Method and apparatus for providing treatment-related prediction information for alzheimer's disease

Abstract

The present disclosure provides a method for providing predictive information related to the treatment of Alzheimer's disease. The method for providing predictive information related to treatment includes the steps of receiving a brain scan image of a subject, determining the amount of tau protein accumulation in a first region of the subject's brain based on the scan image, and calculating a first score associated with the need for treatment of Alzheimer's disease of the subject based on the amount of tau protein accumulation in the first region of the subject's brain.

Description

Technology Field

[0001] The present disclosure relates to a method and apparatus for providing predictive information related to the treatment of Alzheimer's disease, and specifically, to a method and apparatus for performing a prediction related to the treatment of Alzheimer's disease based on the accumulation amount of tau protein in different regions of a subject's brain. Background Technology

[0002] Alzheimer's disease is a type of neurodegenerative disease and the most common cause of dementia in the elderly. The exact cause of Alzheimer's disease has not been clearly identified, and various hypotheses exist regarding its etiology.

[0003] Meanwhile, when deciding whether to treat or how to treat a specific disease, both the necessity of treatment for the individual patient and the benefits of the treatment method to be applied must be considered. However, there is a problem in that existing clinical trials or approvals by relevant agencies cannot accurately predict whether a specific drug is necessary or beneficial to a particular patient. For instance, even though a drug approved by the U.S. FDA has been concluded to provide public health benefits because its benefits outweigh the risks, it is currently very difficult to determine whether an individual patient is suitable for that drug based on specific criteria or prediction methods.

[0004] In particular, regarding the treatment of Alzheimer's disease, it is necessary to identify the "golden time" for treatment, as treatment methods and strategies vary depending on when treatment is required or effective for each individual patient. However, according to existing technology, it is very difficult to determine the golden time for treatment for each individual patient. The problem to be solved

[0005] The present disclosure provides a method for providing predictive information related to the treatment of Alzheimer's disease to solve the above-mentioned problems, a computer program stored on a recording medium, and a device (system). means of solving the problem

[0006] The present disclosure may be implemented in various ways, including a method, a system (device), or a computer program stored on a readable storage medium.

[0007] A method for providing predictive information related to the treatment of Alzheimer's disease, executed by at least one processor according to one embodiment of the present disclosure, comprises the steps of receiving a brain scan image of a subject, determining the amount of tau protein accumulation in a first region of the subject's brain based on the scan image, and calculating a first score associated with the need for treatment of Alzheimer's disease of the subject based on the amount of tau protein accumulation in the first region of the subject's brain.

[0008] In one embodiment of the present disclosure, the method further comprises the steps of determining the amount of tau protein accumulation in a second region of a subject's brain based on a scan image, and calculating a second score associated with the efficacy of the treatment of Alzheimer's disease of the subject based on the amount of tau protein accumulation in the second region, wherein the first region and the second region are different regions from each other.

[0009] In one embodiment of the present disclosure, the first score is maintained or increases as the amount of tau protein accumulation in the first region of the subject's brain increases, and the second score is maintained or decreases as the amount of tau protein accumulation in the second region of the subject's brain increases.

[0010] In one embodiment of the present disclosure, the method further includes the step of calculating a composite score associated with the treatment of Alzheimer's disease of a subject based on a first score and a second score.

[0011] In one embodiment of the present disclosure, the method further includes the step of determining whether to provide treatment or at least one of the type of treatment associated with the subject's Alzheimer's disease based on at least one of a first score, a second score, or a composite score.

[0012] In one embodiment of the present disclosure, the step of determining the start time of the golden time for treating the subject's Alzheimer's disease based on a first score is further included.

[0013] In one embodiment of the present disclosure, the method further includes the step of predicting the time remaining until the start of the treatment golden time for the subject's Alzheimer's disease, based on the start time of the determined treatment golden time.

[0014] In one embodiment of the present disclosure, the step of calculating a first score includes the step of calculating a first time interval between a first time corresponding to the amount of tau protein accumulation in a first region of the subject's brain and a second time in which the amount of tau protein accumulation is a predetermined first threshold value on a first path representing a predicted amount of tau protein accumulation in a first region according to a change in time, and the step of calculating a first score based on the first time interval.

[0015] In one embodiment of the present disclosure, the first pathway is determined based on a data set associated with the amount of tau protein accumulation in a first region of the brain over time for each of the subject and a plurality of other subjects.

[0016] In one embodiment of the present disclosure, the step of determining the end point of the golden time for treating Alzheimer's disease based on a second score is further included.

[0017] In one embodiment of the present disclosure, the method further includes the step of predicting the time remaining until the end of the treatment golden time for the subject's Alzheimer's disease, based on the end point of the determined treatment golden time.

[0018] In one embodiment of the present disclosure, the step of calculating a second score includes the step of calculating a second time interval between a third time corresponding to the amount of tau protein accumulation in a second region of the subject's brain and a fourth time in which the amount of tau protein accumulation is a predetermined second threshold value on a second path representing a predicted amount of tau protein accumulation in a second region according to a change in time, and the step of calculating a second score based on the second time interval.

[0019] In one embodiment of the present disclosure, the second pathway is determined based on a data set associated with the amount of tau protein accumulation in a second region of the brain over time for each of the subject and a plurality of other subjects.

[0020] In one embodiment of the present disclosure, the subject is a patient determined to have accumulated beta-amyloid protein (Aβ) above a predetermined threshold in the subject's brain.

[0021] In one embodiment of the present disclosure, the first region is the Entorhinal Cortex (EC) region.

[0022] In one embodiment of the present disclosure, the second region is the inferior temporal gyrus (ITG) region.

[0023] In one embodiment of the present disclosure, the method further includes the step of identifying that the subject is within a golden time in which there is a possibility of responding to amyloid modulation therapy as an Alzheimer's treatment, in response to a determination that the comprehensive score is greater than a predetermined third threshold.

[0024] In one embodiment of the present disclosure, the method further includes the step of identifying that a subject is likely to respond to an Alzheimer's prevention therapy in response to a determination that a first score is smaller than a predetermined first threshold.

[0025] In one embodiment of the present disclosure, the method further includes the step of identifying that a subject is likely to respond to a combination therapy including amyloid-modifying therapy and tau-modifying therapy as an Alzheimer's treatment in response to a determination that a second score is smaller than a predetermined second threshold.

[0026] A computer program stored on a computer-readable recording medium is provided for executing a method for providing predictive information related to the treatment of Alzheimer's disease according to one embodiment of the present disclosure on a computer.

[0027] An apparatus according to one embodiment of the present disclosure comprises a communication module, a memory, and at least one processor connected to the memory and configured to execute at least one computer-readable program included in the memory, wherein the at least one program includes instructions for receiving a brain scan image of a subject, determining the amount of tau protein accumulation in a first region of the subject's brain based on the scan image, and calculating a first score associated with the need for treatment of Alzheimer's disease of the subject based on the amount of tau protein accumulation in the first region of the subject's brain. Effects of the invention

[0028] According to various embodiments of the present disclosure, the suitability of treatment for Alzheimer's disease at the present time can be determined by providing individual patients with predictive information regarding the necessity or efficacy of treatment for Alzheimer's disease at the present time.

[0029] According to various embodiments of the present disclosure, by predicting the degree of disease progression and efficacy of individual patients over time based on the general progression path of Alzheimer's disease, it is possible to determine when or from when treatment for Alzheimer's disease is appropriate and help patients and doctors establish treatment strategies.

[0030] The effects of the present disclosure are not limited to those mentioned above, and other unmentioned effects will be clearly understood by a person skilled in the art to which the present disclosure pertains (referred to as "person skilled in the art") from the description in the claims. Brief explanation of the drawing

[0031] Embodiments of the present disclosure will be described with reference to the accompanying drawings described below, wherein similar reference numerals indicate similar elements, but are not limited thereto. FIG. 1 is a diagram illustrating an example in which a score associated with the treatment of Alzheimer's disease is calculated based on a brain scan image of a subject according to one embodiment of the present disclosure. FIG. 2 is a schematic diagram showing a configuration in which an information processing system is connected to communicate with a plurality of user terminals to provide a prediction service related to the treatment of Alzheimer's disease according to one embodiment of the present disclosure. FIG. 3 is a block diagram showing the internal configuration of a user terminal and an information processing system according to one embodiment of the present disclosure. FIG. 4 is a block diagram showing the internal configuration of a processor according to one embodiment of the present disclosure. FIG. 5 is a diagram showing an example of a graph of predicted values ​​of tau protein accumulation amounts in a first region and a second region according to a change over time according to one embodiment of the present disclosure. FIG. 6 is a drawing showing an example of a graph displaying necessity scores and efficacy scores over time according to one embodiment of the present disclosure. FIG. 7 is a drawing showing an example of a graph of predicted total scores over time according to one embodiment of the present disclosure. FIG. 8 is a flowchart illustrating a method for providing predictive information related to the treatment of Alzheimer's disease according to one embodiment of the present disclosure. Specific details for implementing the invention

[0032] Hereinafter, specific details for implementing the present disclosure will be described in detail with reference to the attached drawings. However, in the following description, specific descriptions regarding well-known functions or configurations will be omitted if there is a risk that the gist of the present disclosure may be unnecessarily obscured.

[0033] In the attached drawings, identical or corresponding components are assigned the same reference numerals. Additionally, in the description of the following embodiments, the description of identical or corresponding components may be omitted. However, even if a description of a component is omitted, it is not intended that such component is not included in any embodiment.

[0034] The advantages and features of the disclosed embodiments and the methods for achieving them will become clear by referring to the embodiments described below in conjunction with the accompanying drawings. However, the present disclosure is not limited to the embodiments disclosed below but may be implemented in various different forms, and the embodiments provided are merely to make the present disclosure complete and to fully inform those skilled in the art of the scope of the invention.

[0035] The terms used in this specification will be briefly explained, and the disclosed embodiments will be described in detail. The terms used in this specification have been selected to be as generally used as possible, taking into account their functions in this disclosure; however, these terms may vary depending on the intent of those skilled in the art, case law, the emergence of new technologies, etc. Additionally, in specific cases, terms may be arbitrarily selected by the applicant, and in such cases, their meanings will be described in detail in the relevant description of the invention. Therefore, the terms used in this disclosure should be defined not merely by their names, but based on their meanings and the content throughout this disclosure.

[0036] In this specification, singular expressions include plural expressions unless the context clearly specifies them as singular. Additionally, plural expressions include singular expressions unless the context clearly specifies them as plural. Throughout the specification, when a part is described as including a certain component, this means that, unless specifically stated otherwise, it does not exclude other components but may include additional components.

[0037] Additionally, the terms 'module' or 'part' as used in the specification refer to software or hardware components, and the 'module' or 'part' performs certain roles. However, the meaning of 'module' or 'part' is not limited to software or hardware. The 'module' or 'part' may be configured to reside in an addressable storage medium or configured to run on one or more processors. Thus, as an example, the 'module' or 'part' may include components such as software components, object-oriented software components, class components, and task components, and at least one of processes, functions, attributes, procedures, subroutines, segments of program code, drivers, firmware, microcode, circuits, data, databases, data structures, tables, arrays, or variables. The components and the functions provided within the 'module' or 'part' may be combined into a smaller number of components and 'modules' or 'parts', or further separated into additional components and 'modules' or 'parts'.

[0038] According to one embodiment of the present disclosure, a ‘module’ or ‘part’ may be implemented as a processor and memory. The term ‘processor’ should be broadly interpreted to include a general-purpose processor, a central processing unit (CPU), a microprocessor, a digital signal processor (DSP), a controller, a microcontroller, a state machine, etc. In some environments, the term ‘processor’ may refer to an application-specific integrated circuit (ASIC), a programmable logic device (PLD), a field programmable gate array (FPGA), etc. The term ‘processor’ may also refer to a combination of processing devices, such as, for example, a combination of a DSP and a microprocessor, a combination of multiple microprocessors, a combination of one or more microprocessors combined with a DSP core, or any other combination of such configurations. Additionally, the term ‘memory’ should be broadly interpreted to include any electronic component capable of storing electronic information. 'Memory' may refer to various types of processor-readable media, such as Random Access Memory (RAM), Read-Only Memory (ROM), Non-Volatile Random Access Memory (NVRAM), Programmable Read-Only Memory (PROM), Erasable-Programmable Read-Only Memory (EPROM), Electrically Erasable PROM (EEPROM), Flash Memory, Magnetic or Optical Data Storage Devices, Registers, etc. If a processor can read information from memory and / or write information to memory, the memory is said to be in an electronic communication state with the processor. Memory integrated into a processor is in an electronic communication state with the processor.

[0039] In the present disclosure, "subject" may refer to a patient or subject, etc., to whom a prediction regarding the treatment of Alzheimer's disease according to the present disclosure is to be performed. For example, "subject" may be a patient determined to have accumulated beta-amyloid protein (Aβ) in the subject's brain above a predetermined threshold.

[0040] In the present disclosure, 'efficacy' may refer to the therapeutic or involvement ability of a specific drug, therapeutic regimen, etc., to produce a targeted beneficial effect or result, such as the cure of a specific disease, or the reduction or elimination of symptoms of a specific disease.

[0041] In the present disclosure, the "golden time for treatment" may refer to the optimal time for treating a disease. For example, the "golden time for treatment" may refer to a stage or period during the course of the disease that is likely to show the highest treatment success rate.

[0042] FIG. 1 is a diagram illustrating an example in which scores (132, 142, 152) associated with the treatment of Alzheimer's disease are calculated based on a brain scan image (110) of a subject according to one embodiment of the present disclosure. The brain scan image (110) of a subject may be taken in various ways, such as MRI (Magnetic Resonance Imaging), PET scan (Positron Emission Tomography Scans), fMRI (Functional Magnetic Resonance Imaging), CT scan (Computed Tomography Scans), SPECT (Single Photon Emission Computed Tomography), Amyloid PET scan, Tau PET scan, etc., and is not limited to the listed methods. The brain scan image (110) is illustrated as a single image but is not limited thereto and may include a plurality of images and / or images, etc.

[0043] The amount of tau protein accumulation (120) in the subject's brain can be determined / predicted based on the brain scan image (110). In one embodiment, based on the brain scan image (110), the amount of tau protein accumulation in a first region of the subject's brain and the amount of tau protein accumulation in a second region different from the first region of the subject's brain can be determined / predicted.

[0044] In one embodiment, the first region where the amount of tau protein accumulation is determined may be the Entorhinal Cortex (EC) region, and the second region may be the Inferior Temporal Gyrus (ITG) region. The amount of tau protein accumulation in the Entorhinal Cortex region may be determined / predicted by quantifying the relative amount of tau protein accumulation within one or more Entorhinal Cortex regions.

[0045] In contrast, the specific location of the first region and / or the second region can be determined / predicted by calculating the goodness of fit (GOF) between a region map showing the degree of accumulation of tau protein in the brains of multiple subjects and a connectivity map between one or more regions in the brains of multiple subjects (e.g., a structural connectivity map and / or a functional connectivity map). In one embodiment, the goodness of fit between the region map and the connectivity map can be calculated by an artificial intelligence model.

[0046] In one embodiment, based on the determined amount of tau protein accumulation (120), a first score (132) associated with the subject's need for treatment of Alzheimer's disease and / or a second score (142) associated with the efficacy of the treatment may be calculated. For example, the first score (132) may be calculated based on the amount of tau protein accumulation in the first region (112) and the first graph (130), and the second score (142) may be calculated based on the amount of tau protein accumulation in the second region (114) and the second graph (140). The specific process for calculating the first score (132) and the second score (142) will be described in detail later using FIGS. 5 and 6.

[0047] Subsequently, based on the calculated first score (132) and second score (142), a comprehensive score (152) on a third graph (150) related to the treatment of the subject's Alzheimer's disease can be calculated. The specific process for calculating the comprehensive score (152) will be described in detail later using FIG. 7.

[0048] FIG. 2 is a schematic diagram showing a configuration in which an information processing system (230) is connected to communicate with a plurality of user terminals (210_1, 210_2, 210_3) to provide a prediction service related to the treatment of Alzheimer's disease according to one embodiment of the present disclosure. The information processing system (230) may include system(s) capable of providing a prediction service related to the treatment of Alzheimer's disease. In one embodiment, the information processing system (230) may include one or more server devices and / or databases capable of storing, providing, and executing computer-executable programs (e.g., downloadable applications) and data related to a prediction service related to the treatment of Alzheimer's disease, or one or more distributed computing devices and / or distributed databases based on cloud computing services.

[0049] Predictive services related to the treatment of Alzheimer's disease provided by the information processing system (230) can be provided to the user through applications installed on each of the multiple user terminals (210_1, 210_2, 210_3).

[0050] Multiple user terminals (210_1, 210_2, 210_3) can communicate with an information processing system (230) through a network (220). The network (220) can be configured to enable communication between the multiple user terminals (210_1, 210_2, 210_3) and the information processing system (230). Depending on the installation environment, the network (220) may be configured as a wired network such as Ethernet, Power Line Communication, telephone line communication device and RS-serial communication, a mobile communication network, a Wireless LAN (WLAN), Wi-Fi, Bluetooth and ZigBee, or a combination thereof. The communication method is not limited and may include not only communication methods utilizing communication networks that the network (220) may include (e.g., mobile communication network, wired internet, wireless internet, broadcasting network, satellite network, etc.) but also short-range wireless communication between user terminals (210_1, 210_2, 210_3).

[0051] For example, multiple user terminals (210_1, 210_2, 210_3) can transmit a request to an information processing system (230) via a network (220), and the information processing system (230) can receive the request and then transmit a response corresponding to the request to the multiple user terminals (210_1, 210_2, 210_3). For instance, if a user terminal (210_1) transmits a request to the information processing system (230) regarding a subject's brain scan image and a prediction related to the treatment of Alzheimer's disease (request), the information processing system (230) can transmit to the user terminal (210_1) a score related to the necessity or efficacy of treatment for Alzheimer's disease, information related to the golden time for treatment, etc., based on the brain scan image (response).

[0052] In FIG. 2, a mobile phone terminal (210_1), a tablet terminal (210_2), and a PC terminal (210_3) are illustrated as examples of user terminals, but are not limited thereto. The user terminals (210_1, 210_2, 210_3) may be any computing device capable of wired and / or wireless communication and capable of installing and running a prediction application related to the treatment of Alzheimer's disease. For example, user terminals may include medical devices, smartphones, mobile phones, navigation systems, computers, laptops, digital broadcasting terminals, PDAs (Personal Digital Assistants), PMPs (Portable Multimedia Players), tablet PCs, game consoles, wearable devices, IoT (Internet of Things) devices, VR (Virtual Reality) devices, AR (Augmented Reality) devices, etc. Additionally, FIG. 2 illustrates three user terminals (210_1, 210_2, 210_3) communicating with an information processing system (230) through a network (220), but is not limited thereto, and may be configured so that a different number of user terminals communicate with an information processing system (230) through a network (220).

[0053] FIG. 3 is a block diagram showing the internal configuration of a user terminal (210) and an information processing system (230) according to one embodiment of the present disclosure. The user terminal (210) may refer to any computing device capable of executing a prediction application related to the treatment of Alzheimer's disease, etc., and capable of wired / wireless communication, and may include, for example, the mobile phone terminal (210_1), tablet terminal (210_2), PC terminal (210_3) of FIG. 2. As illustrated, the user terminal (210) may include a memory (312), a processor (314), a communication module (316), and an input / output interface (318). Similarly, the information processing system (230) may include a memory (332), a processor (334), a communication module (336), and an input / output interface (338). As illustrated in FIG. 3, the user terminal (210) and the information processing system (230) may be configured to communicate information and / or data through the network (220) using their respective communication modules (316, 336). Additionally, the input / output device (320) may be configured to input information and / or data to the user terminal (210) or output information and / or data generated from the user terminal (210) through the input / output interface (318).

[0054] The memory (312, 332) may include any non-transient computer-readable recording medium. According to one embodiment, the memory (312, 332) may include a permanent mass storage device such as ROM (read-only memory), a disk drive, an SSD (solid-state drive), or a flash memory. As another example, a permanent mass storage device such as ROM, an SSD, a flash memory, or a disk drive may be included in the user terminal (210) or the information processing system (230) as a separate permanent storage device distinct from the memory. Additionally, the memory (312, 332) may store an operating system and at least one program code (e.g., code for a prediction application related to the treatment of Alzheimer's disease).

[0055] These software components may be loaded from a computer-readable recording medium separate from memory (312, 332). This separate computer-readable recording medium may include a recording medium that can be directly connected to the user terminal (210) and the information processing system (230), for example, a computer-readable recording medium such as a floppy drive, disk, tape, DVD / CD-ROM drive, or memory card. As another example, the software components may be loaded into memory (312, 332) via a communication module (316, 336) rather than a computer-readable recording medium. For example, at least one program may be loaded into memory (312, 332) based on a computer program (e.g., a predictive application related to the treatment of Alzheimer's disease) that is installed by files provided through a network (220) by developers or a file distribution system that distributes installation files of the application.

[0056] The processor (314, 334) may be configured to process instructions of a computer program by performing basic arithmetic, logic, and input / output operations. Instructions may be provided to the processor (314, 334) by memory (312, 332) or a communication module (316, 336). For example, the processor (314, 334) may be configured to execute instructions received according to program code stored in a recording device such as memory (312, 332).

[0057] The communication module (316, 336) may provide a configuration or function for the user terminal (210) and the information processing system (230) to communicate with each other via the network (220), and may provide a configuration or function for the user terminal (210) and / or the information processing system (230) to communicate with another user terminal or another system (e.g., a separate cloud system). For example, a request or data generated by the processor (314) of the user terminal (210) according to program code stored in a recording device such as memory (312) may be transmitted to the information processing system (230) via the network (220) under the control of the communication module (316). Conversely, a control signal or command provided under the control of the processor (334) of the information processing system (230) may be received by the user terminal (210) via the communication module (316) of the user terminal (210) through the communication module (336) and the network (220).

[0058] The input / output interface (318) may be a means for interfacing with an input / output device (320). As an example, the input device may include a device such as a camera including an audio sensor and / or an image sensor, a keyboard, a microphone, or a mouse, and the output device may include a device such as a display, a speaker, or a haptic feedback device. As another example, the input / output interface (318) may be a means for interfacing with a device in which the configuration or function for performing input and output is integrated into one, such as a touchscreen. In FIG. 3, the input / output device (320) is not shown as being included in the user terminal (210), but is not limited thereto and may be configured as a single device with the user terminal (210). Additionally, the input / output interface (338) of the information processing system (230) may be a means for interfacing with a device (not shown) for input or output that is connected to the information processing system (230) or that the information processing system (230) may include. In FIG. 3, the input / output interface (318, 338) is shown as an element configured separately from the processor (314, 334), but is not limited thereto, and the input / output interface (318, 338) may be configured to be included in the processor (314, 334).

[0059] The user terminal (210) and the information processing system (230) may include more components than those of FIG. 3. However, it is not necessary to clearly illustrate most of the conventional technical components. In one embodiment, the user terminal (210) may be implemented to include at least some of the input / output devices (320) described above. Additionally, the user terminal (210) may further include other components such as a transceiver, a GPS (Global Positioning System) module, a camera, various sensors, a database, etc. For example, if the user terminal (210) is a smartphone, it may include components that are generally included in a smartphone, and may be implemented to include various components such as an accelerometer, a gyroscope, a microphone module, a camera module, various physical buttons, buttons using a touch panel, input / output ports, and a vibrator for vibration.

[0060] According to one embodiment, the processor (314) of the user terminal (210) may be configured to operate an application or web browser application that provides a prediction service related to the treatment of Alzheimer's disease. At this time, program code associated with the application may be loaded into the memory (312) of the user terminal (210). While the application is running, the processor (314) of the user terminal (210) may receive information and / or data provided from an input / output device (320) through an input / output interface (318) or receive information and / or data from an information processing system (230) through a communication module (316), and may process the received information and / or data and store it in the memory (312). Additionally, such information and / or data may be provided to the information processing system (230) through the communication module (316).

[0061] While the application is running, the processor (314) can receive voice data, text, images, videos, etc. that are input or selected through an input device such as a touch screen, keyboard, audio sensor and / or image sensor, camera, microphone, etc. connected to the input / output interface (318), and can store the received voice data, text, images and / or videos, etc. in memory (312) or provide them to an information processing system (230) through a communication module (316) and a network (220).

[0062] The processor (314) of the user terminal (210) can transmit information and / or data to an input / output device (320) through an input / output interface (318) and output it. For example, the processor (314) of the user terminal (210) can output the processed information and / or data through an output device (320), such as a display output device (e.g., touch screen, display, etc.) or a voice output device (e.g., speaker).

[0063] The processor (334) of the information processing system (230) may be configured to manage, process, and / or store information and / or data received from a plurality of user terminals (210) and / or a plurality of external systems. The information and / or data processed by the processor (334) may be provided to the user terminals (210) through a communication module (336) and a network (220).

[0064] FIG. 4 is a block diagram showing the internal configuration of a processor (400) according to one embodiment of the present disclosure. As illustrated, the processor (400) (e.g., the processor (314) of the user terminal of FIG. 3 or the processor (334) of the information processing system) may include a tau protein accumulation amount determining unit (410), a score calculation unit (420), a treatment determination unit (430), a golden time determining unit (440), etc.

[0065] The tau protein accumulation amount determining unit (410) can determine / predict the amount of tau protein accumulation in a first region of the subject's brain based on the subject's brain scan image. Additionally, the tau protein accumulation amount determining unit (410) can determine / predict the amount of tau protein accumulation in a second region of the subject's brain based on the scan image. At this time, the first region and the second region may be different regions. For example, the first region may be the Entorhinal Cortex (EC) region, and the second region may be the Inferior Temporal Gyrus (ITG) region.

[0066] The score calculation unit (420) can calculate a first score associated with the need for treatment of Alzheimer's disease of the subject based on the amount of tau protein accumulated in the first region of the subject's brain determined by the amount of tau protein accumulated in

[0067] The score calculation unit (420) can calculate a second score associated with the efficacy of the subject's Alzheimer's disease treatment based on the amount of tau protein accumulation in the second region determined by the tau protein accumulation amount determination unit (410). In one embodiment, the score calculation unit (420) can calculate a second score based on a second time interval between a third time corresponding to the amount of tau protein accumulation in the second region of the subject's brain and a fourth time where the amount of tau protein accumulation is a predetermined second threshold, on a second path representing a predicted value of the amount of tau protein accumulation in the second region according to time change. At this time, the second path may be determined / estimated / generated based on a data set associated with the amount of tau protein accumulation in the second region of the brain according to time change for each of the subject and / or other multiple subjects.

[0068] In one embodiment, the first score calculated by the score calculation unit (420) may be maintained or increased as the amount of tau protein accumulation in the first region of the subject's brain increases, and the second score may be maintained or decreased as the amount of tau protein accumulation in the second region of the subject's brain increases. Based on the first score and the second score, the score calculation unit (420) may calculate a comprehensive score associated with the treatment of the subject's Alzheimer's disease.

[0069] The treatment decision unit (430) can determine / predict whether to provide treatment or at least one type of treatment associated with the subject's Alzheimer's disease based on at least one of the first score, second score, or comprehensive score calculated by the score calculation unit (420).

[0070] In one embodiment, the treatment decision unit (430) may identify that the subject is within the golden time for responding to amyloid-modifying therapy as an Alzheimer's treatment in response to a determination that the comprehensive score is greater than a predetermined third threshold (i.e., a period of maintaining that state including high need and high efficacy). In another embodiment, the treatment decision unit (430) may identify that the subject is likely to respond to Alzheimer's preventive therapy in response to a determination that the first score is less than a predetermined first threshold (i.e., including low need and high efficacy). Here, Alzheimer's preventive therapy may include the administration of an amyloid (Aβ) vaccine and / or a tau vaccine. In yet another embodiment, the treatment decision unit (430) may identify that the subject is likely to respond to a combination therapy including amyloid-modifying therapy and tau-modifying therapy as an Alzheimer's treatment in response to a determination that the second score is less than a predetermined second threshold (including high need and low efficacy).

[0071] In one embodiment, if the first score is smaller than a predetermined first threshold, the golden time determination unit (440) can determine / predict the start time of the golden time for treating the subject's Alzheimer's disease based on the first score calculated by the score calculation unit (420). Subsequently, based on the determined start time of the golden time for treatment, the remaining time until the start of the golden time for treating the subject's Alzheimer's disease can be predicted.

[0072] In one embodiment, the golden time determination unit (440) can determine / predict the end time of the golden time for treatment of Alzheimer's disease based on the second score calculated by the score calculation unit (420). Subsequently, the golden time determination unit (440) can predict the time remaining until the end of the golden time for treatment of Alzheimer's disease of the subject based on the determined end time of the golden time for treatment.

[0073] The internal configuration of the processor (400) illustrated in FIG. 4 is merely an example, and in some embodiments, additional configurations other than the illustrated internal configuration may be included, and some configurations may be omitted. For example, if the processor (400) is the processor (334) of the information processing system of FIG. 3 and some of the above internal configurations are omitted, the processor (314) of the user terminal may be configured to perform the functions of the omitted internal configurations. Furthermore, although the internal configuration of the processor (400) in FIG. 4 is described by separating it by function, this does not necessarily mean that they are physically separated. The tau protein accumulation amount determining unit (410), score calculation unit (420), treatment determination unit (430), and golden time determining unit (440) have been described separately, but this is for the purpose of aiding understanding of the invention and is not limited thereto.

[0074] FIG. 5 is a diagram showing an example of a graph (500) of predicted amounts of tau protein accumulation in a first region and a second region according to a change in time according to an embodiment of the present disclosure. In one example, the first path (510) of the graph (500) shows an example of predicted amounts of tau protein accumulation in the first region of the subject's brain according to a change in time, and the second path (520) shows an example of predicted amounts of tau protein accumulation in the second region of the subject's brain according to a change in time.

[0075] Each of the first pathway (510) and the second pathway (520) may be determined / estimated / generated based on a data set associated with the amount of tau protein accumulation in each of the first and second regions of the brain over time for each of the subject and / or multiple other subjects. For example, each of the first pathway (510) and the second pathway (520) may be determined by standardizing and averaging the amount of tau protein accumulation over time for each of the multiple subjects.

[0076] In one embodiment, a plurality of subjects serving as the basis for determining the first pathway (510) and the second pathway (520) may be selected based on at least one of the subjects' medical information, such as age, gender, race, height, body weight, underlying diseases, body mass index (BMI), blood pressure, or smoking status. Through this configuration, data from other subjects having similar medical characteristics to the subject is used, thereby improving the accuracy of the predicted amount of tau protein accumulation over time. Additionally or alternatively, the first pathway (510) and the second pathway (520) may be standard models generated based on big data associated with the amount of tau protein accumulation in each of the first and second regions of the brains of the plurality of subjects.

[0077] A first threshold (518) of the amount of tau protein accumulation in the first region may be set on the first path (510). In one embodiment, the first threshold (518) may be an amount of tau protein accumulation that can cause tau protein propagation to the surrounding region of the first region by the interaction between the amyloid protein and the tau protein within the first region (e.g., the entorhinal cortex region). The second time (t2) of the graph (500) may refer to the time when the amount of tau protein accumulation in the first region on the first path (510) reaches the first threshold (518).

[0078] In the second pathway (520), a second threshold (528) for the amount of tau protein accumulation in the second region may be set. In one embodiment, the second threshold (528) may correspond to the amount of tau protein accumulation at which amyloid protein and tau protein in the second region (e.g., the inferior gyrus region) interact to cause the propagation of tau protein to a wide area. The fourth time (t4) of the graph (500) may refer to the time when the amount of tau protein accumulation in the second region on the second pathway (520) reaches the second threshold (528).

[0079] In one embodiment, the first threshold (518) and / or the second threshold (528) may be determined differently for each subject based on at least one of the subject's medical information. In another embodiment, the first threshold (518) and / or the second threshold (528) may be set based on a control group data set that includes tau protein accumulation data in a control group where the tau protein accumulation amount corresponds to a normal level or where symptoms of Alzheimer's disease do not manifest.

[0080] In the graph (500), the first accumulation amount (512) and the fourth accumulation amount (522) each represent the predicted amount of tau protein accumulation in the first region and the second region, respectively, at the first time (t1), which is any point in time prior to the second time (t2). The second accumulation amount (514) and the fifth accumulation amount (524) each may represent the predicted amount of tau protein accumulation in the first region and the second region, respectively, at the third time (t3), which is any point in time between the second time (t2) and the fourth time (t4). The third accumulation amount (516) and the sixth accumulation amount (526) each represent the predicted amount of tau protein accumulation in the first region and the second region, respectively, at the fifth time (t5), which is any point in time after the fourth time (t4). Each of the first to fifth times (t1, t2, t3, t4, t5) shown in FIG. 5 is similarly shown and described in FIG. 6 and FIG. 7, which will be described later.

[0081] FIG. 6 is a drawing showing an example of a graph (600) displaying a need score and an efficacy score over time according to one embodiment of the present disclosure. A first curve (610) shows an example of a need score (i.e., a first score) associated with the need for treatment of Alzheimer's disease in a subject. A second curve (620) shows an example of an efficacy score (i.e., a second score) associated with the efficacy of treatment of Alzheimer's disease in a subject.

[0082] The necessity score may be maintained or increased as the amount of tau protein accumulation in the first region of the subject's brain increases, and the efficacy score may be maintained or decreased as the amount of tau protein accumulation in the second region of the subject's brain increases. For example, as illustrated, a first curve (610) may be predetermined so that the necessity score increases rapidly at a second time (t2) when the amount of tau protein accumulation in the first region reaches the first threshold (518) of FIG. 5 and then remains maintained. Additionally, a second curve (620) may be predetermined so that the efficacy score decreases at a fourth time (t4) when the amount of tau protein accumulation in the second region reaches the second threshold (528) of FIG. 5 and then remains maintained.

[0083] The first curve (610) may be associated with / correspond to the first path (510) of FIG. 5. For example, each of the first score (612), second score (614), third score (616), and fourth score (618) on the first curve (610) corresponds, in order, to the first accumulation amount (512), second accumulation amount (514), third accumulation amount (516), and first threshold value (518) on the first path (510) of FIG. 5. For instance, if the accumulation amount of tau protein in the first region of the subject's brain corresponds to the first accumulation amount (512), the subject's need score may be the first score (612).

[0084] The second curve (620) may be associated with / correspond to the second path (520) of FIG. 5. For example, each of the fifth score (622), sixth score (624), seventh score (626), and eighth score (628) on the second curve (620) corresponds, in order, to the fourth accumulation amount (522), fifth accumulation amount (524), sixth accumulation amount (526), ​​and second threshold value (528) on the second path (520) of FIG. 5. For instance, if the accumulation amount of tau protein in the second region of the subject's brain corresponds to the fourth accumulation amount (522), the subject's efficacy score may be the fifth score (622).

[0085] In one embodiment, the subject's need score and / or efficacy score may be calculated based on the time corresponding to the subject's actual measured tau protein accumulation amount and the time interval between the second time (t2) and / or the fourth time (t4).

[0086] For example, if the amount of tau protein accumulation measured in the first region of the subject's brain corresponds to the first accumulation amount (512) of FIG. 5, the subject's need score can be calculated as the first score (612) by reflecting a predetermined amount of score change corresponding to the time interval |t2-t1| in the fourth score (618), which is the score at the second time (t2).

[0087] In another example, if the amount of tau protein accumulation measured in the second region of the subject's brain corresponds to the fifth accumulation amount (524) of FIG. 5, the subject's efficacy score can be calculated as the sixth score (624) by reflecting a predetermined amount of score change corresponding to the time interval |t4-t3| in the eighth score (628), which is the score at the fourth time (t4).

[0088] In one embodiment, based on the calculated need score of the subject, the start time of the golden time for treatment of the subject's Alzheimer's disease can be determined / predicted. Subsequently, based on the determined start time of the golden time for treatment, the time remaining until the start of the golden time for treatment of the subject's Alzheimer's disease can be predicted. For example, if the subject's need score is determined to be the first score (612), the current time on the graph (600) is determined to be the first time (t1), the start time of the subject's golden time for treatment is determined to be the second time (t2), and the time remaining until the start of the subject's golden time for treatment can be predicted as t2-t1.

[0089] In one embodiment, in response to a determination that the need score is less than a predetermined threshold score (e.g., a fourth score (618)), the subject may be identified as likely to respond to Alzheimer's preventive therapy. In this case, the Alzheimer's preventive therapy may include the administration of an amyloid (Aβ) vaccine and / or a tau vaccine.

[0090] In one embodiment, based on the calculated efficacy score, the end time of the golden time for treatment of Alzheimer's disease can be determined / predicted. Based on the determined end time of the golden time for treatment, the time remaining until the end of the golden time for treatment of Alzheimer's disease for the subject can be predicted. For example, if the subject's efficacy score is determined to be the 6th score (624), the current time on the graph (600) is determined to be the 3rd time (t3), the end time of the subject's golden time for treatment is determined to be the 4th time (t4), and the time remaining until the end of the subject's golden time for treatment can be predicted as t4-t3.

[0091] In one embodiment, in response to a determination that the efficacy score is smaller than a predetermined threshold (e.g., the 8th score (628)), the subject may be identified as likely to respond to a combination therapy including amyloid-modifying therapy and tau-modifying therapy as an Alzheimer's treatment.

[0092] FIG. 7 is a diagram illustrating an example of a graph (700) of a predicted value of a composite score over time according to one embodiment of the present disclosure. The graph (700) (or, a composite score associated with the treatment of the subject's Alzheimer's disease) may be calculated based on the curves (610, 620) of FIG. 6 (or, a first score and a second score). For example, the composite score may be calculated by multiplying the subject's first score and second score at a specific point in time. For instance, each of the scores (710, 720, 730, 740, 750) of the graph (700) may be calculated from the product of the necessity score and the efficacy score at each of the first time (t1), second time (t2), third time (t3), fourth time (t4), and fifth time (t5) of FIG. 6.

[0093] In one embodiment, in response to the determination that the subject's total score is greater than a predetermined threshold (e.g., a second score (720) and / or a fourth score (740)), the subject may be identified as being within a golden time for responding to amyloid modulation therapy as an Alzheimer's treatment.

[0094] FIG. 8 is a flowchart illustrating a method (800) for providing predictive information related to the treatment of Alzheimer's disease according to one embodiment of the present disclosure. The method (800) may be performed by at least one processor (e.g., a processor of a user terminal, a processor of an information processing system, or a processor of a device for providing predictive information related to the treatment of Alzheimer's disease). The method (800) may be initiated by the processor receiving a brain scan image of a subject (S810). The subject may be a patient determined to have accumulated beta-amyloid protein (Aβ) above a predetermined threshold in the subject's brain.

[0095] Subsequently, the processor can determine the amount of tau protein accumulation in the first region of the subject's brain based on the scanned image (S820).

[0096] Subsequently, the processor can calculate a first score associated with the need for treatment of Alzheimer's disease in the subject based on the amount of tau protein accumulation in the first region of the subject's brain (S830). In one embodiment, the processor can calculate a first time interval between a first time corresponding to the amount of tau protein accumulation in the first region of the subject's brain and a second time in which the amount of tau protein accumulation is a predetermined first threshold value on a first path representing a predicted value of the amount of tau protein accumulation in the first region according to a change in time, and calculate a first score based on the first time interval. At this time, the first path may be determined based on a data set associated with the amount of tau protein accumulation in the first region of the brain according to a change in time for each of the subject and a plurality of other subjects.

[0097] In one embodiment, the processor may identify that the subject is likely to respond to Alzheimer's prevention therapy in response to determining that the first score is smaller than a predetermined first threshold.

[0098] Subsequently, the processor can determine the amount of tau protein accumulation in a second region of the subject's brain based on the scanned image, and calculate a second score associated with the efficacy of the subject's Alzheimer's disease treatment based on the amount of tau protein accumulation in the second region. In one embodiment, the processor can calculate a second time interval between a third time corresponding to the amount of tau protein accumulation in the second region of the subject's brain and a fourth time where the amount of tau protein accumulation is a predetermined second threshold value on a second path representing a predicted value of the amount of tau protein accumulation in the second region over time, and calculate a second score based on the second time interval. At this time, the second path may be determined based on a data set associated with the amount of tau protein accumulation in the second region of the brain over time for each of the subject and a plurality of other subjects.

[0099] The first and second regions of the brain may be different areas. For example, the first region may be the Entorhinal Cortex (EC), and the second region may be the Inferior Temporal Gyrus (ITG).

[0100] In one embodiment, the processor may identify that the subject is likely to respond to a combination therapy including amyloid modulation therapy and tau modulation therapy as an Alzheimer's treatment in response to determining that the second score is smaller than a predetermined second threshold.

[0101] In one embodiment, the first score may be maintained or increased as the amount of tau protein accumulation in the first region of the subject's brain increases, and the second score may be maintained or decreased as the amount of tau protein accumulation in the second region of the subject's brain increases.

[0102] In one embodiment, the processor may calculate a composite score associated with the treatment of the subject's Alzheimer's disease based on a first score and a second score. In response to determining that the composite score is greater than a predetermined third threshold, the processor may identify that the subject is within a golden time for potential response to amyloid modulation therapy as an Alzheimer's treatment.

[0103] In one embodiment, the processor may determine whether to provide treatment or at least one type of treatment associated with the subject's Alzheimer's disease based on at least one of a first score, a second score, or a composite score.

[0104] In one embodiment, the processor determines the start time of the golden time for treatment of the subject's Alzheimer's disease based on a first score, and based on the determined start time of the golden time for treatment, can predict the time remaining until the start of the golden time for treatment of the subject's Alzheimer's disease.

[0105] In one embodiment, the processor determines the end time of the golden time for treatment of Alzheimer's disease based on a second score, and based on the determined end time of the golden time for treatment, can predict the time remaining until the end of the golden time for treatment of Alzheimer's disease of the subject.

[0106] The flowchart illustrated in FIG. 8 and the description above are merely examples and may be implemented differently in some embodiments. For example, one or more steps may be omitted, the order of each step may be changed, one or more steps may be performed in overlap, or one or more steps may be performed repeatedly.

[0107] The method described above may be provided as a computer program stored on a computer-readable recording medium for execution on a computer. The medium may continuously store a computer-executable program, or temporarily store it for execution or download. Additionally, the medium may be various recording or storage means in the form of a single or multiple hardware components, and may not be limited to a medium directly connected to a computer system but may exist distributed over a network. Examples of media may include magnetic media such as hard disks, floppy disks, and magnetic tapes; optical recording media such as CD-ROMs and DVDs; magneto-optical media such as floptical disks; and media configured to store program instructions, including ROM, RAM, and flash memory. Furthermore, other examples of media may include recording or storage media managed by sites, servers, etc., that supply or distribute various software, such as app stores that distribute applications.

[0108] The methods, operations, or techniques of the present disclosure may be implemented by various means. For example, these techniques may be implemented in hardware, firmware, software, or a combination thereof. Those skilled in the art will understand that the various exemplary logical blocks, modules, circuits, and algorithmic steps described in connection with the disclosure herein may be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate such interchangeability between hardware and software, various exemplary components, blocks, modules, circuits, and steps have been generally described above in terms of their functional aspects. Whether such functions are implemented in hardware or in software depends on the design requirements imposed on the specific application and the overall system. Those skilled in the art may implement the functions described in various ways for each specific application, but such implementations should not be construed as departing from the scope of the present disclosure.

[0109] In a hardware implementation, the processing units used to perform the techniques may be implemented in one or more ASICs, DSPs, digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, electronic devices, other electronic units designed to perform the functions described in this disclosure, computers, or a combination thereof.

[0110] Accordingly, the various exemplary logic blocks, modules, and circuits described in connection with the present disclosure may be implemented or performed by any combination of general-purpose processors, DSPs, ASICs, FPGAs or other programmable logic devices, discrete gate or transistor logic, discrete hardware components, or those designed to perform the functions described herein. A general-purpose processor may be a microprocessor, but alternatively, the processor may be any conventional processor, controller, microcontroller, or state machine. The processor may also be implemented as a combination of computing devices, for example, a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors coupled with a DSP core, or any other combination of configurations.

[0111] In firmware and / or software implementations, techniques may be implemented as instructions stored on a computer-readable medium such as random access memory (RAM), read-only memory (ROM), non-volatile random access memory (NVRAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable PROM (EEPROM), flash memory, compact disc (CD), magnetic or optical data storage devices, etc. The instructions may be executable by one or more processors, and may cause the processor(s) to perform specific aspects of the functions described in this disclosure.

[0112] Although the embodiments described above have been described as utilizing aspects of the subject matter disclosed herein in one or more standalone computer systems, the present disclosure is not limited thereto and may be implemented in conjunction with any computing environment, such as a network or a distributed computing environment. Furthermore, aspects of the subject matter in the present disclosure may be implemented in a plurality of processing chips or devices, and storage may be similarly affected across a plurality of devices. Such devices may include PCs, network servers, and portable devices.

[0113] Although the present disclosure has been described in relation to some embodiments, various modifications and changes may be made without departing from the scope of the present disclosure as understood by a person skilled in the art to which the invention of the present disclosure pertains. Furthermore, such modifications and changes should be considered to fall within the scope of the claims appended to this specification. Explanation of the symbols

[0114] 110: Brain scan image 112: Region 1 114: Zone 2 120: Tau protein accumulation 130: Graph 1 132: Score 1 140: 2nd Graph 142: 2nd Score 150: Graph 3 152: Overall Score

Claims

Claim 1 A method for providing predictive information related to the treatment of Alzheimer's disease, executed by at least one processor, comprises: receiving a brain scan image of a subject; determining the amount of tau protein accumulation in a first region of the subject's brain based on the scan image; calculating a first score associated with the need for treatment of Alzheimer's disease of the subject based on the amount of tau protein accumulation in the first region of the subject's brain; determining the amount of tau protein accumulation in a second region of the subject's brain different from the first region based on the scan image; and calculating a second score associated with the efficacy of treatment of Alzheimer's disease of the subject based on the amount of tau protein accumulation in the second region, wherein the step of calculating the second score comprises, on a second path representing a predicted value of the amount of tau protein accumulation in the second region according to a change over time, calculating a second time interval between a third time corresponding to the amount of tau protein accumulation in the second region of the subject's brain and a fourth time where the amount of tau protein accumulation is a predetermined second threshold. A method for providing predictive information related to the treatment of Alzheimer's disease, comprising the step of calculating the second score based on the second time interval. Claim 2 delete Claim 3 A method for providing predictive information related to the treatment of Alzheimer's disease according to claim 1, wherein the first score is maintained or increases as the amount of tau protein accumulation in the first region of the subject's brain increases, and the second score is maintained or decreases as the amount of tau protein accumulation in the second region of the subject's brain increases. Claim 4 A method for providing predictive information related to the treatment of Alzheimer's disease, comprising, in addition to the step of calculating a comprehensive score related to the treatment of Alzheimer's disease of the subject based on the first score and the second score in claim 1. Claim 5 A method for providing predictive information related to the treatment of Alzheimer's disease, further comprising the step of determining whether to treat or at least one of the type of treatment associated with the subject's Alzheimer's disease based on at least one of the first score, the second score, or the composite score according to claim 4. Claim 6 A method for providing predictive information related to the treatment of Alzheimer's disease, comprising, in addition to the step of estimating the start time of the golden time for the treatment of the subject's Alzheimer's disease based on the first score in claim 1. Claim 7 A method for providing predictive information related to the treatment of Alzheimer's disease, comprising, in claim 6, a step of further predicting the time remaining until the start of the treatment golden time of the subject's Alzheimer's disease based on the start time of the treatment golden time determined above. Claim 8 In claim 1, the step of calculating the first score comprises: a step of calculating a first time interval between a first time corresponding to the amount of tau protein accumulation in the first region of the subject's brain and a second time in which the amount of tau protein accumulation is a predetermined first threshold value on a first path representing a predicted amount of tau protein accumulation in the first region according to a change in time; and a step of calculating the first score based on the first time interval, and a method for providing predictive information related to the treatment of Alzheimer's disease. Claim 9 A method for providing predictive information related to the treatment of Alzheimer's disease, wherein the first pathway is determined based on a dataset associated with the accumulation amount of tau protein in a first region of the brain over time for each of the subject and a plurality of other subjects. Claim 10 A method for providing predictive information related to the treatment of Alzheimer's disease, comprising, in addition to the step of determining the end point of the golden time for the treatment of Alzheimer's disease based on the second score in claim 1. Claim 11 A method for providing predictive information related to the treatment of Alzheimer's disease, comprising, in claim 10, a step of further predicting the time remaining until the end of the golden time for the treatment of the subject's Alzheimer's disease based on the end time of the golden time for treatment determined above. Claim 12 delete Claim 13 A method for providing predictive information related to the treatment of Alzheimer's disease, wherein the second pathway is determined based on a dataset associated with the accumulation amount of tau protein in a second region of the brain over time for each of the subject and a plurality of other subjects. Claim 14 A method for providing predictive information related to the treatment of Alzheimer's disease, wherein, in claim 1, the subject is a patient determined to have accumulated beta-amyloid protein (Aβ) above a predetermined threshold in the subject's brain. Claim 15 A method for providing predictive information related to the treatment of Alzheimer's disease, wherein, in claim 1, the first region is an Entorhinal Cortex (EC) region. Claim 16 A method for providing predictive information related to the treatment of Alzheimer's disease, wherein, in claim 1, the second region is the Inferior Temporal Gyrus (ITG) region. Claim 17 A method for providing predictive information related to the treatment of Alzheimer's disease, further comprising the step of identifying that the subject is within a golden time for responding to amyloid control treatment as an Alzheimer's treatment in response to the determination that the comprehensive score is greater than a predetermined third threshold in claim 4. Claim 18 A method for providing predictive information related to the treatment of Alzheimer's disease, comprising further including the step of identifying that the subject is likely to respond to an Alzheimer's preventive therapy in response to determining that the first score is smaller than a predetermined first threshold in claim 1. Claim 19 A method for providing predictive information related to the treatment of Alzheimer's disease, further comprising the step of identifying that the subject is likely to respond to combination therapy including amyloid modulation therapy and tau modulation therapy as an Alzheimer's treatment, in response to the determination that the second score is smaller than a predetermined second threshold. Claim 20 A computer program stored on a computer-readable recording medium for executing a method according to any one of paragraphs 1, 3 through 11 and 13 through 19 on a computer. Claim 21 In a device, a communication module; memory; and includes at least one processor connected to the memory and configured to execute at least one computer-readable program included in the memory, wherein the at least one program includes instructions for receiving a brain scan image of a subject, determining the amount of tau protein accumulation in a first region of the subject's brain based on the scan image, calculating a first score associated with the need for treatment of the subject's Alzheimer's disease based on the amount of tau protein accumulation in the first region of the subject's brain, determining the amount of tau protein accumulation in a second region of the subject's brain different from the first region based on the scan image, and calculating a second score associated with the efficacy of treatment of the subject's Alzheimer's disease based on the amount of tau protein accumulation in the second region, wherein calculating the second score comprises calculating a second time interval between a third time corresponding to the amount of tau protein accumulation in the second region of the subject's brain and a fourth time in which the amount of tau protein accumulation is a predetermined second threshold value on a second path representing a predicted value of the amount of tau protein accumulation in the second region according to a change over time, and based on the second time interval A device comprising calculating a second score.

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