard bone according to the pattern of feature points extracted by the feature point extraction unit.

5. In paragraph 4, The above operation unit, An artificial intelligence-based bone quality determination system using drilling temperature of an implant surgical kit characterized by displaying a preset color according to the bone density determined on a CT scan image of a photographed implant target bone and displaying it on a monitor or external terminal.

6. In paragraph 5, The above main server is, An artificial intelligence-based bone quality determination system using drilling temperature of an implant surgical kit, characterized in that the CT scan image of the implant target bone, which is colored according to the bone density and bone density determined by the above calculation unit, is stored as relational data in a database together with patient identification information including at least one selected from the patient classification number and resident registration number of the subject of the CT scan image.

7. In paragraph 1, The above operation unit, An artificial intelligence-based bone quality determination system using drilling temperature of an implant surgical kit characterized in that vector data including the measured drilling temperature is input while performing implant surgery using the drill on multiple artificial bones set to an arbitrary bone density so that the drill cutting heat is below a threshold temperature of 47℃, and is learned using an artificial neural network (ANN) method.

8. In paragraph 1, The above operation unit, An artificial intelligence-based bone quality determination system using drilling temperature of an implant surgical kit characterized in that vector data including the measured drilling temperature is input while performing implant surgery on multiple artificial bones set to an arbitrary bone density so that the drill cutting heat is below a threshold temperature of 47℃, and is learned using the SVM (Support Vector Machine) method.

9. In any one of paragraphs 1 to 8, An artificial intelligence-based bone quality determination system using drilling temperature of an implant surgical kit, characterized in that the size of a fixture to be implanted in a hole is determined based on the bone density determined by the above bone quality determination system, and the diameter of a drill to be used for precision machining the hole among drills stored in a database is determined together according to the bone density.

10. In paragraph 9, An artificial intelligence-based bone quality determination system using drilling temperature of an implant surgical kit, characterized in that a CT scan image is output to a monitor or external terminal connected to the main server via wired or wireless connection, and bone density and the number or name of the drill used for each procedure step are displayed on the CT scan image.

11. In paragraph 10, An artificial intelligence-based bone quality determination system using drilling temperature of an implant surgical kit, characterized in that when displaying the number or name of the drill determined by bone density on a CT scan image, the drill image pre-stored in the database is displayed on a monitor or external terminal for each procedure step.

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