AI-based mental illness diagnosis system and method using exosome SERS signals

Abstract

The present invention relates to an AI-based system and method for diagnosing mental illness using exosome SERS signals. The AI-based system for diagnosing mental illness using exosome SERS signals includes: a first learning unit that inputs a first signal map acquired using exosomes acquired from a normal subject and a second signal map acquired using exosomes acquired from a patient with a mental illness into a mental illness diagnostic algorithm and trains the algorithm to classify the exosome SERS signals included in the input signal map as 0 or 1; a signal acquisition unit that drops exosomes acquired from a subject onto a chip containing a plurality of dot arrays and then acquires a signal map including the plurality of exosome SERS signals from the chip; and a diagnosis unit that inputs the acquired signal map into the trained mental illness diagnostic algorithm to acquire a signal value of 0 or 1 for each exosome SERS signal included in the signal map and diagnoses the subject as normal or mentally ill using the average of the acquired signal values.

Description

[Technical Field]

[0001] The present invention relates to an artificial intelligence-based mental illness diagnosis system and method using exosome SERS signals, and more specifically, to a mental illness diagnosis system and method that detects a SERS signal map that covers the entire exosome rather than a specific marker, and diagnoses and classifies mental illnesses through an artificial intelligence algorithm trained using the detected SERS signal map. [Background technology]

[0002] Mental illnesses involve disturbances in function, emotions, and behavior, and are diagnosed as mental illnesses or mental health disorders when the disturbances cause significant distress and interference with daily life.

[0003] The effects of mental illness can be long-lasting or temporary, and nearly 50% of adults will experience a mental illness at some point in their lives. Depression is the number one cause of illness leading to mental disability, and despite being a very common illness, only about 20% of people with a mental illness receive professional help.

[0004] In general, mental illnesses are always clearly distinguishable from normal behavior, but it is very difficult to distinguish between those who have certain personality traits and those who have a personality disorder.

[0005] Therefore, in order to ensure objectivity in diagnosing mental disorders, mental disorders have traditionally been diagnosed according to the criteria set out in the Diagnostic and Statistical Manual of Mental Disorders (DSM), published by the American Psychiatric Association.

[0006] However, the Diagnostic and Statistical Manual of Mental Disorders (DSM) is merely a collection of clinical symptoms and has not been verified by laboratory evaluations (imaging, blood tests, neurophysiological tests, etc.). Therefore, in recent years, research has been actively conducted on a validated psychiatric diagnostic classification system that is not based solely on clinical medical experience.

[0007] The technology behind the present invention is disclosed in Patent Publication No. 2021-112167 (published on August 5, 2021). Summary of the Invention [Problem to be solved by the invention]

[0008] Thus, the present invention provides a mental illness diagnostic system and method that detects a SERS signal map across the entire exosome rather than a specific marker, and diagnoses and classifies mental illnesses through an artificial intelligence algorithm trained using the detected SERS signal map. [Means for solving the problem]

[0009] According to an embodiment of the present invention for achieving this technical objective, an AI-based mental illness diagnosis system using exosome SERS signals includes: a first learning unit that inputs a first signal map acquired using exosomes acquired from a normal subject and a second signal map acquired using exosomes acquired from a patient with a mental illness into a mental illness diagnosis algorithm and trains the mental illness diagnosis algorithm to classify the exosome SERS signals included in the input signal map as 0 or 1; a signal acquisition unit that drops exosomes acquired from the subject onto a chip including a plurality of dot arrays and then acquires a signal map including a plurality of exosome SERS signals from the chip; and a diagnosis unit that inputs the acquired signal map into the trained mental illness diagnosis algorithm to acquire a signal value of 0 or 1 for each exosome SERS signal included in the signal map and diagnoses the subject as normal or mentally ill using the average of the acquired signal values.

[0010] The measurement device may further include a classification unit that, if diagnosed with a mental illness, inputs a plurality of exosome SERS signals acquired from the subject into a plurality of mental illness classification algorithms to acquire a signal value of 0 or 1 for each of the plurality of exosome SERS signals, and classifies the type of mental illness using an average of the acquired signal values.

[0011] The system may further include a SERS signal collector that acquires a first signal map from exosomes obtained from a normal subject and a second signal map from exosomes obtained from a psychiatric patient, and then labels all n*m (where n and m are the same or different natural numbers) exosome SERS signals included in the first signal map as 0 and all n*m exosome SERS signals included in the second signal map as 1.

[0012] The system may further include a second learning unit that inputs second signal maps acquired from psychiatric patients who fall under a specific type of psychiatric disorder among the psychiatric patients and second signal maps acquired from the remaining psychiatric patients excluding the specific type of psychiatric disorder into the plurality of psychiatric disorder classification algorithms, and trains each of the psychiatric disorder classification algorithms to determine whether the input signal map falls under the specific type of psychiatric disorder.

[0013] The diagnostic unit inputs the n*m ​​exosome SERS signals included in the acquired signal map into the mental illness diagnostic algorithm, outputs a signal value of 0 or 1 corresponding to each of the n*m ​​exosome SERS signals, and classifies the subject as normal if the average of the output signal values ​​is close to 0, and diagnoses the subject as having a mental illness if the average of the output signal values ​​is close to 1.

[0014] The classification unit inputs n*m exosome SERS signals into the plurality of psychiatric disorder classification algorithms, and the plurality of psychiatric disorder classification algorithms output signal values ​​of 0 or 1 for each of the input n*m exosome SERS signals, and compare the average of the output signal values ​​with a classification standard value for a specific type of psychiatric disorder to determine whether or not the specific type of psychiatric disorder is present.

[0015] Furthermore, a method for diagnosing a mental illness using a mental illness diagnostic system according to an embodiment of the present invention includes the steps of: inputting a first signal map obtained using exosomes obtained from a normal subject and a second signal map obtained using exosomes obtained from a patient with a mental illness into a mental illness diagnostic algorithm, and training the mental illness diagnostic algorithm to classify exosome SERS (Surface Enhanced Raman Spectroscopy) signals included in the input signal maps as 0 or 1; dropping exosomes obtained from a subject onto a chip including a plurality of dot arrays, and then acquiring a signal map including a plurality of exosome SERS signals from the chip; and inputting the acquired signal map into the trained mental illness diagnostic algorithm to acquire a signal value of 0 or 1 for each exosome SERS signal included in the signal map, and diagnosing the subject as normal or mentally ill using the average of the acquired signal values. [Effects of the Invention]

[0016] Thus, according to the present invention, the presence or absence of a mental disorder can be diagnosed by inputting an exosome SERS signal map into a mental disorder diagnostic algorithm. Furthermore, an exosome SERS signal map that has been diagnosed as having a mental disorder can be input into multiple mental disorder classification algorithms and reanalyzed to classify the type of mental disorder, thereby enabling a specific diagnosis of the mental disorder. [Brief explanation of the drawings]

[0017] [Figure 1]1 is a configuration diagram illustrating a mental illness diagnostic system according to an embodiment of the present invention. [Figure 2] 1 is a flowchart illustrating a mental illness diagnosis method using a mental illness diagnosis system according to an embodiment of the present invention. [Figure 3] FIG. 3 is an exemplary diagram illustrating step S210 shown in FIG. 2. [Figure 4] FIG. 1 is an illustrative diagram showing the results of nanoparticle tracking analysis of exosomes from normal individuals and exosomes from patients with depression. [Figure 5] FIG. 3 is an illustrative diagram illustrating a method for labeling exosome SERS signals in step S220 shown in FIG. 2. [Figure 6] FIG. 3 is an illustrative diagram illustrating a method for training a mental illness diagnosis algorithm using the SERS signal of labeled exosomes in step S220 shown in FIG. 2. [Figure 7] FIG. 1 is an exemplary diagram showing the results of a performance evaluation of an AI-based mental disorder diagnosis method using exosome SERS signals according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0018] Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings, in which the thickness of lines and the size of components shown in the drawings are exaggerated for clarity and convenience of explanation.

[0019] In addition, the terms described below are defined in consideration of their functions in the present invention, and may vary depending on the intentions or practices of users or operators. Therefore, the definitions of such terms should be based on the overall content of this specification.

[0020] The mental illness diagnostic system according to the embodiment of the present invention will be described in more detail below with reference to FIG.

[0021] FIG. 1 is a configuration diagram illustrating a mental illness diagnostic system according to an embodiment of the present invention.

[0022] As shown in FIG. 1, a mental illness diagnosis system 100 according to an embodiment of the present invention includes a SERS signal collecting unit 110, a first learning unit 120, a second learning unit 130, a signal acquiring unit 140, a diagnosing unit 150, and a classifying unit 160.

[0023] First, the SERS signal collecting unit 110 performs Raman spectroscopy on exosomes collected from the plasma of a normal individual and exosomes collected from the plasma of a patient with a mental illness, and obtains signal maps for each.

[0024] Here, the signal map has a size of n*m depending on the number of dot arrays included in the chip, and the SERS signal collector 110 acquires n*m exosome SERS signals depending on the size of the signal map.

[0025] For the sake of convenience, the signal map obtained from a normal subject will be referred to as a first signal map, and the signal map obtained from a mental illness patient will be referred to as a second signal map.

[0026] Then, the SERS signal collecting unit 110 labels the n*m ​​exosome SERS signals included in the first signal map as 0, and labels the n*m ​​exosome SERS signals included in the second signal map as 1.

[0027] The first learning unit 120 constructs a mental illness diagnosis algorithm based on deep learning, and inputs the exosome SERS signal labeled with 0 and the exosome SERS signal labeled with 1 into the constructed mental illness diagnosis algorithm for learning. Then, the mental illness diagnosis algorithm outputs a signal value of 0 or 1 corresponding to the input exosome SERS signal.

[0028] The second learning unit 130 constructs a mental illness classification algorithm based on deep learning. At this time, a plurality of mental illness classification algorithms are constructed corresponding to the types of mental illness. Here, the types of mental illness may include at least one of depression, bipolar disorder, panic disorder, schizophrenia, dementia, and delusional disorder, but are not necessarily limited thereto and may include various other types of mental illness.

[0029] Next, the second learning unit 130 inputs the exosome SERS signals acquired from each psychiatric patient into multiple psychiatric disorder classification algorithms for training. Here, the classification algorithm for a specific type of psychiatric disorder receives the exosome SERS signals acquired from the psychiatric patient corresponding to the specific type of psychiatric disorder and the exosome SERS signals acquired from the remaining types of psychiatric disorder patients excluding the specific type of psychiatric disorder, and is trained to determine whether the exosome SERS signals correspond to the specific type of psychiatric disorder. The psychiatric disorder classification algorithm then outputs a signal value of 0 or 1 for the input exosome SERS signals.

[0030] The signal acquisition unit 140 drops exosomes collected from the plasma of the subject onto a chip, and then performs Raman spectroscopy on the chip to acquire multiple exosome SERS signals.

[0031] The diagnostic unit 150 inputs the acquired exosome SERS signals into a trained mental illness diagnostic algorithm and acquires a signal value of 0 or 1 for each exosome SERS signal. The diagnostic unit 150 then diagnoses the subject as normal or mentally ill using the average of the acquired signal values.

[0032] If the subject is diagnosed with a mental illness, the classification unit 160 inputs the multiple exosome SERS signals into multiple trained mental illness classification algorithms, respectively.

[0033] Then, the multiple mental illness classification algorithms output signal values ​​of 0 or 1 for the multiple input exosome SERS signals, and the classification unit 160 compares the average of the output signal values ​​with the classification standard value for the corresponding type of mental illness to determine whether or not the signal corresponds to each type of mental illness.

[0034] For example, a depression classification algorithm outputs a signal value of 0 or 1 for multiple input exosome SERS signals and compares the average of the output signal values ​​with a depression classification standard to determine whether depression occurs. A manic-depression classification algorithm outputs a signal value of 0 or 1 for multiple input exosome SERS signals and compares the average of the output signal values ​​with a manic-depression classification standard to determine whether manic-depression occurs. A panic disorder classification algorithm outputs a signal value of 0 or 1 for multiple input exosome SERS signals and compares the average of the output signal values ​​with a panic disorder classification standard to determine whether panic disorder occurs. A schizophrenia classification algorithm outputs a signal value of 0 or 1 for multiple input exosome SERS signals and compares the average of the output signal values ​​with a schizophrenia classification standard to determine whether schizophrenia occurs. The dementia classification algorithm outputs a signal value of 0 or 1 for multiple input exosome SERS signals and compares the average of the output signal values ​​with a dementia classification standard value to determine whether or not a subject has dementia.Finally, the delusional disorder classification algorithm outputs a signal value of 0 or 1 for multiple input exosome SERS signals and compares the average of the output signal values ​​with a delusional disorder classification standard value to determine whether or not a subject has a delusional disorder.

[0035] The classification unit 160 then provides information about the mental illnesses that were determined to be positive.

[0036] A mental illness diagnostic method using a mental illness diagnostic system according to an embodiment of the present invention will be described in more detail with reference to FIGS.

[0037] FIG. 2 is a flowchart illustrating a mental illness diagnosis method using the mental illness diagnosis system according to an embodiment of the present invention.

[0038] As shown in Figure 2, a mental illness diagnosis method using a mental illness diagnosis system according to an embodiment of the present invention includes a step of learning a mental illness diagnosis algorithm and a mental illness classification algorithm, and a step of diagnosing a mental illness using the learned mental illness diagnosis algorithm and mental illness classification algorithm.

[0039] First, the stage of training the mental illness diagnosis algorithm and the mental illness classification algorithm will be described. The mental illness diagnosis system 100 collects exosome SERS signals from a group of normal subjects and a group of mental illness patients (step S210).

[0040] FIG. 3 is an exemplary diagram illustrating step S210 shown in FIG.

[0041] As shown in Figure 3, plasma samples from normal individuals were obtained and exosomes were isolated from the plasma using size exclusion chromatography (SEC). Exosomes were also isolated from plasma samples from patients with psychiatric disorders using the same method.

[0042] Exosomes isolated from plasma samples of patients with psychiatric disorders contain both psychiatric disorder-associated exosomes and normal exosomes.

[0043] FIG. 4 is an illustrative diagram showing the results of nanoparticle tracking analysis of exosomes from normal subjects and exosomes from patients with depression.

[0044] As shown in Figure 4, the results of nanoparticle tracking analysis confirm that there are no significant differences in particle size, mode size, or particle concentration between exosomes from healthy individuals and exosomes from patients with depression. However, heterogeneity was observed in the signals detected by Raman spectroscopy.

[0045] Therefore, according to an embodiment of the present invention, exosomes isolated from plasma samples of normal individuals and exosomes isolated from patients with psychiatric disorders are subjected to Raman spectroscopy to detect exosome SERS signals.

[0046] To explain this in more detail, an exosome solution isolated from a plasma sample of a normal individual and an exosome solution isolated from a plasma sample of a psychiatric patient are dropped onto each Au nanoparticle assembly array chip and then dried.

[0047] Here, the Au nanoparticle assembly array chip is prepared by precipitating Au nanoparticles (AuNPs) in a colloidal solution and then coating the NPs on an APTES-functionalized glass surface. To increase the detection throughput and uniformity of the signal acquisition process on the APTES-functionalized glass surface, the Au nanoparticle assembly array chip includes an n*m dot array (where n and m are the same or different natural numbers), and the exosome SERS signal is measured at each dot.

[0048] Next, the SERS signal collecting unit 110 performs Raman spectroscopy on the Au nanoparticle assembly array chip on which the exosome solution has been dried to collect an exosome SERS signal map containing multiple exosome SERS signals corresponding to the dot array.

[0049] That is, the SERS signal collecting unit 110 collects a first signal map including n*m first exosome SERS signals from the exosome solution of a normal individual, and collects a second signal map including n*m second exosome SERS signals from the exosome solution of a mental illness patient.

[0050] The mental illness diagnostic system 100 according to an embodiment of the present invention constructs a mental illness patient group using patients who have been diagnosed with at least one mental illness from among depression, bipolar disorder, panic disorder, schizophrenia, dementia, and delusional disorder.

[0051] After step S210 is completed, the first learning unit 120 learns a mental illness diagnosis algorithm using the first signal map acquired from the normal group and the second signal map acquired from the mental illness patient group (step S220).

[0052] FIG. 5 is an exemplary diagram illustrating a method for labeling an exosome SERS signal in step S220 shown in FIG. 2, and FIG. 6 is an exemplary diagram illustrating a method for training a mental disorder diagnosis algorithm using the exosome SERS signal labeled in step S220 shown in FIG. 2.

[0053] Since the exosome solution from a patient with a psychiatric disorder contains both normal exosomes and exosomes associated with a psychiatric disorder, the n*m ​​dots arranged on the Au nanoparticle assembly array chip may contain only one of normal exosomes or exosomes associated with a psychiatric disorder, or may contain both normal exosomes and exosomes associated with a psychiatric disorder. In other words, the SERS signals of the multiple exosomes corresponding to the dot array are output differently.

[0054] As shown in FIG. 5, the first learning unit 120 according to an embodiment of the present invention does not classify exosome SERS signals according to the presence or absence of psychiatric disease-related exosomes or normal exosomes, but labels all of the first exosome SERS signals obtained from normal individuals as 0, and labels all of the second exosome SERS signals obtained from psychiatric disease patients as 1.

[0055] Next, as shown in FIG. 6, the first learning unit 120 randomly extracts learning data and test data from the first exosome SERS signal and the second exosome SERS signal.

[0056] Then, the first learning unit 120 uses the first exosome SERS signal and the second exosome SERS signal corresponding to the extracted learning data as input data and the labeled values ​​as output data to learn a mental illness diagnosis algorithm.

[0057] That is, the mental illness diagnostic algorithm outputs a signal value of 0 or 1 corresponding to multiple input exosome SERS signals, and first diagnoses the presence or absence of a mental illness using the average of the output signal values.

[0058] Next, the second learning unit 130 learns a mental illness diagnostic algorithm using the second exosome SERS signals of the mental illness patient group acquired in step S210 (step S230).

[0059] The second learning unit 130 constructs a plurality of mental illness classification algorithms corresponding to depression, bipolar disorder, panic disorder, schizophrenia, dementia, and delusional disorder, respectively.

[0060] Then, the second learning unit 130 inputs the second exosome SERS signals obtained from the depression patient group and the second exosome SERS signals obtained from the remaining types of mental illness patient groups excluding depression patients into the depression classification algorithm, and trains the depression classification algorithm to output a signal value of 0 or 1 corresponding to the input exosome SERS signals.

[0061] In addition, the second learning unit 130 inputs the second exosome SERS signals obtained from the group of bipolar disorder patients and the second exosome SERS signals obtained from the group of patients with the remaining types of mental illness excluding the bipolar disorder patients into the panic disorder classification algorithm, and trains the panic disorder classification algorithm to output a signal value of 0 or 1 in response to the input exosome SERS signals.

[0062] The second learning unit 130 also trains the panic disorder classification algorithm, the schizophrenia classification algorithm, the dementia classification algorithm, and the delusional disorder classification algorithm in the same manner.

[0063] Once learning of the algorithm is completed using steps S210 to S230, the mental illness diagnosis system 100 diagnoses a mental illness in the subject.

[0064] First, the signal acquiring unit 140 acquires the SERS signal of exosomes extracted from the plasma of the subject (step S240).

[0065] The user collects plasma from the subject and applies chromatography to the collected plasma to separate exosomes.

[0066] The user then drops the solution containing dissolved exosomes onto the Au nanoparticle assembly array chip and allows it to dry.

[0067] Next, Raman spectroscopy is performed on the Au nanoparticle assembly array chip to acquire n*m ​​(e.g., 100) exosome SERS signals corresponding to the dot array.

[0068] When step S240 is completed, the diagnosis unit 150 inputs the n*m ​​exosome SERS signals into a mental illness diagnosis algorithm to determine whether the subject has a mental illness (step S250).

[0069] More specifically, the diagnosis unit 150 inputs the n*m ​​exosome SERS signals into a mental illness diagnosis algorithm, which then outputs a signal value of 0 or 1 corresponding to each of the n*m ​​exosome SERS signals.

[0070] If the average of the output signal values ​​is close to 0, the diagnosis unit 150 diagnoses the subject as a normal person, and if the average of the output signal values ​​is close to 1, the diagnosis unit 150 diagnoses the subject as a mental illness patient.

[0071] If the subject is diagnosed as a mental illness patient in step S250, the classification unit 160 inputs the n*m ​​exosome SERS signals into multiple trained mental illness classification algorithms to classify the mental illness of the subject (step S260).

[0072] In more detail, the classification unit 160 inputs the n*m ​​exosome SERS signals into a depression classification algorithm, a bipolar disorder classification algorithm, a panic disorder classification algorithm, a schizophrenia classification algorithm, a dementia classification algorithm, and a delusional disorder classification algorithm, respectively.

[0073] The depression classification algorithm then outputs a signal value of 0 or 1 for each of the input n*m exosome SERS signals, and compares the average of the output signal values ​​with the depression classification standard value to determine whether the subject has depression.

[0074] The bipolar disorder classification algorithm outputs a signal value of 0 or 1 for each of the n*m ​​exosome SERS signals input, and compares the average of the output signal values ​​with the bipolar disorder classification standard value to determine whether the subject has bipolar disorder.

[0075] The panic disorder classification algorithm outputs a signal value of 0 or 1 for each of the n*m ​​exosome SERS signals input, and compares the average of the output signal values ​​with the panic disorder classification standard value to determine whether the subject has panic disorder.

[0076] The schizophrenia classification algorithm outputs a signal value of 0 or 1 for each of the n*m ​​input exosome SERS signals, and compares the average of the output signal values ​​with the schizophrenia classification standard value to determine whether the subject has schizophrenia.

[0077] The dementia classification algorithm outputs a signal value of 0 or 1 for each of the input n*m exosome SERS signals, and compares the average of the output signal values ​​with the dementia classification standard value to determine whether the subject has dementia.

[0078] Finally, the delusional disorder classification algorithm outputs a signal value of 0 or 1 for each of the n*m ​​exosome SERS signals input, and compares the average of the output signal values ​​with the delusional disorder classification standard value to determine whether the subject has delusional disorder.

[0079] The classifier 160 then uses the output results from the six mental illness classification algorithms to provide a predicted outcome of the mental illness.

[0080] FIG. 7 is an exemplary view showing the results of performance evaluation of an AI-based mental disorder diagnosis method using exosome SERS signals according to an embodiment of the present invention.

[0081] As shown in Figure 7a, the human maps obtained from exosomes of normal individuals and those obtained from depressed patients are easily distinguishable with the naked eye.

[0082] As shown in Figure 7b, the final diagnostic value was approximately 1.86 times higher in the depression patient group than in the normal group.

[0083] As shown in Figure 7c, a receiver operating characteristic (ROC) curve was plotted to verify the effectiveness of the mental illness diagnosis algorithm or mental illness classification algorithm. The area under the ROC curve (AUC) indicates greater usefulness as it approaches 1. The mental illness diagnosis algorithm or mental illness classification algorithm according to an embodiment of the present invention had a value of approximately 0.939, a sensitivity of 91.4%, and a specificity of approximately 88.6%. This indicates that the mental illness diagnosis algorithm or mental illness classification algorithm has excellent performance.

[0084] As described above, the mental illness diagnosis system according to the present invention can diagnose the presence or absence of a mental illness by inputting an exosome SERS signal map into a mental illness diagnosis algorithm, and further, by inputting an exosome SERS signal map that has been diagnosed as having a mental illness into multiple mental illness classification algorithms and reanalyzing it to classify the type of mental illness, a specific diagnosis of the mental illness is possible.

[0085] Although the present invention has been described with reference to the embodiments shown in the drawings, these are merely illustrative, and those skilled in the art will recognize that various modifications and equivalent embodiments are possible. Therefore, the true technical scope of protection of the present invention should be determined by the technical spirit of the claims. [Explanation of symbols]

[0086] 100: Mental Illness Diagnostic System 110: SERS signal collection unit 120: First Study Section 130: Second Learning Section 140: Signal acquisition section 150: Diagnostic Department 160: Classification department

Claims

1. In an artificial intelligence-based mental illness diagnosis system using exosome SERS signals, A first learning unit that inputs a first signal map obtained using exosomes obtained from a normal subject and a second signal map obtained using exosomes obtained from a psychiatric patient into a psychiatric disorder diagnostic algorithm and trains the psychiatric disorder diagnostic algorithm to classify the exosome SERS signals included in the input signal maps into 0 or 1, respectively; A signal acquisition unit that drops exosomes acquired from a subject onto a chip including a plurality of dot arrays and then acquires a signal map including a plurality of exosome SERS signals from the chip; and A diagnostic unit that inputs the acquired signal map into a trained mental illness diagnostic algorithm, acquires a signal value of 0 or 1 for each exosome SERS signal included in the signal map, and diagnoses the subject as normal or mentally ill using the average of the acquired signal values; A mental illness diagnostic system, including:

2. 2. The mental illness diagnostic system according to claim 1, further comprising a classification unit that, if diagnosed with a mental illness, inputs a plurality of exosome SERS signals acquired from the subject into a plurality of mental illness classification algorithms to acquire a signal value of 0 or 1 for each of the plurality of exosome SERS signals, and classifies the type of mental illness using an average of the acquired signal values.

3. 3. The mental illness diagnosis system of claim 2, further comprising a SERS signal collection unit that acquires a first signal map from exosomes obtained from a normal subject and a second signal map from exosomes obtained from a mental illness patient, and then labels all n*m (where n and m are the same or different natural numbers) exosome SERS signals included in the first signal map as 0 and all n*m exosome SERS signals included in the second signal map as 1.

4. The mental illness diagnosis system of claim 3, further comprising a second learning unit that inputs second signal maps obtained from mental illness patients who fall under a specific type of mental illness from among the mental illness patients and second signal maps obtained from the remaining mental illness patients excluding the specific type of mental illness into the plurality of mental illness classification algorithms, and trains each of the mental illness classification algorithms to determine whether the input signal map falls under the specific type of mental illness.

5. The diagnostic unit The n*m ​​exosome SERS signals included in the acquired signal map are input to the mental illness diagnosis algorithm, and a signal value of 0 or 1 is output corresponding to each of the n*m ​​exosome SERS signals; 4. The mental illness diagnostic system according to claim 3, wherein if the average of the output signal values ​​is close to 0, the subject is classified as normal, and if the average of the output signal values ​​is close to 1, the subject is diagnosed as having a mental illness.

6. The classification unit 5. The mental illness diagnostic system of claim 4, wherein n*m exosome SERS signals are input to the plurality of mental illness classification algorithms, and the plurality of mental illness classification algorithms output a signal value of 0 or 1 for each of the input n*m exosome SERS signals, and compare the average of the output signal values ​​with a classification standard value for a specific type of mental illness to determine whether or not the specific type of mental illness applies.

7. A mental illness diagnostic method using a mental illness diagnostic system, A step of inputting a first signal map obtained using exosomes obtained from a normal subject and a second signal map obtained using exosomes obtained from a patient with a mental illness into a mental illness diagnostic algorithm, and training the mental illness diagnostic algorithm to classify the exosome SERS signals included in the input signal maps as 0 or 1, respectively; A step of dropping exosomes obtained from a subject onto a chip including a plurality of dot arrays, and then obtaining a signal map including a plurality of exosome SERS signals from the chip; and The acquired signal map is input into a trained mental illness diagnosis algorithm to acquire a signal value of 0 or 1 for each exosome SERS signal included in the signal map, and the average of the acquired signal values ​​is used to diagnose the subject as normal or mentally ill; A method for diagnosing a mental disorder, comprising:

Images