Cognitive weakness intervention system based on 3D printing technology

Abstract

The invention discloses a cognitive weakness intervention system based on a 3D printing technology. The cognitive weakness intervention system comprises a skull scanning device, a 3D printing device, an upper computer, an MCU and a cloud server. The skull scanning equipment is used for scanning the skull of a patient and performing noninvasive, painless and radiation-free scanning on the head of a human body by utilizing the principles of a magnetic field and radio frequency electromagnetic waves so as to obtain high-definition image data; and the obtained high-definition image data is sent to the 3D printing equipment, and the 3D printing equipment accurately determines three cognitive targets involving execution function, semantics and scene memory and primary movement brain region targets involving body function decline. According to a 3D structure of the head, a head ring is customized, and the four magnetic rings correspondingly relate to four target spots of a primary movement brain area with execution function, semantic and scene memory and body function decline.

Description

Technical Field

[0001] The present invention relates to the technical field of cognitive impairment intervention systems, and in particular to a cognitive impairment intervention system based on 3D printing technology. Background Art

[0002] Cognitive decline, a new hotspot in geriatric medicine and geriatric health research, refers to a syndrome characterized by the simultaneous presence of physical frailty and subjective cognitive decline or mild cognitive impairment, without the presence of dementia. Cognitive decline represents an intermediate stage between physical frailty and dementia due to neurodegenerative diseases, and carries a higher risk of progression to dementia and a poorer prognosis than isolated physical frailty and age-related cognitive impairment. Without timely intervention, it significantly increases adverse health outcomes such as disability, falls, hospitalization, disability, and death in older adults, placing a heavy burden on individuals, families, healthcare systems, and society. Its progression is reversible, and potential subtypes of cognitive decline represent a crucial "window period" for dementia prevention and treatment. Therefore, effectively identifying individuals at increased risk for cognitive decline and implementing early, home-based interventions are crucial.

[0003] Traditional methods of identifying cognitive decline and follow-up are often limited by inefficiency and data quality issues. Elderly people with cognitive decline are mainly distributed in the community, and different elderly people have their own personalized needs, which cannot be generalized. In addition, with the development of big data and artificial intelligence technologies, as well as the gradual popularization of smart medical care, medical data analysis and identification based on big data and artificial intelligence have become possible; collect and process sample data from more sources, establish target prediction models, and set personalized intervention plans based on different prediction situations.

[0004] Currently, no effective solutions have been proposed for the problems in related technologies. Summary of the Invention

[0005] In response to the problems in the related art, the present invention proposes a cognitive impairment intervention system based on 3D printing technology to overcome the above-mentioned technical problems existing in the existing related art.

[0006] To this end, the specific technical solutions adopted in the present invention are as follows:

[0007] A cognitive impairment intervention system based on 3D printing technology, including a skull scanning device, a 3D printing device, a host computer, an MCU, and a cloud server;

[0008] The skull scanning device is used to scan the patient's skull. By utilizing the principles of magnetic field and radio frequency electromagnetic waves, it performs a non-invasive, painless, and radiation-free scan of the human head to obtain high-definition imaging data.

[0009] The acquired high-definition image data is sent to the 3D printing device, which accurately determines three cognitive targets involving executive function, semantics, and episodic memory, and identifies the primary motor brain area where physical function declines;

[0010] The host computer includes a power module, a communication module and a control module;

[0011] The MCU includes circuit control, driver, magnetic coil, current limiting protection, capacitor and temperature sensor;

[0012] The cloud server includes decision-making algorithms, user logs, and interaction controls;

[0013] The cloud server sends personalized iTBS to the motor brain area and the corresponding cognitive cluster brain area based on the subtype of cognitive decline, achieving rehabilitation of cognitive and physical functions.

[0014] Preferably, the decision algorithm, the user log and the interactive control are interconnected.

[0015] Preferably, the circuit control is connected to the host computer, the current limiting protection, the capacitor, the temperature sensor and the decision algorithm respectively.

[0016] Preferably, the other end of the circuit control is connected to the driver, and the other end of the driver is connected to the magnetic coil and the capacitor.

[0017] Preferably, the other end of the magnetic coil is connected to the temperature sensor and the interactive control.

[0018] Preferably, the MCU also includes a data acquisition module, a data analysis module, a data generation module, a data storage module and a data comparison module; the cloud server is unidirectionally connected to the data acquisition module; the data acquisition module is unidirectionally connected to the data analysis module; the data analysis module is unidirectionally connected to the data generation module; the data generation module is unidirectionally connected to the data storage module; and the data storage module is unidirectionally connected to the data comparison module.

[0019] Preferably, the cloud server also includes a signal transceiver module, a scene construction module, a task design module, a task guidance module, an environment interaction module and a cognitive assessment module; the signal transceiver module is bidirectionally connected to the WiFi module; the communication module and the signal transceiver module transmit signals through the WiFi antenna; the signal transceiver module is bidirectionally connected to the scene construction module; the scene construction module is bidirectionally connected to the task design module; the task design module is bidirectionally connected to the task guidance module; the task guidance module is bidirectionally connected to the environment interaction module; and the environment interaction module is unidirectionally connected to the cognitive assessment module.

[0020] Preferably, the host computer also includes: constructing a basic data-score value mapping table, including score values ​​corresponding to different numerical intervals of age, height, weight, sleep data, personal chronic disease history and family chronic disease history, quantizing the basic data into component values ​​according to the basic data-score value mapping table, and then normalizing all score values ​​to the range of [0, 1] and splicing them to obtain the basic data feature vector.

[0021] Preferably, the skull scanning device further comprises: combining 3D scanning technology with a 10-20 channel electroencephalogram (EEG) cap, and applying EEG navigation technology to accurately locate the patient's cerebral cortex area;

[0022] The patient wears a multi-channel EEG cap, and a high-precision digital model of the anatomical structure of the head is obtained through 3D scanning;

[0023] Using the sensor positions in the EEG cap, precise registration is performed using 3D scan data to match EEG channels to the individual’s anatomy.

[0024] The navigation system uses the 10-20 international standard electrode system (10-20 system) to locate specific brain regions. The following areas are of particular interest:

[0025] Right primary motor cortex (Primary Motor Cortex, Right Hemisphere)

[0026] Located in the precentral gyrus of the brain, it is responsible for motor control, especially movement on the opposite side of the body.

[0027] Left ventrolateral prefrontal cortex (Left Hemisphere)

[0028] Located in the prefrontal region of the brain, it is involved in cognitive functions such as decision-making, planning, problem-solving, and inhibitory control.

[0029] Anterior Cingulate Cortex, Left Hemisphere

[0030] Located in the cingulate gyrus region of the brain, it is involved in emotion regulation, pain processing, as well as attention and decision-making.

[0031] Left dorsolateral frontal cortex (Dorsolateral Prefrontal Cortex, Left Hemisphere)

[0032] Located in the dorsolateral part of the frontal lobe, it is associated with working memory, executive function, goal-directed behavior, and emotion regulation.

[0033] Preferably, the decision algorithm further includes: a cloud-based decision mechanism for remotely controlling transcranial magnetic stimulation (TMS) that automatically develops a personalized stimulation plan for each patient by integrating patient data, treatment needs, and real-time feedback; the process of this mechanism is as follows:

[0034] Data collection and upload: The patient's EEG data, clinical information, and treatment progress are uploaded to the cloud through the device. 3D scanning data is combined with EEG data to provide further accurate brain region positioning information;

[0035] Cloud-based decision-making model: The cloud-based system uses real-time analysis of patients' EEG data, individual differences, and established treatment plans. Based on machine learning or rule-based algorithms, it automatically optimizes treatment parameters such as stimulation frequency, number of bursts, and stimulation intensity. This system combines clinical guidelines with personalized data to tailor treatment strategies to individual patient needs.

[0036] Stimulation frequency and number of trains setting:

[0037] Stimulation Frequency: Based on the patient's pathological condition and treatment progress, the cloud system dynamically adjusts the stimulation frequency to regulate the excitability of the cerebral cortex;

[0038] Train Number: The cloud system flexibly sets the number of times each stimulation training is performed (train number) based on treatment goals and patient feedback to ensure the continuity and effectiveness of the stimulation effect;

[0039] Treatment feedback mechanism: Patient treatment progress and feedback (e.g., efficacy evaluation, symptom improvement, etc.) are monitored in real time via a cloud-based platform. The cloud-based decision-making system adjusts treatment plans based on this feedback, including fine-tuning stimulation parameters or changing treatment plans.

[0040] Remote Monitoring and Adjustment: During treatment, doctors can remotely monitor the patient's condition through the cloud platform and further adjust stimulation parameters based on real-time data to maximize treatment effectiveness. This mechanism allows for remote control of the TMS device, precisely adjusting stimulation intensity, frequency, and number of bursts, enabling personalized and precise treatment.

[0041] This cloud-based decision-making mechanism can not only ensure the treatment effect of patients, but also improve the personalization and flexibility of treatment, reduce human intervention, and improve the scientificity and efficiency of treatment.

[0042] The beneficial effects of the present invention are: through the precise registration of 3D scanning technology and the real-time navigation of the EEG cap, these key brain areas can be located with high precision, providing a more accurate reference for the spatial analysis of the EEG, thereby improving the ability to analyze neural activity;

[0043] Through skull scanning combined with 3D printing, we accurately identified three cognitive targets involving executive function, semantics, and episodic memory, as well as primary motor brain targets associated with decreased physical function. Based on the 3D structure of the skull, we customized a headband and four magnetic rings corresponding to the four targets of executive function, semantics, episodic memory, and primary motor brain targets associated with decreased physical function. The control system delivers the following three transcranial magnetic stimulation modes for adaptive matching based on the subtype of cognitive impairment:

[0044] 1. Intermittent Theta Burst Stimulation (iTBS): Suitable for patients who need to activate low-functioning brain regions (such as those with impaired executive function or motor function), iTBS has the potential to rapidly promote cortical excitability and neuroplasticity. iTBS is commonly used to activate the left dorsolateral prefrontal cortex (DLPFC) to improve executive function, and can also be used to activate the motor cortex to enhance physical function recovery.

[0045] 2. Continuous Theta Burst Stimulation (cTBS): Suitable for patients with overactive or functional imbalanced brain regions, such as those with semantic confusion or contextual memory impairment, cTBS can suppress cortical hyperexcitability and promote neural network remodeling. cTBS is often used in abnormally active areas, such as the right prefrontal cortex or anterior cingulate gyrus, to help recalibrate brain networks.

[0046] 3. Repetitive transcranial magnetic stimulation (rTMS): The system selects high- or low-frequency rTMS based on the excitability of the target area. High-frequency rTMS (≥5Hz) is used to enhance cortical activity and is often used in executive and language-related areas; low-frequency rTMS (≤1Hz) is used to suppress abnormally active brain regions and is suitable for overactive episodic memory networks. This treatment modality offers a stable rhythm and a wide range of applications, including for patients with cognitive decline and comorbidities such as anxiety and depression.

[0047] Through the intelligent comparison mechanism of the above three stimulation paradigms, the system dynamically evaluates the neural response patterns in iTBS, cTBS and rTMS, and combines the patient's EEG characteristics, brain region metabolic levels and previous treatment responses to achieve real-time matching and adjustment of the individual's optimal stimulation plan, thereby improving the combined rehabilitation effect of cognitive and motor functions. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0049] Figure 1 2 is a schematic structural diagram of a cognitive impairment intervention system based on 3D printing technology according to an embodiment of the present invention;

[0050] Figure 2 2 is a schematic structural diagram of an MCU in a cognitive impairment intervention system based on 3D printing technology according to an embodiment of the present invention;

[0051] Figure 3 3D printing technology is a schematic diagram of the structure of a cloud server in a cognitive impairment intervention system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0052] To further illustrate each embodiment, the present invention provides drawings, which are part of the disclosure of the present invention. They are mainly used to illustrate the embodiments and can be used in conjunction with the relevant descriptions in the specification to explain the operating principles of the embodiments. By referring to these contents, ordinary technicians in this field should be able to understand other possible implementation methods and advantages of the present invention. The components in the figures are not drawn to scale, and similar component symbols are generally used to represent similar components.

[0053] According to an embodiment of the present invention, a cognitive impairment intervention system based on 3D printing technology is provided.

[0054] Embodiment 1;

[0055] like Figure 1-3 As shown, the cognitive impairment intervention system based on 3D printing technology according to an embodiment of the present invention includes a skull scanning device, a 3D printing device, a host computer, an MCU and a cloud server;

[0056] The skull scanning device is used to scan the patient's skull. By utilizing the principles of magnetic field and radio frequency electromagnetic waves, it performs a non-invasive, painless, and radiation-free scan of the human head to obtain high-definition imaging data.

[0057] The acquired high-definition image data is sent to the 3D printing device, which accurately determines three cognitive targets involving executive function, semantics, and episodic memory, and identifies the primary motor brain area where physical function declines;

[0058] The host computer includes a power module, a communication module and a control module;

[0059] The MCU includes circuit control, driver, magnetic coil, current limiting protection, capacitor and temperature sensor;

[0060] The cloud server includes decision-making algorithms, user logs, and interaction controls;

[0061] The cloud server sends personalized iTBS to the motor brain area and the corresponding cognitive cluster brain area based on the subtype of cognitive decline, achieving rehabilitation of cognitive and physical functions.

[0062] Embodiment 2;

[0063] like Figure 1-3 As shown, the decision algorithm, the user log and the interactive control are interconnected, the circuit control is respectively connected to the host computer, the current limiting protection, the capacitor, the temperature sensor and the decision algorithm, the other end of the circuit control is connected to the driver, the other end of the driver is connected to the magnetic coil and the capacitor, and the other end of the magnetic coil is connected to the temperature sensor and the interactive control.

[0064] Embodiment 3;

[0065] like Figure 1-3 As shown, the MCU also includes a data acquisition module, a data analysis module, a data generation module, a data storage module and a data comparison module; the cloud server is unidirectionally connected to the data acquisition module; the data acquisition module is unidirectionally connected to the data analysis module; the data analysis module is unidirectionally connected to the data generation module; the data generation module is unidirectionally connected to the data storage module; the data storage module is unidirectionally connected to the data comparison module, and the cloud server also includes a signal transceiver module, a scene construction module, a task design module, a task guidance module, an environment interaction module and a cognitive assessment module; the signal transceiver module is bidirectionally connected to the WiFi module; the communication module and the signal transceiver module transmit signals through the WiFi antenna; the signal transceiver module is bidirectionally connected to the scene construction module; the scene construction module is bidirectionally connected to the task design module; the task design module is bidirectionally connected to the task guidance module; the task guidance module is bidirectionally connected to the environment interaction module; the environment interaction module is unidirectionally connected to the cognitive assessment module.

[0066] The host computer also includes: constructing a basic data-score value mapping table, including score values ​​corresponding to different numerical intervals of age, height, weight, sleep data, personal chronic disease history and family chronic disease history, quantizing the basic data into component values ​​according to the basic data-score value mapping table, and then normalizing all score values ​​to the range of [0, 1] and splicing them to obtain a basic data feature vector.

[0067] Embodiment 4:

[0068] like Figure 1-3 As shown, the skull scanning device also includes: combining 3D scanning technology with a 10-20 channel electroencephalogram (EEG) cap, and applying EEG navigation technology to accurately locate the patient's cerebral cortex area;

[0069] The patient wears a multi-channel EEG cap, and a high-precision digital model of the anatomical structure of the head is obtained through 3D scanning;

[0070] Using the sensor positions in the EEG cap, precise registration is performed using 3D scan data to match EEG channels to the individual’s anatomy.

[0071] The navigation system uses the 10-20 international standard electrode system (10-20 system) to locate specific brain regions. The following areas are of particular interest:

[0072] Right primary motor cortex (Primary Motor Cortex, Right Hemisphere)

[0073] Located in the precentral gyrus of the brain, it is responsible for motor control, especially movement on the opposite side of the body.

[0074] Left ventrolateral prefrontal cortex (Left Hemisphere)

[0075] Located in the prefrontal region of the brain, it is involved in cognitive functions such as decision-making, planning, problem-solving, and inhibitory control.

[0076] Anterior Cingulate Cortex, Left Hemisphere

[0077] Located in the cingulate gyrus region of the brain, it is involved in emotion regulation, pain processing, as well as attention and decision-making.

[0078] Left dorsolateral frontal cortex (Dorsolateral Prefrontal Cortex, Left Hemisphere)

[0079] Located in the dorsolateral part of the frontal lobe, it is associated with working memory, executive function, goal-directed behavior, and emotion regulation.

[0080] Preferably, the decision algorithm further includes: a cloud-based decision mechanism for remotely controlling transcranial magnetic stimulation (TMS) that automatically develops a personalized stimulation plan for each patient by integrating patient data, treatment needs, and real-time feedback; the process of this mechanism is as follows:

[0081] Data collection and upload: The patient's EEG data, clinical information, and treatment progress are uploaded to the cloud through the device. 3D scanning data is combined with EEG data to provide further accurate brain region positioning information;

[0082] Cloud-based decision-making model: The cloud-based system uses real-time analysis of patients' EEG data, individual differences, and established treatment plans. Based on machine learning or rule-based algorithms, it automatically optimizes treatment parameters such as stimulation frequency, number of bursts, and stimulation intensity. This system combines clinical guidelines with personalized data to tailor treatment strategies to individual patient needs.

[0083] Stimulation frequency and number of trains setting:

[0084] Stimulation Frequency: Based on the patient's pathological condition and treatment progress, the cloud system dynamically adjusts the stimulation frequency to regulate the excitability of the cerebral cortex;

[0085] Train Number: The cloud system flexibly sets the number of times each stimulation training is performed (train number) based on treatment goals and patient feedback to ensure the continuity and effectiveness of the stimulation effect;

[0086] Treatment feedback mechanism: Patient treatment progress and feedback (e.g., efficacy evaluation, symptom improvement, etc.) are monitored in real time via a cloud-based platform. The cloud-based decision-making system adjusts treatment plans based on this feedback, including fine-tuning stimulation parameters or changing treatment plans.

[0087] Remote Monitoring and Adjustment: During treatment, doctors can remotely monitor the patient's condition through the cloud platform and further adjust stimulation parameters based on real-time data to maximize treatment effectiveness. This mechanism allows for remote control of the TMS device, precisely adjusting stimulation intensity, frequency, and number of bursts, enabling personalized and precise treatment.

[0088] This cloud-based decision-making mechanism can not only ensure the treatment effect of patients, but also improve the personalization and flexibility of treatment, reduce human intervention, and improve the scientificity and efficiency of treatment.

[0089] In summary, with the help of the above technical solution of the present invention, three cognitive targets involving executive function, semantic memory and episodic memory are accurately determined through skull scanning combined with 3D printing; the primary motor brain area with decreased physical function is determined. The control system accurately determines the three cognitive targets involving executive function, semantic memory and episodic memory based on the subtype of cognitive decline, and locates the primary motor brain area related to decreased physical function. On this basis, the control system combines the subtype of cognitive decline of the patient and makes personalized decisions through the cloud-based intelligent decision-making mechanism. In the three stimulation modes of iTBS (intermittent theta rhythm stimulation), cTBS (continuous theta rhythm stimulation) and rTMS (repetitive transcranial magnetic stimulation), the adaptability and effectiveness of the activation and inhibition effects on different brain areas are dynamically evaluated.

[0090] By analyzing the patient's brain functional maps, disease progression, and neuroplasticity parameters, the system intelligently matches the most appropriate stimulation mode. For example, iTBS may be used to boost cognitive excitation in cognitive brain areas, cTBS to suppress overactive areas, and rTMS to enhance rhythmic motor control. Ultimately, this system achieves simultaneous rehabilitation of cognitive and physical functions, enhancing the individualization and dynamic adjustment capabilities of treatment.

[0091] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention shall be included in the scope of protection of the present invention. The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention shall be included in the scope of protection of the present invention.

Claims

1. A cognitive impairment intervention system based on 3D printing technology, characterized in that: Including skull scanning equipment, 3D printing equipment, host computer, MCU and cloud server; The skull scanning device is used to scan the patient's skull. By utilizing the principles of magnetic field and radio frequency electromagnetic waves, it performs a non-invasive, painless, and radiation-free scan of the human head to obtain high-definition imaging data. The acquired high-definition image data is sent to the 3D printing device, which accurately determines three cognitive targets involving executive function, semantics, and episodic memory, and identifies the primary motor brain area where physical function declines; The host computer includes a power module, a communication module and a control module; The MCU includes circuit control, driver, magnetic coil, current limiting protection, capacitor and temperature sensor; The cloud server includes decision-making algorithms, user logs, and interaction controls; The cloud server sends personalized iTBS to the motor brain area and the corresponding cognitive cluster brain area based on the subtype of cognitive decline, thereby achieving rehabilitation of cognitive and physical functions.

2. The cognitive impairment intervention system based on 3D printing technology according to claim 1, characterized in that: The decision algorithm, the user log and the interaction control are interconnected.

3. The cognitive impairment intervention system based on 3D printing technology according to claim 2, characterized in that: The circuit control is respectively connected to the host computer, the current limiting protection, the capacitor, the temperature sensor and the decision algorithm.

4. The cognitive impairment intervention system based on 3D printing technology according to claim 3, characterized in that: The other end of the circuit control is connected to the driver, and the other end of the driver is connected to the magnetic coil and the capacitor.

5. The cognitive impairment intervention system based on 3D printing technology according to claim 4, characterized in that: The other end of the magnetic coil is connected to the temperature sensor and the interactive control.

6. The cognitive impairment intervention system based on 3D printing technology according to claim 1, characterized in that: The MCU also includes a data acquisition module, a data analysis module, a data generation module, a data storage module and a data comparison module; the cloud server is unidirectionally connected to the data acquisition module; the data acquisition module is unidirectionally connected to the data analysis module; the data analysis module is unidirectionally connected to the data generation module; the data generation module is unidirectionally connected to the data storage module; and the data storage module is unidirectionally connected to the data comparison module.

7. The cognitive impairment intervention system based on 3D printing technology according to claim 1, characterized in that: The cloud server also includes a signal transceiver module, a scene construction module, a task design module, a task guidance module, an environment interaction module and a cognitive assessment module; the signal transceiver module is bidirectionally connected to the WiFi module; the communication module and the signal transceiver module transmit signals through the WiFi antenna; the signal transceiver module is bidirectionally connected to the scene construction module; the scene construction module is bidirectionally connected to the task design module; the task design module is bidirectionally connected to the task guidance module; and the task guidance module is bidirectionally connected to the environment interaction module.

8. The cognitive impairment intervention system based on 3D printing technology according to claim 1, characterized in that: The host computer also includes: constructing a basic data-score value mapping table, including score values ​​corresponding to different numerical intervals of age, height, weight, sleep data, personal chronic disease history and family chronic disease history, quantizing the basic data into component values ​​based on the basic data and score value mapping table, and then normalizing all score values ​​to the range of [0, 1] and splicing them to obtain a basic data feature vector.

9. The cognitive impairment intervention system based on 3D printing technology according to claim 1, characterized in that: The skull scanning device also includes: combining 3D scanning technology with a 10-20 channel electroencephalogram (EEG) cap to accurately locate the patient's cerebral cortex area using EEG navigation technology; The patient wears a multi-channel EEG cap, and a high-precision digital model of the anatomical structure of the head is obtained through 3D scanning; Using the sensor positions in the EEG cap, precise registration is performed using 3D scan data to match EEG channels to the individual’s anatomy. The navigation system uses the 10-20 international standard electrode system (10-20 system) to locate specific brain regions, with a particular focus on the following areas: the right primary motor cortex, located in the precentral gyrus of the brain, which is responsible for movement control, particularly of the contralateral side of the body; The left ventrolateral prefrontal cortex is located in the frontal region of the brain and is involved in cognitive functions such as decision-making, planning, problem solving, and inhibitory control; The left presynaptic lobe is located in the cingulate gyrus region of the brain and is involved in emotion regulation, pain processing, attention, and decision-making. The left dorsolateral frontal cortex is located in the dorsolateral part of the frontal lobe and is associated with working memory, executive function, goal-directed behavior, and emotion regulation.

10. The cognitive impairment intervention system based on 3D printing technology according to claim 1, characterized in that: The decision-making algorithm also includes a cloud-based decision-making mechanism for remotely controlling transcranial magnetic stimulation that automatically develops a personalized stimulation plan for each patient by integrating patient data, treatment needs, and real-time feedback. The process of this mechanism is as follows: Data collection and upload: The patient's EEG data, clinical information, and treatment progress are uploaded to the cloud through the device. 3D scanning data and EEG data are combined to provide further accurate brain region positioning information; Cloud-based decision-making model: The cloud-based system uses real-time analysis of patients' EEG data, individual differences, and established treatment plans. Based on machine learning or rule-based algorithms, it automatically optimizes treatment parameters such as stimulation frequency, number of bursts, and stimulation intensity. This system combines clinical guidelines with personalized data to tailor treatment strategies to individual patient needs. Stimulation frequency and number of trains setting: Stimulation frequency: Based on the patient's pathological condition and treatment progress, the cloud system dynamically adjusts the stimulation frequency to regulate the excitability of the cerebral cortex; Number of sessions: The cloud system flexibly sets the number of sessions for each stimulation session based on treatment goals and patient feedback to ensure the continuity and effectiveness of the stimulation effect. Treatment feedback mechanism: Patient treatment progress and feedback are monitored in real time through the cloud platform. The cloud-based decision-making system adjusts the treatment plan based on this feedback, including fine-tuning stimulation parameters or changing the treatment plan; Remote monitoring and adjustment: During the treatment process, doctors can remotely monitor the patient's condition through the cloud platform and further adjust the stimulation parameters based on real-time data to ensure maximum treatment effect. This mechanism can remotely control the TMS device.

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