KR20230054146A - Method and apparatus for supplying current for cancer treatment - Google Patents

Abstract

Provided are a method and apparatus for supplying current for cancer treatment. The method comprises the steps of: supplying current to a cancer cell area in a patient's body through at least one electrode inserted into the patient's body; measuring bio-impedance of cancer cells based on the supplied current; setting the frequency, intensity, and duty cycle of an electric field for tumor treatment fields (TTF) to be applied to the patient based on the measured bio-impedance; and providing the set electric field to the cancer cells.

Description

Current supply method and device for cancer treatment {METHOD AND APPARATUS FOR SUPPLYING CURRENT FOR CANCER TREATMENT}

The present invention relates to a method and apparatus for supplying current for cancer treatment.

Tumor Treating Fields (TTF) is a cancer treatment that disrupts cell division and induces apoptosis of cancer cells.

Conventional electric field tumor treatment is performed by attaching electrodes around a cancer site and continuously sending electric field signals to the patient's body.

However, since this method is performed by attaching electrodes on the patient's skin, there are limitations in stimulating cancer cells with electrodes outside the body, resulting in low treatment effect and high energy consumption of the treatment device.

Publication No. 10-2005-0045995, 2005.05.17.

The problem to be solved by the present invention is to provide a current supply method and apparatus for cancer treatment.

However, the problem to be solved by the present invention is not limited to the above problem, and other problems may exist.

A current supply method for cancer treatment according to an aspect of the present invention for solving the above problems includes supplying current to a cancer cell region in a patient's body through at least one electrode inserted into the patient's body, the supplied current Measuring the bio-impedance of the cancer cells based on, setting the frequency, intensity, and duty cycle of an electric field for Tumor Treating Fields (TTF) to be applied to the patient based on the measured bio-impedance; and and providing the set electric field to the cancer cells.

In addition, the duty cycle may be set within a range based on preset minimum and maximum values, and the frequency and intensity may be set to provide the electric field with alternating current and low intensity at an intermediate frequency.

The method may further include constructing an artificial intelligence-based learning model using the bioimpedance measured for each of a plurality of patients and the duty cycle set for each of the plurality of patients as input values and using a treatment result as an output value. can

In addition, the method may include, when a bio-impedance of a specific value is input, searching for a plurality of treatment results related to the input bio-impedance through the learning model, and most satisfying a predetermined condition among the searched plurality of treatment results. The method may further include determining one of the treatment results to be determined, and outputting a duty cycle corresponding to the determined treatment result as a result.

The method may further include measuring the size of the cancer cell based on the measured bio impedance and resetting at least one of the frequency, intensity, and duty cycle based on the measured size of the cancer cell. can

In addition, the method uses bioimpedance measured for each patient, the size of a cancer cell measured according to the measured bioimpedance for each patient, and a duty cycle set for each patient as input values and a treatment result as a result. A step of constructing an artificial intelligence-based learning model using the value as a value may be further included.

In addition, the method may include, when a bio-impedance of a specific value is input, predicting a size of a cancer cell corresponding to the input bio-impedance through the learning model, a plurality of data related to the predicted size and the input bio-impedance Searching for a treatment result, determining one treatment result that most satisfies a predetermined condition among the searched plurality of treatment results, and outputting a duty cycle corresponding to the determined treatment result as a result. can do.

An apparatus for supplying current for cancer treatment according to another aspect of the present invention for solving the above problems is a communication unit, at least one electrode inserted into a patient's body, and at least one process for supplying current for cancer treatment. a memory and a processor operating according to the process, wherein the processor supplies a current to a cancer cell region in the body through the at least one electrode based on the process, and the cancer cell based on the supplied current Measuring bio impedance (bio impedance), setting the frequency, intensity and duty cycle of the electric field for TTF (Tumor Treating Fields) to be applied to the patient based on the measured bio impedance, and setting the electric field to the cancer cells. to provide.

In addition, the duty cycle may be set within a range based on preset minimum and maximum values, and the frequency and intensity may be set to provide the electric field with alternating current and low intensity at an intermediate frequency.

In addition, the processor may construct an artificial intelligence-based learning model by using the bioimpedance measured for each patient and the duty cycle set for each patient as an input value and using a treatment result as an output value.

In addition, when a bio-impedance of a specific value is input, the processor searches for a plurality of treatment results related to the input bio-impedance through the learning model, and among the searched plurality of treatment results, a predetermined condition is most satisfied. Any one treatment result may be determined, and a duty cycle corresponding to the determined treatment result may be output as a result.

Also, the processor may measure the size of the cancer cell based on the measured bio-impedance, and reset at least one of the frequency, intensity, and duty cycle based on the measured size of the cancer cell.

In addition, the processor takes the bioimpedance measured for each of a plurality of patients, the size of cancer cells measured according to the measured bioimpedance for each of the plurality of patients, and the duty cycle set for each of the plurality of patients as input values, and outputs a treatment result as an input value. It can be used as a value to build an artificial intelligence-based learning model.

In addition, when a bio-impedance of a specific value is input, the processor predicts the size of cancer cells corresponding to the input bio-impedance through the learning model, and provides a plurality of treatments related to the predicted size and the input bio-impedance. Results may be searched, one treatment result that most satisfies a preset condition among the searched plurality of treatment results may be determined, and a duty cycle corresponding to the determined treatment result may be output as a result.

In addition to this, other methods for implementing the present invention, other systems, and methods stored in computer-readable recording media may be further provided to execute the methods.

Other specific details of the invention are included in the detailed description and drawings.

According to the present invention described above, efficient energy management is possible by appropriately adjusting the duty cycle according to the patient's condition without setting it to an indiscriminately high value. That is, energy consumed by the battery is saved, and through this, the operating time of the device can be extended.

In addition, since a large-capacity battery is not required, the device can be miniaturized by using a small-sized battery, and through this, usability for patients can be maximized.

In addition, the treatment effect can be enhanced by inserting the electrode into the patient's body and providing stimulation directly to the cancer cell site.

The effects of the present invention are not limited to the effects mentioned above, and other effects not mentioned will be clearly understood by those skilled in the art from the description below.

1 is a view for explaining an actual use of a current supply device for cancer treatment according to the present invention.
2 is a diagram for explaining a system including the current supply device of FIG. 1 according to the present invention.
3 is a flow chart of a current supply method for cancer treatment according to the present invention.
4 is a diagram for explaining a duty cycle according to the present invention.

Advantages and features of the present invention, and methods of achieving them, will become clear with reference to the detailed description of the following embodiments taken in conjunction with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below, but may be implemented in various different forms, only these embodiments are intended to complete the disclosure of the present invention, and are common in the art to which the present invention belongs. It is provided to fully inform the person skilled in the art of the scope of the invention, and the invention is only defined by the scope of the claims.

Terminology used herein is for describing the embodiments and is not intended to limit the present invention. In this specification, singular forms also include plural forms unless specifically stated otherwise in a phrase. As used herein, "comprises" and/or "comprising" does not exclude the presence or addition of one or more other elements other than the recited elements. Like reference numerals throughout the specification refer to like elements, and “and/or” includes each and every combination of one or more of the recited elements. Although "first", "second", etc. are used to describe various components, these components are not limited by these terms, of course. These terms are only used to distinguish one component from another. Accordingly, it goes without saying that the first element mentioned below may also be the second element within the technical spirit of the present invention.

Unless otherwise defined, all terms (including technical and scientific terms) used in this specification may be used with meanings commonly understood by those skilled in the art to which the present invention belongs. In addition, terms defined in commonly used dictionaries are not interpreted ideally or excessively unless explicitly specifically defined.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.

Prior to the description, the meaning of the terms used in this specification will be briefly described. However, it should be noted that the description of terms is intended to help the understanding of the present specification, and is not used in the sense of limiting the technical spirit of the present invention unless explicitly described as limiting the present invention.

In this specification, 'device' includes all various devices capable of providing results to users by performing calculation processing. For example, the devices can be computers and mobile terminals. The computer may be in the form of a server receiving a request from a client and processing information. Also, a computer may correspond to a sequencing device that performs sequencing. The mobile terminal includes a mobile phone, a smart phone, a personal digital assistants (PDA), a portable multimedia player (PMP), a navigation device, a notebook PC, a slate PC, a tablet PC, and an ultrabook. ), a wearable device (eg, a watch type terminal (smartwatch), a glass type terminal (smart glass), a head mounted display (HMD)), and the like may be included.

In this specification, a 'learning model' is a learning model based on artificial intelligence, and can be learned based on various artificial intelligence algorithms. For example, algorithms for learning such as CNN, DNN, RNN, KNN, and support vector machine (SVM) are all applicable.

In the present specification, 'patient' may include all types of cancer patients. For example, it may include brain cancer patients, pancreatic cancer patients, liver cancer patients, and the like.

1 is a view for explaining an actual use of a current supply device for cancer treatment according to the present invention.

2 is a diagram for explaining a system including the current supply device of FIG. 1 according to the present invention.

3 is a flow chart of a current supply method for cancer treatment according to the present invention.

4 is a diagram for explaining a duty cycle according to the present invention.

Referring to FIG. 1, the current supplying device 10 (hereinafter referred to as the current supplying device) for cancer treatment according to the present invention is formed such that at least one electrode 11 is connected to the current supplying device 10 by wire as an individual device. can However, without being limited thereto, the electrode 11 may be connected to the current supply device 10 wirelessly.

The current supply device 10 of the present invention is small and light, so that a patient can attach it to his or her body or put it in a pocket so that it can be conveniently and lightly carried.

Referring to FIG. 2 , the current supply device 10 may include at least one electrode 11, a battery 12, a communication unit 13, and a processor 14. However, in some embodiments, the current supply device 10 may include fewer or more components than those shown in FIG. 2 .

At least one electrode 11 may be inserted into a patient's body to supply current to a region in the patient's body where cancer cells exist.

Depending on the embodiment, at least one electrode 11 may be formed in a size small enough to be inserted into a patient's body. For example, the electrode 11 may be formed to have a width of 12 mm, a length of 19 mm, and a thickness of 0.5 mm.

Depending on the embodiment, when the number of electrodes 11 is plural, the distance between each electrode may be 15 mm.

The battery 12 may provide power so that the electrode 11, the communication unit 13, and the processor 14 perform their respective functions. The battery 12 provides power so that an electric field is applied to the cancer cell region through the electrode 11 according to a duty cycle to be described later. At this time, the processor 14 appropriately sets the duty cycle to reduce energy consumption of the battery 12. can be reduced

Depending on the embodiment, when the electrode 11 is wirelessly connected to the current supply device 10, the electrode 11 may be implanted in a patient's body and operated through wireless charging.

According to the embodiment, when the electrode 11 is wirelessly connected to the current supply device 10, a program (application) related to the function of the current supply device 10 is installed on a patient's terminal (eg, smart phone). , the electrode 11 can be operated according to the control of the patient's terminal. That is, in this case, the patient's terminal itself may serve as the current supply device 10 .

The communication unit 13 may receive a current value from the electrode 11 through a communication network. More specifically, the communication unit 13 may receive a current value measured from a current sensor (not shown) provided on the electrode 11 .

The communication unit 13 may transmit and receive various information to and from the external device 20 through a communication network.

Here, the external device 20 may include a terminal used by a patient, a terminal used by a doctor, and a server of a medical institution, but is not limited thereto.

The external device 20 may be connected to the current supply device 10 through a network, such as a mobile phone, smart phone, PDA (Personal Digital Assistant), PMP (Portable Multimedia Player), tablet PC, desktop, laptop, etc., and includes a camera. and may be all types of handheld-based wireless communication devices capable of inputting and outputting various information through a screen.

Users (patients, doctors, medical institutions, etc.) of the external device 20 can receive various types of information through the current supply device 10 and monitor cancer cell conditions and treatment progress.

Here, various types of communication networks may be used as the communication network, for example, wireless LANs such as WLAN (Wireless LAN), Wi-Fi, Wibro, Wimax, and High Speed Downlink Packet Access (HSDPA). A wired communication method such as communication method or Ethernet, xDSL (ADSL, VDSL), HFC (Hybrid Fiber Coax), FTTC (Fiber to The Curb), FTTH (Fiber To The Home) may be used.

On the other hand, the communication network is not limited to the communication methods presented above, and may include all other types of communication methods that are widely known or will be developed in the future in addition to the above communication methods.

A memory (not shown) may store at least one process for supplying current for cancer treatment and a learning model built based on artificial intelligence.

The processor 14 may perform various operations related to overall functions for controlling the current supply device 10 and a learning model built on the basis of artificial intelligence. For example, the processor 14 may perform various operations associated with an overall function for controlling the current supply device 10 and a learning model built based on artificial intelligence by executing programs stored in a memory (not shown). . The processor 14 is implemented as a Central Processing Unit (CPU), a Graphic Processing Unit (GPU), a Digital Signal Processor (DSP), a Neural Processing Unit (NPU), or an Application Processor (AP) provided in the current supply unit 10. It may be, but is not limited thereto.

The processor 14 may include an artificial intelligence module (not shown).

The artificial intelligence module (not shown) serves to set a treatment (more specifically, the duty cycle of the electromagnetic field) suitable for the condition of the patient's cancer cells using an artificial intelligence-based learning model.

The artificial intelligence module (not shown) may consist of one or more cores, and may include a central processing unit (CPU), a general purpose graphics processing unit (GPGPU), and a tensor processing unit (TPU) of a computing device. : tensor processing unit), and a processor for data analysis and deep learning. The artificial intelligence module (not shown) may read a computer program stored in a memory and process data for learning according to an embodiment of the present invention. According to an embodiment of the present invention, an artificial intelligence module (not shown) may perform an operation for learning a neural network. The artificial intelligence module (not shown) processes input data for learning in deep learning (DL: deep learning), extracts features from input data, calculates errors, and updates neural network weights using backpropagation. Calculations can be performed for learning. At least one of the CPU, GPGPU, and TPU of the artificial intelligence module (not shown) may process learning of the network function. For example, the CPU and GPGPU can process learning of network functions and data classification using network functions. In addition, in one embodiment of the present invention, the learning of a network function and data classification using a network function may be processed by using processors of a plurality of computing devices together. In addition, a computer program executed in a computing device according to an embodiment of the present invention may be a CPU, GPGPU or TPU executable program.

Hereinafter, with reference to FIG. 3, the current supplying device 10 will be described in detail for performing the current supplying method for cancer treatment of the present invention. Hereinafter, the steps of FIG. 3 are described as being performed by the processor 14, but the current supply device 10 itself may perform the steps.

The processor 14 may supply current to the cancer cell region in the patient's body through at least one electrode 11 inserted into the patient's body (S110).

The processor 14 may measure the bio impedance of the cancer cell based on the supplied current (S120).

In more detail, the processor 14 may apply a current to the electrode 11 implanted in the cancer cell region of the patient's body. A current applied through the electrode 11 may be measured by a current sensor. The current sensor may be provided on the electrode 11, but is not limited thereto and may be included in the current supply device 10.

The processor 14 may measure the bio impedance of the cancer cell based on the current value measured by the current sensor. Since cancer cells have higher electrical resistance than normal cells, the bioimpedance of a cancer cell site may be measured as a larger value than the bioimpedance of surrounding normal cells.

The processor 14 may set the frequency, strength, and duty cycle of an electric field for Tumor Treating Fields (TTF) to be applied to the patient based on the measured bioimpedance (S130).

Here, the frequency and the strength may be set so that the electric field is provided with alternating current and low intensity at an intermediate frequency. For example, the frequency may be set to 100 kHz to 300 kHz, and the intensity may be set to 1 V/cm to 3 V/cm. In the present invention, the frequency and the intensity may be set to the same value for each patient, but are not limited thereto.

Here, the duty cycle may be set within a range based on preset minimum and maximum values. The higher the duty cycle, the better the effect in killing cells. However, if the duty cycle is too high, there is neurotoxicity, so it is necessary to set the maximum value. It is also necessary to set a minimum value because too low a duty cycle has little effect on killing cells.

For example, the minimum value may be set to 50% and the maximum value may be set to 90%. Based on 1 minute, in the case of 50% duty, it is in an 'on' state (electric field is provided) for 30 seconds, and is in an 'off' state (electric field is not provided) for 30 seconds.

Depending on the embodiment, the duty cycle may be set to a larger value as the value of bioimpedance of the cancer cell increases.

According to the embodiment, significant cancer cell death can be induced when the duty cycle is 75%. The results of applying a duty cycle of 75% and 100% are similar (an average of 75.5% cancer cell reduction compared to the control group), but adverse effects on normal cells (i.e., 100% duty cycle) , neurotoxicity). Therefore, the most effective and safe treatment is possible when the duty cycle is 75%.

The processor 14 may provide the set electric field to the cancer cells (S140).

That is, treatment may be performed by providing an electric field based on the set frequency, intensity, and duty cycle to the patient's cancer cells. Accordingly, since a treatment tailored to the state of cancer cells of a patient is used, it is possible to efficiently treat each patient.

According to an embodiment, when treatment is performed on cancer cells of a specific patient by setting the frequency to 200 kHz, the intensity to 2.5 V/cm, and changing the duty cycle to three cases of 50%, 75%, and 100%, FIG. Referring to, it can be seen that the case similar to that of the control is when the duty is applied at 75%.

Although not shown in FIG. 3, the present invention includes the steps of constructing an artificial intelligence-based learning model using the bioimpedance measured for each patient and the duty cycle set for each patient as input values and using the treatment result as an output value. may further include. That is, the above-described artificial intelligence module (not shown) can build a learning model by learning what the treatment results were when treatment was performed using the bioimpedance value and duty cycle according to the cancer cell state of each patient.

After the learning model is built in this way, when a bio-impedance of a specific value for a specific patient is input, the processor 14 searches for a plurality of treatment results related to the input bio-impedance through the learning model, and the searched plurality Among the treatment results, one treatment result that most satisfies a preset condition may be determined, and a duty cycle corresponding to the determined treatment result may be output as a result. Here, each operation may be performed by the processor 14 or may be performed by the learning model itself.

For example, if the condition is 'recovery within the shortest period', the treatment results for all treatments that have been performed for a specific value of bioimpedance are searched, and among them, the treatment results when cancer cells are killed the fastest are determined. , The duty cycle when the determined treatment result is obtained can be determined as the optimal treatment and set as the duty cycle applied for the treatment of the specific patient.

Although not shown in FIG. 3, the present invention includes measuring the size of the cancer cells based on the measured bio-impedance and resetting at least one of the frequency, intensity, and duty cycle based on the measured size of the cancer cells. may further include. That is, since setting the duty cycle to a larger value as the size of the cancer cell increases is more effective for treatment, if the preset duty cycle value is not suitable for the measured size, the duty cycle value may be adjusted to an appropriate value.

In addition, the present invention uses the bioimpedance measured for each patient, the size of a cancer cell measured according to the measured bioimpedance for each patient, and the duty cycle set for each patient as an input value, and the treatment result as a result value. It may further include the step of building an artificial intelligence-based learning model using

That is, the above-described artificial intelligence module (not shown) can build a learning model by learning what the treatment result was when treatment was performed using the bioimpedance value and duty cycle according to the state (size) of each cancer cell of each patient. there is.

After the learning model is built, the processor 14 predicts the size of cancer cells corresponding to the input bio-impedance through the learning model when a bio-impedance of a specific value for a specific patient is input, and the predicted size and searching for a plurality of treatment results related to the input bioimpedance, determining one treatment result that most satisfies a predetermined condition among the searched plurality of treatment results, and setting a duty cycle corresponding to the determined treatment result. The result can be output. Here, each operation may be performed by the processor 14 or may be performed by the learning model itself.

For example, if the condition is 'recovery within the shortest period of time', among all the treatments that have been performed for a specific value of bioimpedance, a treatment result when the size of the measured cancer cell is the same is searched, and among them, the cancer cell grows the fastest. The treatment result in the case of death is determined, and the duty cycle in the case of the determined treatment result is determined as the optimal treatment, and the duty cycle applied for the treatment of the specific patient may be set.

3 describes that steps S110 and step S140 are sequentially executed, but this is merely an example of the technical idea of this embodiment, and those skilled in the art to which this embodiment belongs will Since it will be possible to change and execute the order described in FIG. 3 without departing from the essential characteristics or to perform steps S110 and S140 in parallel, it will be possible to apply various modifications and variations, so FIG. 3 is not limited to a time-series order. .

Meanwhile, in the above description, steps S110 and S140 may be further divided into additional steps or combined into fewer steps according to an embodiment of the present invention. Also, some steps may be omitted if necessary, and the order of steps may be changed.

The method according to the present invention described above may be implemented as a program (or application) to be executed in combination with a computer, which is hardware, and stored in a medium. Here, the computer may be the device 10 described above.

The above-mentioned program is C, C++, JAVA, Ruby, C, C++, JAVA, Ruby, which the processor (CPU) of the computer can read through the device interface of the computer so that the computer reads the program and executes the methods implemented as a program. It may include a code coded in a computer language such as machine language. These codes may include functional codes related to functions defining necessary functions for executing the methods, and include control codes related to execution procedures necessary for the processor of the computer to execute the functions according to a predetermined procedure. can do. In addition, these codes may further include memory reference related codes for which location (address address) of the computer's internal or external memory should be referenced for additional information or media required for the computer's processor to execute the functions. there is. In addition, when the processor of the computer needs to communicate with any other remote computer or server in order to execute the functions, the code uses the computer's communication module to determine how to communicate with any other remote computer or server. It may further include communication-related codes for whether to communicate, what kind of information or media to transmit/receive during communication, and the like.

The storage medium is not a medium that stores data for a short moment, such as a register, cache, or memory, but a medium that stores data semi-permanently and is readable by a device. Specifically, examples of the storage medium include ROM, RAM, CD-ROM, magnetic tape, floppy disk, optical data storage device, etc., but are not limited thereto. That is, the program may be stored in various recording media on various servers accessible by the computer or various recording media on the user's computer. In addition, the medium may be distributed to computer systems connected by a network, and computer readable codes may be stored in a distributed manner.

The above description of the present invention is for illustrative purposes, and those skilled in the art can understand that it can be easily modified into other specific forms without changing the technical spirit or essential features of the present invention. will be. Therefore, the embodiments described above should be understood as illustrative in all respects and not limiting. For example, each component described as a single type may be implemented in a distributed manner, and similarly, components described as distributed may be implemented in a combined form.

The scope of the present invention is indicated by the following claims rather than the detailed description above, and all changes or modifications derived from the meaning and scope of the claims and equivalent concepts should be interpreted as being included in the scope of the present invention. do.

10: current supply
11: electrode
12: Battery
13: Ministry of Communications
14: Processor
20: external device

Claims (15)

In the method performed by the current supply device for cancer treatment,
supplying current to a region of cancer cells in the patient's body through at least one electrode inserted into the patient's body;
measuring bio impedance of the cancer cells based on the supplied current;
setting a frequency, intensity, and duty cycle of an electric field for Tumor Treating Fields (TTF) to be applied to the patient based on the measured bio-impedance; and
A current supply method for cancer treatment, comprising: providing the set electric field to the cancer cells. According to claim 1,
The duty cycle is set within a range based on preset minimum and maximum values,
The frequency and intensity are set so that the electric field is provided with alternating current and low intensity at an intermediate frequency, a current supply method for cancer treatment. According to claim 1,
Constructing an artificial intelligence-based learning model using the bioimpedance measured for each patient and the duty cycle set for each patient as an input value and using the treatment result as a result value; further comprising, current for cancer treatment supply method. According to claim 3,
searching for a plurality of treatment results related to the input bio-impedance through the learning model when a bio-impedance of a specific value is input;
determining one treatment result that most satisfies a predetermined condition among the searched plurality of treatment results; and
Outputting a duty cycle corresponding to the determined treatment result as a result; further comprising a current supply method for cancer treatment. According to claim 1,
measuring the size of the cancer cells based on the measured bio-impedance; and
Resetting at least one of the frequency, intensity, and duty cycle based on the measured size of the cancer cells; further comprising a current supply method for cancer treatment. According to claim 5,
Artificial intelligence using the bioimpedance measured for each patient, the size of cancer cells measured according to the measured bioimpedance for each patient, and the duty cycle set for each patient as an input value and using the treatment result as a result value Building a based learning model; further comprising a current supply method for cancer treatment. According to claim 6,
predicting a size of a cancer cell corresponding to the input bioimpedance through the learning model when a bioimpedance of a specific value is input;
searching for a plurality of treatment results related to the predicted size and the input bioimpedance;
determining one treatment result that most satisfies a predetermined condition among the searched plurality of treatment results; and
Outputting a duty cycle corresponding to the determined treatment result as a result; further comprising a current supply method for cancer treatment. communications department;
at least one electrode inserted into a patient's body;
a memory storing at least one process for supplying current for cancer treatment; and
A processor operating according to the process; includes,
The processor, based on the process,
supplying current to a region of cancer cells in the body through the at least one electrode;
Based on the supplied current, the bio impedance of the cancer cells is measured,
Setting a frequency, intensity, and duty cycle of an electric field for Tumor Treating Fields (TTF) to be applied to the patient based on the measured bioimpedance;
A current supply device for cancer treatment, which provides the set electric field to the cancer cells. According to claim 8,
The duty cycle is set within a range based on preset minimum and maximum values,
The frequency and intensity are set so that the electric field is provided with an alternating current and low intensity at an intermediate frequency, a current supply device for cancer treatment. According to claim 8,
the processor,
A current supply device for cancer treatment, which constructs an artificial intelligence-based learning model using bioimpedance measured for each patient and a duty cycle set for each patient as an input value and a treatment result as a result value. According to claim 10,
When a bioimpedance of a specific value is input, the processor:
Searching for a plurality of treatment results related to the input bioimpedance through the learning model;
Determining any one treatment result that most satisfies a predetermined condition among the searched plurality of treatment results;
A current supply device for cancer treatment that outputs a duty cycle corresponding to the determined treatment result as a result. According to claim 8,
the processor,
Measuring the size of the cancer cells based on the measured bioimpedance;
Based on the measured size of the cancer cell, resetting at least one of the frequency, intensity and duty cycle, the current supply device for cancer treatment. According to claim 12,
the processor,
Artificial intelligence using the bioimpedance measured for each patient, the size of cancer cells measured according to the measured bioimpedance for each patient, and the duty cycle set for each patient as an input value and using the treatment result as a result value A current supply device for cancer treatment that builds a learning model based on According to claim 13,
When a bioimpedance of a specific value is input, the processor:
Predicting the size of cancer cells corresponding to the input bioimpedance through the learning model;
Searching for a plurality of treatment results related to the predicted size and the input bioimpedance;
Determining any one treatment result that most satisfies a predetermined condition among the searched plurality of treatment results;
A current supply device for cancer treatment that outputs a duty cycle corresponding to the determined treatment result as a result. In a program stored in a computer readable recording medium coupled to a computer to execute a current supply method for cancer treatment,
said program,
a first process of supplying current to a region of cancer cells in a patient's body through at least one electrode inserted into the patient's body;
a second process of measuring bio impedance of the cancer cells based on the supplied current;
a third process of setting a frequency, strength, and duty cycle of an electric field for Tumor Treating Fields (TTF) to be applied to the patient based on the measured bioimpedance; and
A fourth process of providing the set electric field to the cancer cells; to perform, the program.

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