Methods for diagnosing and classifying tinnitus patients and providing individualized treatment protocols for tinnitus patients - Patents.com

Abstract

The present invention relates to a method for diagnosing tinnitus patients, comprising: an information input step of inputting information including patient information, tinnitus test results and interview results; and a diagnosis step of deriving a scale for tinnitus symptoms, a negative thought scale related to tinnitus, and a negative emotion scale related to tinnitus from the information, and diagnosing the patient based on the scale for tinnitus symptoms, the negative thought scale related to tinnitus, and the negative emotion scale related to tinnitus, wherein the interview includes items for negative thoughts related to tinnitus and items for negative emotions related to tinnitus, and the diagnosis step is performed using a deep learning-based artificial intelligence algorithm.

Description

[Technical field]

[0001] The present invention relates to a method for providing a patient with an individualized treatment protocol, and more particularly to a method for diagnosing or classifying tinnitus patients and providing them with an appropriate individualized treatment protocol. [Background technology]

[0002] Generally, tinnitus refers to a state where sound stimuli are felt in the absence of external auditory stimuli. Tinnitus is known to occur mainly as a complication of hearing loss, and has been known to occur when the cerebrum compensates for the inability of the cerebrum to receive normal sound stimuli due to hearing loss. However, various studies have confirmed that there are many causes of tinnitus other than hearing loss, such as stress, fatigue, noise, and ear canal.

[0003] Tinnitus has a variety of causes and symptoms, so different treatments are given to different patients. In particular, many efforts have been made to provide individualized treatment for patients according to their tinnitus symptoms (Korean Patent Registration No. 10-105766).

[0004] However, the effectiveness of treatment varies greatly depending on the patient, and as a result, various treatment methods have been developed, with more than 15 different treatment methods currently being used in Korea, including drug therapy, tinnitus masking, hearing aid insertion surgery, and regular consultation therapy.

[0005] Tinnitus patients account for approximately 10% of the total population (estimated at approximately 5 million people), and approximately 30% of the elderly population, but there is still no established treatment, and tinnitus retraining therapy, which consists of sound therapy and consultation, is the most widely used clinical treatment. However, the reality is that these various tinnitus treatment methods are not currently being efficiently provided to patients in a way that suits them. Although much research is being conducted on individualized treatment based on tinnitus symptoms, the selection of treatment methods based on other causes such as psychological factors is done by the doctor's intuition without any special criteria. Summary of the Invention [Problem to be solved by the invention]

[0006] The present invention is intended to solve the problems of the prior art described above, and its object is to provide a method for determining an optimal tinnitus treatment protocol that reflects various factors that affect tinnitus treatment. [Means for solving the problem]

[0007] To achieve the above-mentioned object, the method for diagnosing tinnitus patients according to the present invention includes an information input step of inputting information including patient information, tinnitus test results and interview results, and a diagnosis step of deriving a scale for tinnitus symptoms, a negative thought scale related to tinnitus, and a negative emotion scale related to tinnitus from the information, and diagnosing the patient based on the scale for tinnitus symptoms, the negative thought scale related to tinnitus, and the negative emotion scale related to tinnitus, wherein the interview includes items for negative thoughts related to tinnitus and items for negative emotions related to tinnitus, and the diagnosis step is performed using a deep learning-based artificial intelligence algorithm.

[0008] In this case, the scale for the tinnitus symptom may be derived to reflect the results of a tinnitus test.

[0009] The tinnitus-related negative thought scale and the tinnitus-related negative emotion scale may be derived based on the results of a medical interview.

[0010] A method for classifying tinnitus patients according to another aspect of the present invention includes an information input step of inputting information including patient information, tinnitus test results and interview results, a diagnosis step of deriving a scale for tinnitus symptoms, a negative thought scale related to tinnitus, and a negative emotion scale related to tinnitus from the information, and diagnosing the patient based on the scale for tinnitus symptoms, the negative thought scale related to tinnitus, and the negative emotion scale related to tinnitus, and a classification step of classifying the patient into a plurality of treatment groups based on the diagnostic information for the patient, wherein the interview includes items for negative thoughts related to tinnitus and items for negative emotions related to tinnitus, and the diagnosis step and the classification step are performed by a deep learning-based artificial intelligence algorithm.

[0011] In this case, the scale for the tinnitus symptom may be derived to reflect the results of a tinnitus test.

[0012] The tinnitus-related negative thought scale and the tinnitus-related negative emotion scale may be derived reflecting the results of a medical interview.

[0013] The treatment groups may consist of a functional treatment group, an affective treatment group and a cognitive treatment group.

[0014] According to another aspect of the present invention, there is provided a method for providing an individualized treatment protocol for a tinnitus patient, comprising: an information input step of inputting information including patient information, tinnitus test results, and interview results; a diagnosis step of deriving a scale for tinnitus symptoms, a negative thought scale related to tinnitus, and a negative emotion scale related to tinnitus from the information, and diagnosing the patient based on the scale for tinnitus symptoms, the negative thought scale related to tinnitus, and the negative emotion scale related to tinnitus; and a treatment protocol providing step of providing an individualized treatment protocol for the patient based on the diagnosis result, wherein the interview includes an item for negative thoughts related to tinnitus and an item for negative emotions related to tinnitus, the treatment protocol is a treatment process composed of a plurality of treatment methods, and the treatment protocol providing step provides an individualized treatment protocol for the patient by combining a treatment method selected from a functional treatment method group, an emotional treatment method group, and a cognitive treatment method group based on the diagnosis result, and the diagnosis step and the treatment protocol providing step are performed by a deep learning-based artificial intelligence algorithm.

[0015] According to another aspect of the present invention, there is provided a method for providing an individualized treatment protocol for a tinnitus patient, comprising: an information input step of inputting information including patient information, tinnitus test results, and interview results; a diagnosis step of deriving a scale for tinnitus symptoms, a negative thought scale related to tinnitus, and a negative emotion scale related to tinnitus from the information, and diagnosing the patient based on the scale for tinnitus symptoms, the negative thought scale related to tinnitus, and the negative emotion scale related to tinnitus; a classification step of classifying the patient into a plurality of treatment groups based on the diagnostic information for the patient; and a treatment protocol providing step of providing an individualized treatment protocol for the patient based on the classification result, wherein the interview includes an item for negative thoughts related to tinnitus and an item for negative emotions related to tinnitus, the treatment protocol is a treatment process composed of a plurality of treatment methods, and the treatment protocol providing step provides an individualized treatment protocol for the patient by combining a treatment method selected from a functional treatment method group, an emotional treatment method group, and a cognitive treatment method group based on the classification result, and the diagnosis step, the classification step, and the treatment protocol providing step are performed by a deep learning-based artificial intelligence algorithm.

[0016] The treatment groups may consist of a functional treatment group, an affective treatment group and a cognitive treatment group.

[0017] The scale for tinnitus symptoms may be derived reflecting the results of a tinnitus test.

[0018] The tinnitus-related negative thoughts scale and the tinnitus-related negative emotions scale may be derived reflecting the results of a medical interview.

[0019] Functional treatment methods are those that alleviate or directly treat tinnitus symptoms and may include sound therapy and tinnitus retraining therapy.

[0020] Affective treatment modalities may include meditation therapy, relaxation therapy, sleep hygiene therapy, mindfulness therapy, and acceptance and commitment therapy (ACT).

[0021] Cognitive treatment methods may include thought book treatments and cognitive education treatments. Effect of the Invention

[0022] The present invention configured as described above has the advantage that it is possible to accurately diagnose and classify patients by performing diagnosis and classification by reflecting the emotional and cognitive factors of the patient using an artificial intelligence algorithm, and also to diagnose and classify patients more quickly because the diagnosis and classification of patients is performed automatically.

[0023] In addition, unlike the conventional approach in which the process of reflecting emotional and cognitive factors when applying a patient treatment method relied on the doctor's intuition, by constructing and providing a treatment protocol using an artificial intelligence algorithm, it is possible to improve the effectiveness of treatment. [Brief description of the drawings]

[0024] [Figure 1] 1 is a flow chart illustrating the process of diagnosing and classifying a patient to provide an individualized treatment protocol in an embodiment of the present invention. [Diagram 2] 1 is a flow chart illustrating a process for providing an individualized treatment protocol according to an embodiment of the present invention. [Diagram 3] 1 is a flow chart illustrating a process for providing an individualized treatment protocol according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0025] Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.

[0026] However, the embodiments of the present invention may be modified into various other forms, and the scope of the present invention is not limited to the embodiments described below. The shapes and sizes of elements in the drawings may be exaggerated for clarity, and elements represented by the same reference numerals in the drawings are the same elements.

[0027] Throughout the specification, when a part is described as being "connected" to another part, this includes not only the case where the part is "directly connected" to another part, but also the case where the part is "electrically connected" to another part via another element therebetween. Furthermore, when a part is described as "including" or "comprising" a certain component, this does not mean that the part excludes the other component, but means that the part can further include or comprise the other component, unless otherwise specified.

[0028] Furthermore, terms such as "first" and "second" are used to distinguish one component from another, and should not be used to limit the scope of rights. For example, a first component may be named a second component, and similarly, a second component may be named a first component.

[0029] FIG. 1 is a flow chart illustrating the process of diagnosing and classifying a patient to provide an individualized treatment protocol in an embodiment of the present invention.

[0030] The present invention first inputs information for diagnosis and classification of tinnitus patients.

[0031] The information to be input includes the patient's personal information, tinnitus test results, and medical interview results.

[0032] The patient's personal information includes the patient's age and sex, and may further include accident or surgery history, etc.

[0033] Tinnitus test results are the results tested for a patient's tinnitus condition and include hearing threshold, tinnitus frequency, tinnitus loudness, and tinnitus disability scale.

[0034] The interview results were the result of administering a tinnitus-related questionnaire to the patient, which included items about negative thoughts related to tinnitus and items about negative feelings related to tinnitus.

[0035] The items for negative thoughts related to tinnitus were about bad thoughts that arose from tinnitus, and patients responded to statements such as "I will get dementia because of my tinnitus," "My tinnitus will never go away," and "Tinnitus means I have a serious illness in my head."

[0036] Such items for negative thoughts can be constructed so that the patient responds with a numerical value or frequency indicating how often they think about the subject.

[0037] Items regarding negative feelings related to tinnitus require patients to respond to statements such as "Tinnitus makes me feel anxious," "Tinnitus makes me feel depressed," and "Tinnitus makes me feel uncomfortable."

[0038] Such items for negative emotions can be constructed so that the patient responds with a numerical value or frequency indicating how often they feel that way about the subject matter.

[0039] The patient is then diagnosed and classified based on the information entered.

[0040] In the present invention, the criteria for diagnosing and classifying patients are based on the characteristics of the treatment methods, and for this reason, in the present invention, various tinnitus treatment methods are classified into a functional treatment method group, an emotional treatment method group, and a cognitive treatment method group.

[0041] The functional treatment group consists of methods that alleviate the symptoms of tinnitus or directly treat tinnitus, and includes sound therapy and hearing rehabilitation.

[0042] Sound therapy is a treatment that primarily involves exposing patients with tinnitus to natural sounds, music, or environmental sounds in order to treat the pain caused by tinnitus.

[0043] Hearing rehabilitation is a treatment that aims to reduce the inconvenience caused by tinnitus and hearing loss through methods such as wearing hearing aids, speech recognition training, and improving listening strategies in noisy environments.

[0044] The emotional treatment group is a group of treatments aimed at alleviating or resolving the emotional problems caused by tinnitus, and includes meditation therapy, relaxation therapy, sleep hygiene treatment, mindfulness therapy, and acceptance and commitment therapy (ACT).

[0045] Meditation therapy is a treatment method that reduces stress and stabilizes the mind and body by activating the parasympathetic nervous system through meditation, and is effective in reducing stress caused by tinnitus.

[0046] Relaxation therapy is a treatment method that trains the muscles to reduce tension and stress throughout the body, and has the effect of replenishing the body's energy through relaxation response training. In addition to the central nervous system (brain and spinal cord) and peripheral nervous system, the human body also has an autonomic nervous system, which is divided into the sympathetic nervous system and the parasympathetic nervous system, and these systems are always in balance by working against each other. However, when we are stressed or worry too much, the sympathetic nervous system becomes dominant, causing a "stress response" that speeds up the pulse, increases blood pressure, speeds up breathing, and increases muscle tension. In contrast, when relaxation response training is used, the parasympathetic nervous system becomes dominant, causing the opposite response, i.e., a "relaxation response" that reduces the pulse, lowers blood pressure, reduces breathing rate, and reduces muscle tension. Relaxation therapy has the following effects, which ultimately improve the effectiveness of tinnitus treatment.

[0047] 1. Reduces fatigue and aids in ability to address concerns

[0048] 2. Reduces stress, which can lead to high blood pressure, arteriosclerosis, heart attacks and strokes

[0049] 3. Helps you quit smoking and drinking

[0050] 4. It can be used to relieve insomnia

[0051] 5. Prevent waste of physical energy

[0052] 6. Increased clarity and focus

[0053] Sleep hygiene treatment is a treatment method for improving sleep, and has the effect of eliminating the vicious cycle of insomnia and tinnitus that worsens symptoms when insomnia is experienced due to tinnitus. Sleep hygiene treatment can be carried out by educating patients on the following principles for improving sleep.

[0054] TIFF2025508032000002.tif97132

[0055] Mindfulness therapy is a treatment method that controls secondary reactions to primary stimuli through mindfulness meditation. Usually, we first feel a primary stimulus and then have a secondary reaction to it (primary stimulus → secondary reaction), but mindfulness meditation is a meditation that feels the primary stimulus (sensation, thought, etc.) as it is, but tries to make an effort not to show unnecessary secondary reactions to it (towards only having necessary secondary reactions). In mindfulness meditation, distracting thoughts and pain are embodied when the human brain and body react (secondary reaction) to specific sensations and thoughts (primary stimuli or primary sensations) that arise in the body and mind in a different way from the original way (e.g. reflex (conditioned reflex, unconditioned reflex), behaviorism, formation of classical conditioning). In other words, it means that all things and the world itself are still, but distracting thoughts and pain are the result of humans reacting to them on their own. The famous proverb "Mountains are mountains, water is water" comes from this meaning. Therefore, the aim of this meditation is to continue to observe all physical sensations and thoughts in the body, while being as unresponsive as possible to those sensations, thoughts, and other stimuli.

[0056] Acceptance and commitment therapy is a treatment method that allows therapeutic change to occur when the function, not the form, of personal experience is changed by changing the primary context in which certain types of behavior (thoughts, emotions) influence other forms of behavior (external actions). Rather than trying to suppress or eliminate painful experiences, the treatment strategy of accepting them and making commitment behavior based on personal values ​​helps the treatment to have workability throughout the patient's life.

[0057] The cognitive treatment group is a treatment method for alleviating or eliminating the cognitive problems caused by tinnitus, and includes thought record therapy and educational therapy for cognitive elements.

[0058] Thought record therapy is a treatment method in which situations in which negative feelings about tinnitus occur and the negative thoughts that arise at that time are recorded, and these are then corrected into positive thoughts.

[0059] Educational therapy for cognitive factors is an educational consultation treatment method that corrects negative thoughts about tinnitus into positive ones.

[0060] In the present invention, the characteristics of treatment items are used as a criterion for diagnosing and classifying patients because the majority of tinnitus patients experience both emotional and cognitive problems due to the tinnitus symptoms, and it is to compensate for the fact that sufficient therapeutic effects cannot be obtained when only treating the tinnitus symptoms due to emotional and cognitive problems.

[0061] In the past, functional treatment, emotional treatment, and cognitive treatment were selectively performed depending on the intuition of a counselor or doctor based on their experience, but in the present invention, functional problems, emotional problems, and cognitive problems corresponding to the classification of treatment items are applied as diagnostic and classification criteria for patients in order to objectively assign treatment items suitable for patients. In addition, the process of diagnosing patients based on input information can be performed by a deep learning-based artificial intelligence algorithm.

[0062] In the process of diagnosing a patient, the AI ​​algorithm can derive a "tinnitus symptom scale (functional scale)" from the tinnitus test results, and derive and reflect a "tinnitus-related negative thought scale (cognitive scale)" and a "tinnitus-related negative emotion scale (emotional scale)" from the interview results. The "tinnitus symptom scale (functional scale)" is the result of quantifying the patient's tinnitus symptoms, while the "tinnitus-related negative thought scale (cognitive scale)" and "tinnitus-related negative emotion scale (emotional scale)" are the results of quantifying the answers to the interview items regarding negative thoughts related to tinnitus and negative emotions related to tinnitus.

[0063] If the answers to the items regarding negative thoughts related to tinnitus and the items regarding negative emotions related to tinnitus are received in numerical form, the numerical answers can be used as is or a separate formula can be applied to the numerical answers to derive a scale, and if the answers are not in numerical form, they can be converted into numerical form and used.

[0064] The tinnitus test results for deriving the "scale for tinnitus symptoms (functional scale)" may be applied without limitation to tinnitus test methods developed up to now and the result data therefrom, and technologies developed in the future may also be applied without limitation within the scope that does not impair the characteristics of the present invention. However, a process for correcting or standardizing differences due to the expression method of the result data may be added.

[0065] High values ​​on the functional scale indicate a state in which treatment primarily from the functional treatment group is needed, high values ​​on the cognitive scale indicate a state in which treatment primarily from the cognitive treatment group is needed, and high values ​​on the emotional scale indicate a state in which treatment primarily from the emotional treatment group is needed.

[0066] The AI ​​algorithm for diagnosing a patient may be learned through deep learning by collecting data on tinnitus diagnosis results performed on existing patients as learning data. In this case, the learning data may be tinnitus diagnosis data reflecting functional, cognitive, and emotional scales for each individual patient, or may include tinnitus test results and interview results as well as treatment result data without content for each scale. If there is no data for each scale, functional, cognitive, and emotional scales may be assigned by learning cases with high treatment effects for each patient through treatment result data.

[0067] Through the above process, unlike the conventional method of diagnosing patients based only on their tinnitus symptoms, a diagnosis of a tinnitus patient can be made that reflects not only the patient's tinnitus symptoms but also psychological factors. The diagnosis according to the present invention reflects a diagnosis of psychological factors, and is therefore suitable for providing a treatment protocol that includes not only a functional treatment method group but also an emotional treatment method group and a cognitive treatment method group in an optimized form for each patient.

[0068] In this case, a treatment protocol can be created based on the diagnosis results for each patient as described above, but it is also possible to classify and group patients and then create individualized treatment protocols for each group.

[0069] To group patients, an additional step can be taken to classify patients based on their functional, cognitive and affective measures.

[0070] For example, patients can be classified into three treatment groups, such as a functional treatment group in which functional treatment is required as a priority, an emotional treatment group in which emotional treatment is required as a priority, and a cognitive treatment group in which cognitive treatment is required as a priority. Since such classification is performed using the functional, cognitive, and emotional scales diagnosed for each patient, it is possible to classify patients into a larger number of treatment groups as necessary. The process of classifying patients based on the functional, cognitive, and emotional scales of the patients can be performed by a deep learning-based artificial intelligence algorithm.

[0071] The AI ​​algorithm for classifying patients may be learned through deep learning by collecting data on tinnitus diagnosis and patient classification results previously performed on patients as learning data. In this case, the learning data may include both tinnitus diagnosis data for individual patients and patient classification result data performed by a doctor, or may include tinnitus diagnosis data and treatment result data therefor. If there is no classification result data, the AI ​​algorithm may classify patients by learning cases with high treatment effects for each patient through treatment result data.

[0072] The AI ​​algorithm for diagnosing the functional, cognitive, and emotional scales of a patient and the AI ​​algorithm for classifying patients can learn in a complementary manner. For example, in the process of learning to assign functional, cognitive, and emotional scales to each patient through treatment result data, the AI ​​algorithm can first learn cases in which treatment is highly effective for each patient through the treatment result data and then classify the patients.

[0073] In this way, when a deep learning-based artificial intelligence algorithm diagnoses and classifies a patient based on information composed of the patient's personal information, tinnitus test results, and medical interview results, not only can it diagnose and classify patients more accurately without making mistakes in classification due to missing parts of the information, but it can also diagnose and classify patients more quickly because the diagnosis and classification of patients is performed automatically.

[0074] 2 and 3 are flow charts illustrating a process for providing a personalized treatment protocol according to an embodiment of the present invention.

[0075] The configuration for inputting information for diagnosis and classification related to tinnitus and diagnosing and classifying patients based on the input information is the same as the above-mentioned diagnosis and classification method, so detailed explanation will be omitted.

[0076] After the diagnosis and classification of the patient, a treatment protocol is matched to the classified patient. The treatment protocol is a treatment process consisting of a plurality of treatment methods, and the treatment items included in the treatment protocol are selected from a functional treatment method group, an emotional treatment method group, and a cognitive treatment method group.

[0077] At this time, the treatment items included in the treatment protocol change according to the diagnosis results of each patient or the treatment group to which the patient belongs.

[0078] For example, as described above, when classified into three treatment groups, functional treatment group, emotional treatment group, and cognitive treatment group, the treatment items required for each treatment group are selected preferentially, but this is not limited thereto, and other treatment items may be included in the treatment protocol. In the case of the functional treatment group, the functional treatment method group is mainly included, but the emotional treatment method group and the cognitive treatment method group may be included, while in the case of the emotional treatment group, the emotional treatment method group is mainly included, but the functional treatment method group and the cognitive treatment method group may be included, while in the case of the cognitive treatment group, the cognitive treatment method group is mainly included, but the functional treatment method group and the emotional treatment method group may be included.

[0079] In this way, the process of configuring a treatment protocol depending on the patient classification can be carried out using deep learning-based artificial intelligence algorithms.

[0080] In addition, when dividing into three or more treatment groups, a treatment protocol can be constructed by configuring the number of items included in the three treatment items differently for each treatment group.

[0081] In addition, the treatment protocol matched to each treatment group is not fixed, and the treatment items included in each treatment protocol can be changed to reflect the treatment results. Specifically, the treatment protocol provided for each classification can be changed by reflecting information on the treatment results in the deep learning process of the artificial intelligence algorithm that configures the treatment protocol according to the patient classification.

[0082] When a treatment protocol is constructed based on the diagnosis results for each patient, the treatment is selected from a functional treatment method group, a cognitive treatment method group, and an emotional treatment method group, reflecting the functional scale, the cognitive scale, and the emotional scale. At this time, weights can be assigned to each scale, and the values ​​of each scale can be divided into multiple ranges, and treatment items can be selected according to each range.

[0083] The treatment protocol provided to each patient is not fixed, and the treatment items included in each treatment protocol can be changed to reflect the treatment results. Specifically, the treatment protocol provided to each patient can be changed by reflecting information on the treatment results in the deep learning process of the artificial intelligence algorithm that configures the treatment protocol for each patient.

[0084] The treatment protocol can be changed based on the treatment results, and the related information can also be used to reflect the diagnosis and classification of patients. Specifically, in the deep learning process of the artificial intelligence algorithm that diagnoses and classifies patients, the diagnosis and classification criteria can be changed based on the information on the treatment results.

[0085] According to the above, unlike the conventional method in which the process of reflecting emotional and cognitive factors when applying a treatment method to a patient was dependent on the doctor's intuition, the effectiveness of treatment can be improved by constructing and providing a treatment protocol using an artificial intelligence algorithm.

[0086] Although the present invention has been described above through preferred embodiments, the above-mentioned embodiments are merely illustrative of the technical idea of ​​the present invention, and it will be understood by those skilled in the art that various changes are possible without departing from the technical idea of ​​the present invention. Therefore, the scope of protection of the present invention should be interpreted not by specific embodiments but by the matters described in the claims, and all technical ideas within the scope equivalent thereto should also be interpreted as being included in the scope of the present invention.

Claims

1. An information input step of inputting information including patient information, tinnitus test results, and interview results; and a diagnostic step of deriving a scale for tinnitus symptoms, a scale for negative thoughts related to tinnitus, and a scale for negative emotions related to tinnitus from the information, and diagnosing the patient based on the scale for tinnitus symptoms, the scale for negative thoughts related to tinnitus, and the scale for negative emotions related to tinnitus, The questionnaire included items for negative thoughts related to tinnitus and negative feelings related to tinnitus. A method for diagnosing tinnitus patients, characterized in that the diagnosis step is performed using a deep learning-based artificial intelligence algorithm.

2. The method for diagnosing tinnitus patients according to claim 1 , wherein the scale for tinnitus symptoms is derived based on the results of a tinnitus test.

3. The method for diagnosing a tinnitus patient according to claim 1 , wherein the tinnitus-related negative thought scale and the tinnitus-related negative emotion scale are derived based on the results of a medical interview.

4. An information input step of inputting information including patient information, tinnitus test results, and interview results; a diagnostic step of deriving a scale for tinnitus symptoms, a scale for negative thoughts related to tinnitus, and a scale for negative emotions related to tinnitus from the information, and diagnosing the patient based on the scale for tinnitus symptoms, the scale for negative thoughts related to tinnitus, and the scale for negative emotions related to tinnitus; classifying the patient into a plurality of treatment groups based on diagnostic information for the patient; The questionnaire included items for negative thoughts related to tinnitus and negative feelings related to tinnitus.

13. A method for classifying tinnitus patients, characterized in that the diagnosis and classification steps are performed using a deep learning-based artificial intelligence algorithm.

5. The method for classifying tinnitus patients according to claim 4, wherein the scale for tinnitus symptoms is derived based on the results of a tinnitus test.

6. The method for classifying tinnitus patients according to claim 4, wherein the tinnitus-related negative thought scale and the tinnitus-related negative emotion scale are derived based on the results of a medical interview.

7. 5. The method for classifying tinnitus patients according to claim 4, wherein the treatment groups consist of a functional treatment group, an affective treatment group and a cognitive treatment group.

8. An information input step of inputting information including patient information, tinnitus test results, and interview results; a diagnostic step of deriving a scale for tinnitus symptoms, a scale for negative thoughts related to tinnitus, and a scale for negative emotions related to tinnitus from the information, and diagnosing the patient based on the scale for tinnitus symptoms, the scale for negative thoughts related to tinnitus, and the scale for negative emotions related to tinnitus; A treatment protocol providing step of providing a patient-individualized treatment protocol based on the diagnosis result; The questionnaire included items for negative thoughts related to tinnitus and negative feelings related to tinnitus. The treatment protocol is a treatment process composed of a plurality of treatment methods, and the treatment protocol providing step provides an individualized treatment protocol for a patient by combining treatment methods selected from a functional treatment method group, an emotional treatment method group, and a cognitive treatment method group based on the diagnosis result; A method for providing an individualized treatment protocol for a tinnitus patient, characterized in that the diagnosis step and the treatment protocol providing step are performed using a deep learning-based artificial intelligence algorithm.

9. An information input step of inputting information including patient information, tinnitus test results, and interview results; a diagnostic step of deriving a scale for tinnitus symptoms, a scale for negative thoughts related to tinnitus, and a scale for negative emotions related to tinnitus from the information, and diagnosing the patient based on the scale for tinnitus symptoms, the scale for negative thoughts related to tinnitus, and the scale for negative emotions related to tinnitus; classifying the patient into a plurality of treatment groups based on diagnostic information for the patient; and providing a treatment protocol for providing a patient-individualized treatment protocol based on the classification result. The questionnaire included items for negative thoughts related to tinnitus and negative feelings related to tinnitus. The treatment protocol is a treatment process composed of a plurality of treatment methods, and the treatment protocol providing step provides an individualized treatment protocol for a patient by combining treatment methods selected from a functional treatment method group, an emotional treatment method group, and a cognitive treatment method group based on the classification result; 13. A method for providing an individualized treatment protocol for a tinnitus patient, wherein the diagnosis, classification and treatment protocol providing steps are performed using deep learning-based artificial intelligence algorithms.

10. The method for providing an individualized treatment protocol for a tinnitus patient according to claim 9, wherein the treatment groups consist of a functional treatment group, an emotional treatment group and a cognitive treatment group.

11. The method for providing an individualized treatment protocol for a tinnitus patient according to claim 8 or 9, wherein the scale for tinnitus symptoms is derived reflecting the results of a tinnitus test.

12. The method for providing an individualized treatment protocol for a tinnitus patient according to claim 8 or 9, characterized in that the tinnitus-related negative thinking scale and the tinnitus-related negative emotion scale are derived based on the results of a medical interview.

13. A method for providing an individualized treatment protocol for a tinnitus patient as described in claim 8 or 9, characterized in that the functional treatment method group is an item that alleviates or directly treats tinnitus symptoms, and includes sound therapy and tinnitus retraining therapy.

14. 10. The method for providing an individualized treatment protocol for a tinnitus patient according to claim 8 or 9, characterized in that the group of emotional treatment methods includes meditation therapy, relaxation therapy treatment, sleep hygiene treatment, mindfulness therapy, and acceptance and commitment therapy.

15. The method for providing an individualized treatment protocol for a tinnitus patient according to claim 8 or 9, wherein the cognitive treatment method group includes thought record treatment and educational treatment for cognitive elements.

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