Otitis media diagnosis assistance system and method

Abstract

The present invention relates to an otitis media diagnosis assistance system and method for diagnosing a disease through an algorithm on the basis of image data obtained using an otoscopic camera to capture the inside of the ear of a patient to be diagnosed, and, according to the diagnosis result, recommending a hospital located nearby or requesting a reading. The otoscopic camera for generating image data by photographing the inside of the ear of a patient to be diagnosed and an inference engine trained, in order to diagnose the disease of the patient to be diagnosed, with medical data related to a tympanic membrane of a dynamic knowledge base composed of medical data related to the tympanic membrane are used to recognize the tympanic membrane inside the ear of the patient to be diagnosed on the basis of the image data, and otitis media is diagnosed according to an abnormal state of the tympanic membrane.

Description

Otitis media diagnostic assistance system and method

[0001] The present invention relates to a system and method for diagnosing otitis media using an algorithm, and more particularly, to a diagnostic assistance system and method for diagnosing otitis media using an algorithm of an inference engine in image data captured by an otoscope camera, and requesting a diagnosis from a professional diagnosing doctor when the diagnosis result outputs a result other than normal.

[0002] The middle ear refers to the space between the eardrum and the inner ear. Otitis media is a bacterial infection that occurs in the middle ear due to a weakened immune system or infection by bacteria or viruses. It is a common disease in infants and young children with weak immune systems.

[0003] Symptoms of otitis media generally include ear pain, along with frequent occurrences of pus, discharge, tinnitus, hearing loss, and dizziness. In severe cases of otitis media, it can cause hearing loss along with symptoms such as fever, headache, and indigestion.

[0004] Because infants and young children have limitations in describing their symptoms, it is important to treat otitis media early, as it can lead to hearing loss or serious side effects such as developmental disabilities if the timing of treatment is missed.

[0005] Currently, in the field of otolaryngology, a technology has been developed to evaluate the eardrum and diagnose otitis media using image data analysis technology utilizing artificial intelligence, allowing for the diagnosis of diseases with relatively high accuracy.

[0006] As a conventional technique for diagnosing otitis media, Patent Publication No. 10-2021-0121119 and Patent Registration No. 10-2595647 are known.

[0007] Patent Publication No. 10-2021-0121119 discloses a technique for determining a parameter set from one or more data sets related to an eardrum from a query system and outputting a classification of the eardrum based on at least a classifier model derived from the parameter set.

[0008] In addition, Patent Publication No. 10-2595647 discloses a technology for generating big data by collecting patient information, tympanic membrane images, and hearing value information, extracting an tympanic membrane model from the tympanic membrane images, and then learning hearing disease and hearing value information corresponding to the tympanic membrane model to derive a predicted hearing value from the tympanic membrane images.

[0009] According to the prior art disclosed in Patent Publication No. 10-2021-0121119 and Patent Registration No. 10-2595647, a technology is proposed to analyze tympanic membrane image data to derive diagnostic results on the hearing and tympanic membrane condition of a subject for diagnosis. However, since early treatment is important for otitis media, it is difficult to quickly and easily diagnose the disease even in homes with underdeveloped medical facilities.

[0010] Therefore, when building a disease diagnosis assistance system, if an algorithm diagnoses a disease based on a dynamic knowledge base composed of medical data related to the disease, and if an abnormality is found based on the diagnosis results, the disease diagnosis assistance system recommends a hospital to the user or requests a non-face-to-face treatment, a more convenient and rapid disease diagnosis assistance system can be built.

[0011] [Patent Document]

[0012] Patent Publication No. 10-2021-0121119 (Published: October 7, 2021, Title: Machine Learning for Otitis Media Diagnosis)

[0013] Patent Publication No. 10-2595647 (Publication Date: October 30, 2023, Title: Deep Learning-Based Tympanic Drum Image Analysis and Hearing Value Prediction System)

[0014] The technical problem of the present invention is to provide an otitis media diagnosis assistance system that diagnoses otitis media by analyzing image data taken of the inside of the ear with an otoscope camera through an algorithm that has learned medical data related to the eardrum and determining an abnormality of the eardrum.

[0015] Furthermore, another object of the present invention is to provide an otitis media diagnosis assistance system and method that analyzes image data captured inside the ear with an otoscope camera using an algorithm to diagnose a disease according to an abnormality in the eardrum of a patient to be diagnosed, recommends a nearby hospital or requests treatment at a hospital remotely when a non-normal diagnosis result is output, and transmits the diagnosis result and image data to a central server of the hospital so that a specialist can interpret them when a request for treatment is made.

[0016] In order to solve such technical problems, the otitis media diagnosis assistance system according to the present invention may include an otoscope camera that generates image data by photographing the inside of the ear of a patient to be diagnosed, a dynamic knowledge base composed of medical data related to the eardrum to diagnose a disease of the patient to be diagnosed, and an inference engine that learns the medical data related to the eardrum of the dynamic knowledge base to recognize the eardrum inside the ear of the patient to be diagnosed based on the image data and diagnose a disease according to an abnormal state of the eardrum, and a medical assistance unit that recommends hospitals in the order of proximity from the location of the patient to be diagnosed in the case of using the otitis media diagnosis assistance system for personal use based on the diagnosis result of the otitis media diagnosis unit, or requests a remote reading in the case of using the otitis media diagnosis assistance system for medical staff, a medical request confirmation unit that confirms whether the patient to be diagnosed has made a medical appointment at the recommended hospital or confirms whether the reading request has been made, and a patient data communication unit that transmits the diagnosis result of the patient to be diagnosed and the image data to a hospital server when the medical request confirmation unit confirms the medical appointment or reading request.

[0017] The above otitis media diagnosis assistance system may further include a data output unit that outputs the image data captured by the otoscope camera to a user device of the otitis media diagnosis assistance system via wireless and wired communication including a USB (Universal Serial Bus) cable, WIFI (Wireless Fidelity), Bluetooth, and NFC so that the image data can be checked in real time.

[0018] The above dynamic knowledge base can be updated with additional data from existing data by at least one medical professional.

[0019] The above otitis media diagnosis unit may perform a self-diagnosis process when using the personal otitis media diagnosis assistance system, and the otitis media diagnosis unit may output either normal or treatment request as a diagnosis result depending on the abnormal state of the eardrum using the inference engine.

[0020] The above otitis media diagnosis unit, when using the otitis media diagnosis assistance system for medical staff, performs a hospital diagnosis process, and the otitis media diagnosis unit can output at least one of normal, acute otitis media, otitis media with effusion, and chronic otitis media as the diagnosis result according to the abnormal state of the eardrum using the inference engine.

[0021] The above inference engine can diagnose the laryngeal disease of the patient to be diagnosed using a CNN (Convolutional Neural Network) or Transformer model.

[0022] The above-mentioned medical assistance unit, when a medical treatment request is output as the diagnosis result of the self-diagnosis process in the otitis media diagnosis unit, recommends a hospital in order of proximity based on the location of the patient to be diagnosed, or, when at least one of acute otitis media, effusion otitis media, and chronic otitis media is output as the diagnosis result of the hospital diagnosis process in the otitis media diagnosis unit, can remotely request an interpretation by receiving input of whether the medical staff wishes to request an interpretation.

[0023] The above-mentioned medical assistance unit can recommend at least one hospital in order of proximity to the patient to be diagnosed by referring to the GPS (Global Positioning System) information of the patient to be diagnosed.

[0024] The above patient data communication unit can receive the reading results from the hospital server and output them to the user device through the data output unit.

[0025] Meanwhile, a method for assisting in the diagnosis of otitis media according to another embodiment of the present invention,

[0026] The method comprises the steps of: generating image data by photographing the inside of the ear of a patient to be diagnosed with an otoscope camera; configuring a dynamic knowledge base with medical data related to the eardrum to diagnose a disease of the patient to be diagnosed; recognizing the eardrum inside the ear of the patient to be diagnosed based on the image data using an inference engine of an otitis media diagnosis unit that has learned the medical data related to the eardrum of the dynamic knowledge base, and diagnosing a disease according to an abnormal state of the eardrum; recommending a hospital in the order of proximity from the location of the patient to be diagnosed through a medical assistance unit in the case of using the otitis media diagnosis assistance system for personal use based on the diagnosis result of the otitis media diagnosis unit, or remotely requesting a reading through the medical assistance unit in the case of using the otitis media diagnosis assistance system for medical staff; confirming whether the patient to be diagnosed has a treatment appointment at the recommended hospital through a medical assistance confirmation unit or confirming whether the reading request has been made through a medical assistance confirmation unit; and, when the medical assistance confirmation unit confirms the treatment appointment or reading request, transmitting the diagnosis result and the image data of the patient to be diagnosed from a patient data communication unit to a hospital server; and receiving the reading result from the patient data communication unit and providing it to a user through a data output unit from the hospital server. Can be.

[0027] The above dynamic knowledge base can be updated with additional data from existing data by at least one medical professional.

[0028] The above inference engine can diagnose the laryngeal disease of the patient to be diagnosed using a CNN (Convolutional Neural Network) or Transformer model.

[0029] The process of diagnosing a disease of the patient to be diagnosed using the inference engine of the otitis media diagnosis unit may include a step of extracting the medical data related to the eardrum from the dynamic knowledge base, a step of configuring the medical data related to the eardrum as learning data, and a step of training the inference engine based on the learning data, and a step of analyzing image data captured by the otoscope camera using the inference engine that has trained the medical data to recognize the eardrum, and diagnosing the disease of the patient to be diagnosed based on the abnormal state of the eardrum and outputting the diagnosis result.

[0030] The above otitis media diagnosis unit may perform a self-diagnosis process when using the personal otitis media diagnosis assistance system, and the otitis media diagnosis unit may output either normal or treatment request as a diagnosis result depending on the abnormal state of the eardrum using the inference engine.

[0031] The above otitis media diagnosis unit, when using the otitis media diagnosis assistance system for medical staff, performs a hospital diagnosis process, and the otitis media diagnosis unit can output at least one of normal, acute otitis media, otitis media with effusion, and chronic otitis media as the diagnosis result according to the abnormal state of the eardrum using the inference engine.

[0032] The above-mentioned medical assistance unit, when a medical treatment request is output as the diagnosis result of the self-diagnosis process in the otitis media diagnosis unit, recommends a hospital in order of proximity based on the location of the patient to be diagnosed, or, when at least one of acute otitis media, effusion otitis media, and chronic otitis media is output as the diagnosis result of the hospital diagnosis process in the otitis media diagnosis unit, can remotely request an interpretation by receiving input of whether the medical staff wishes to request an interpretation.

[0033] The above-mentioned medical assistance unit can recommend at least one hospital in order of proximity to the patient to be diagnosed by referring to the GPS (Global Positioning System) information of the patient to be diagnosed.

[0034] Specific details of other embodiments are included in the detailed description and drawings.

[0035] According to the technical problem solving means described above, the otitis media diagnosis assistance system and method according to the embodiment of the present invention can diagnose otitis media by analyzing image data captured by an otoscope camera using an inference engine that has learned medical data related to the eardrum, and thus has the following advantages.

[0036] First, the otitis media diagnosis assistance system and method can contribute to the revitalization of local hospitals by actively recommending hospital visits when abnormalities in the eardrum are discovered by judging image data through an algorithm.

[0037] Second, the otitis media diagnosis assistance system and method can assist otolaryngologists in providing quick and accurate treatment by preemptively examining patients using an otoscope camera and a mobile mobile device in areas with low medical accessibility and transmitting the examination results and image data to the hospital.

[0038] Third, the otitis media diagnosis assistance system and method can directly check and photograph the inside of the ear in real time through the screen of a smart terminal connected wirelessly and wired to an otoscope camera.

[0039] Figure 1 illustrates an otitis media diagnosis assistance system according to an embodiment of the present invention.

[0040] Figure 2 illustrates the diagnostic process of the otitis media diagnostic assistance system.

[0041] Figure 3 shows an otoscope camera of an otitis media diagnosis assistance system.

[0042] Figure 4 illustrates providing an image of the inside of the ear of a patient to be diagnosed using an otoscopic camera through a user device.

[0043] Figure 5 illustrates a process for self-diagnosis and a process for hospital use.

[0044] Figure 6 illustrates a method for assisting in the diagnosis of otitis media.

[0045] The following detailed description of the present invention refers to the accompanying drawings, which illustrate specific embodiments in which the present invention may be practiced, in order to clarify the objects, techniques, solutions, and advantages of the present invention. These embodiments are described in sufficient detail to enable those skilled in the art to practice the present invention. Furthermore, throughout the detailed description and claims, the word "comprise" and variations thereof are not intended to exclude other technical features, additions, components, or steps. Other objects, advantages, and features of the present invention will become apparent to those skilled in the art, in part from this description, and in part from practice of the present invention. The examples and drawings below are provided by way of illustration and are not intended to limit the present invention. Moreover, the present invention encompasses all possible combinations of the embodiments set forth herein. It should be understood that the various embodiments of the present invention, while different from one another, are not necessarily mutually exclusive. It should also be understood that the positions or arrangements of individual components within each disclosed embodiment may be varied without departing from the spirit and scope of the present invention. Accordingly, the detailed description set forth below is not intended to be limiting, and the scope of the present invention is defined solely by the appended claims, along with the full scope equivalent to which such claims are entitled, if properly described. Furthermore, unless otherwise indicated herein or clearly contradicted by context, items referred to in the singular encompass the plural unless the context otherwise requires. Furthermore, in describing the present invention, if a detailed description of a related known structure or function is determined to obscure the gist of the present invention, the detailed description will be omitted.

[0046] FIG. 1 illustrates an otitis media diagnosis assistance system according to an embodiment of the present invention, FIG. 2 illustrates a diagnosis process of the otitis media diagnosis assistance system, FIG. 3 illustrates an otoscope camera of the otitis media diagnosis assistance system, and FIG. 4 illustrates providing an image of the inside of the ear of a patient to be diagnosed by taking a picture with the otoscope camera through a user device.

[0047] Referring to FIGS. 1 to 4, the otitis media diagnosis assistance system (100) includes an otoscope camera (110), a dynamic knowledge base (120), an otitis media diagnosis unit (130), a treatment assistance unit (140), a treatment request confirmation unit (150), a patient data communication unit (160), a data output unit (170), and a control unit (170).

[0048] The otoscope camera (110) can capture images of the inside of the ear of a patient to be diagnosed and generate image data.

[0049] According to an embodiment of the present invention, as can be seen in FIG. 3, the optical camera (110) uses a CMOS (Complementary Metal-Oxide-Semiconductor) sensor as the image sensor and is configured using a VARI FOCAL LENS as the lens, but is not limited thereto and can be configured using various image sensors and lenses.

[0050] The dynamic knowledge base (120) may be composed of medical data related to the eardrum to diagnose a disease of a patient to be diagnosed.

[0051] According to an embodiment of the present invention, the dynamic knowledge base (120) can reduce errors in disease diagnosis by updating data added to existing data through at least one medical staff member.

[0052] The otitis media diagnosis unit (130) can recognize the eardrum inside the ear of a patient to be diagnosed based on image data using an inference engine (131) that has learned medical data (20) related to the eardrum in the dynamic knowledge base (120), and can diagnose a disease based on the abnormal condition of the eardrum.

[0053] A detailed description of the otitis media diagnosis unit (130) will be provided using Fig. 5.

[0054] The inference engine (131) of the otitis media diagnosis unit (130) learns medical data (20) related to the eardrum using a CNN (Convolutional Neural Network) or a Transformer model, and the learned inference engine (131) can diagnose a disease by analyzing image data taken of the inside of the ear of a patient to be diagnosed, but is not limited thereto, and according to an embodiment of the present invention, the inference engine (131) can learn medical data (20) related to the eardrum and diagnose a disease of a patient to be diagnosed using at least one or more of various algorithms such as linear regression, logistic regression, decision tree algorithm, support vector machine, naive Bayes, K-nearest neighbor, random forest algorithm, ensemble, and recurrent neural network.

[0055] The medical assistant unit (140) can recommend hospitals in order of proximity from the location of the patient to be diagnosed when using the otitis media diagnosis assistant system (100) for personal use based on the diagnosis results of the otitis media diagnosis unit (130), or can request a remote diagnosis when using the otitis media diagnosis assistant system (100) for medical staff.

[0056] According to an embodiment of the present invention, the medical assistant unit (140) can recommend at least one hospital in order of proximity to the patient to be diagnosed by referring to the GPS (Global Positioning System) information of the patient to be diagnosed, and a detailed description of the medical assistant unit (140) will be described using FIG. 5.

[0057] The medical treatment request confirmation department (150) can confirm whether a patient to be diagnosed has made a reservation for treatment at a hospital recommended by the medical treatment assistance department (140) or whether a reading request has been made.

[0058] When the patient data communication unit (160) confirms a treatment reservation or reading request in the treatment request confirmation unit (150), it can transmit the diagnosis results and image data of the patient to be diagnosed to the hospital server (30).

[0059] As can be seen in Fig. 4, the data output unit (160) can output image data captured by the optical camera to a user device (10) via wireless and wired communication including a USB (Universal Serial Bus) cable, WIFI (Wireless Fidelity), Bluetooth, and NFC so that the data can be checked in real time.

[0060] The patient data communication unit (160) can receive the reading results from the hospital server (30) and output them to the user device (10) through the data output unit (170).

[0061] The control unit (180) generally corresponds to a server, and the control unit (180) can perform overall control of the otoscope camera (110), dynamic knowledge base (120), otitis media diagnosis unit (130), medical assistance unit (140), medical request confirmation unit (150), patient data communication unit (160), and data output unit (170).

[0062] Figure 5 illustrates a process for self-diagnosis and a process for hospital use.

[0063] Referring to FIG. 5, the otitis media diagnosis unit (130) and the medical assistance unit (140) can use a self-diagnosis process when the otitis media diagnosis assistance system (100) is used for personal use, and can use a hospital process when the otitis media diagnosis assistance system (100) is used for medical staff.

[0064] When the otitis media diagnosis unit (130) uses the otitis media diagnosis assistance system (100) for personal use, the self-diagnosis process is performed, and the otitis media diagnosis unit (130) can output either normal or treatment request as a diagnosis result depending on the abnormal condition of the eardrum.

[0065] When the otitis media diagnosis unit (130) outputs a treatment request as a diagnosis result of the self-diagnosis process, the treatment assistance unit (140) can recommend a nearby hospital based on the location of the patient to be diagnosed.

[0066] According to an embodiment of the present invention, the medical assistant (140) can recommend at least one hospital in order of proximity to the patient to be diagnosed by referring to the GPS (Global Positioning System) information of the patient to be diagnosed.

[0067] When the otitis media diagnosis unit (130) uses the otitis media diagnosis assistance system (100) for medical staff, it performs a hospital diagnosis process, and the otitis media diagnosis unit (130) can output at least one of the following diagnosis results: normal, acute otitis media, exudative otitis media, and chronic otitis media, depending on the abnormal condition of the eardrum.

[0068] When at least one of acute otitis media, otitis media with effusion, and chronic otitis media is output as a diagnosis result of the hospital diagnosis process in the otitis media diagnosis unit (130), a request for interpretation by medical staff can be input and interpretation can be requested remotely.

[0069] Figure 6 illustrates a method for assisting in the diagnosis of otitis media.

[0070] Referring to FIG. 6, the method for assisting in the diagnosis of otitis media can diagnose a disease based on image data of the inside of the ear of a patient to be diagnosed captured by an otoscope camera (110), and based on the diagnosis results, can recommend a hospital in order of proximity from the location of the patient to be diagnosed or request an interpretation.

[0071] The diagnostic aid method for otitis media can be as follows.

[0072] The method for assisting in the diagnosis of otitis media proceeds with a step of generating image data by photographing the inside of the ear of a patient to be diagnosed using an otoscope camera (110) (S100).

[0073] The method for assisting in the diagnosis of otitis media proceeds with a step of configuring a dynamic knowledge base (120) with medical data (20) related to the eardrum to diagnose the disease of the patient to be diagnosed (S110).

[0074] The otitis media diagnosis assistance method uses the inference engine (131) of the otitis media diagnosis unit (130) that has learned medical data (20) related to the eardrum of the dynamic knowledge base (120) to recognize the eardrum inside the ear of the patient to be diagnosed based on image data, and the otitis media diagnosis unit (130) proceeds with a step of diagnosing a disease according to the abnormal state of the eardrum (S120).

[0075] At this time, the process of diagnosing the disease of the patient to be diagnosed using the inference engine (131) using the CNN (Convolutional Neural Network) or Transformer model of the otitis media diagnosis unit (130) is as follows.

[0076] The otitis media diagnosis unit (130) proceeds with the step of extracting medical data related to the eardrum from the dynamic knowledge base.

[0077] The otitis media diagnosis unit (130) configures medical data related to the eardrum as learning data and proceeds with a step of training the inference engine (131) based on the learning data.

[0078] The otitis media diagnosis unit (130) analyzes image data captured by an otoscope camera (110) using an inference engine (131) that has learned medical data (20) to recognize the eardrum, diagnoses the disease of the patient to be diagnosed based on the abnormal condition of the eardrum, and outputs the diagnosis result, and the otitis media diagnosis unit (130) completes the process of diagnosing the disease of the patient to be diagnosed using the inference engine (131).

[0079] Here, the otitis media diagnosis unit (130) performs a self-diagnosis process when using the otitis media diagnosis assistance system (100) for personal use, and the otitis media diagnosis unit (130) can output either a normal diagnosis result or a treatment request result depending on the abnormal condition of the eardrum using the inference engine (131).

[0080] Alternatively, the otitis media diagnosis unit (130) may perform a hospital diagnosis process when using the otitis media diagnosis assistance system (100) for medical staff, and the otitis media diagnosis unit (130) may output at least one of normal, acute otitis media, exudative otitis media, and chronic otitis media as a diagnosis result depending on the abnormal condition of the eardrum using the inference engine (131).

[0081] In the method for assisting the diagnosis of otitis media, when the otitis media diagnosis assistance system (100) is used for personal use according to the diagnosis result of the otitis media diagnosis unit (130), the medical assistant unit (140) may proceed with a step of recommending hospitals in the order of proximity from the location of the patient to be diagnosed (S130-1), or in the method for assisting the diagnosis of otitis media, when the otitis media diagnosis assistance system (100) is used for medical staff, the medical assistant unit (140) may proceed with a step of requesting a reading remotely (S130-2).

[0082] Here, when a treatment request is output as a diagnosis result of a self-diagnosis process in the otitis media diagnosis unit (130), the treatment request unit (140) can recommend at least one hospital in order of proximity to the patient to be diagnosed based on the location of the patient to be diagnosed by referring to GPS (Global Positioning System) information.

[0083] Alternatively, when the diagnosis result of the hospital diagnosis process in the otitis media diagnosis unit (130) is one of acute otitis media, otitis media with effusion, and chronic otitis media, the medical referral unit (140) can remotely request a diagnosis by receiving input from the medical staff whether they wish to request a diagnosis.

[0084] The method for assisting in the diagnosis of otitis media proceeds with a step of transmitting the diagnosis results and image data of the patient to be diagnosed from the patient data communication unit (160) to the hospital server (30) when the appointment for diagnosis is confirmed in the treatment request confirmation unit (150) (140-1), or, when the interpretation request is confirmed, a step of transmitting the diagnosis results and image data of the patient to be diagnosed from the patient data communication unit (160) to the hospital server (30) is performed (S140-2).

[0085] The otitis media diagnosis assistance method proceeds with a step of receiving the reading results from the patient data communication unit (160) in the hospital server (30) and providing them to the user through the data output unit (170) (S150), and the otitis media diagnosis assistance system (100) completes the otitis media diagnosis assistance method that can diagnose the disease of the patient to be diagnosed.

[0086] While the present invention has been described with reference to the embodiments illustrated in the drawings, these are merely exemplary, and those skilled in the art will understand that various modifications and equivalent embodiments are possible. For example, while the present invention has been described based on the assumption of a system for diagnosing otitis media, the present invention can be equally applied to all cases where ear-related diseases can be diagnosed by learning medical data without any particular modification. Therefore, the true technical protection scope of the present invention should be defined solely by the appended claims.

[0087] [Explanation of symbols]

[0088] 100: Otitis Media Diagnosis Assistance System

[0089] 110: Lee Kyung Camera

[0090] 120: Dynamic Knowledge Base

[0091] 130: Otitis media diagnosis department

[0092] 131: Inference Engine

[0093] 140: Medical Assistant Department

[0094] 150: Medical Request Confirmation Form

[0095] 160: Patient Data Communication Department

[0096] 170: Data output section

[0097] 180: Control unit

Claims

1. An otoscope camera that captures images of the inside of the patient's ear to generate image data; A dynamic knowledge base consisting of medical data related to the tympanic membrane for diagnosing diseases of the above-mentioned patients; An otitis media diagnosis unit that recognizes the tympanic membrane inside the ear of the patient to be diagnosed based on the image data using an inference engine that has learned medical data related to the tympanic membrane of the dynamic knowledge base, and diagnoses a disease according to the abnormal condition of the tympanic membrane; A medical assistant unit that recommends hospitals in order of proximity from the location of the patient to be diagnosed when using the otitis media diagnosis assistant system for personal use based on the diagnosis results of the otitis media diagnosis unit, or requests a remote interpretation when using the otitis media diagnosis assistant system for medical staff; A medical referral confirmation department that confirms whether the patient subject to the above diagnosis has made an appointment for treatment at the above-mentioned recommended hospital or whether the above-mentioned interpretation request has been made; and An otitis media diagnosis assistance system, comprising: a patient data communication unit that transmits the diagnosis results and image data of the patient to be diagnosed to a hospital server when the above-mentioned treatment reservation or interpretation request is confirmed in the above-mentioned treatment request confirmation unit.

2. In paragraph 1, The above otitis media diagnosis assistance system further includes a data output unit that outputs the image data captured by the otoscope camera to a user device of the above otitis media diagnosis assistance system via wireless and wired communication including a USB (Universal Serial Bus) cable, WIFI (Wireless Fidelity), Bluetooth, and NFC so that the image data can be checked in real time.

3. In paragraph 1, The above dynamic knowledge base is an otitis media diagnosis assistance system that updates data added to existing data through at least one medical professional.

4. In paragraph 1, The above otitis media diagnosis section is, An otitis media diagnosis assistance system that, when using the above personal otitis media diagnosis assistance system, performs a self-diagnosis process, and the otitis media diagnosis unit outputs one of the diagnosis results as normal or a treatment request based on the abnormal condition of the tympanic membrane by using the inference engine.

5. In paragraph 1, The above otitis media diagnosis section is, An otitis media diagnosis assistance system that, when using the above otitis media diagnosis assistance system for medical staff, performs a hospital diagnosis process, and the otitis media diagnosis unit outputs at least one of normal, acute otitis media, otitis media with effusion, and chronic otitis media as a diagnosis result according to the abnormal state of the tympanic membrane using the above inference engine.

6. In paragraph 1, The above inference engine is an otitis media diagnosis assistance system that diagnoses the disease of the patient to be diagnosed using a CNN (Convolutional Neural Network) or Transformer model.

7. In paragraph 1, The above medical assistance department, When a request for treatment is output based on the above diagnosis result of the above self-diagnosis process in the above otitis media diagnosis department, hospitals are recommended in order of proximity based on the location of the patient to be diagnosed, or An otitis media diagnosis assistance system that, when at least one of acute otitis media, otitis media with effusion, and chronic otitis media is output as the diagnosis result of the hospital diagnosis process in the above otitis media diagnosis department, receives an input of whether the medical staff wishes to request an interpretation and remotely requests an interpretation.

8. In paragraph 7, The above-mentioned diagnosis assistance system is an otitis media diagnosis assistance system that recommends at least one hospital in order of proximity to the patient to be diagnosed by referring to the GPS (Global Positioning System) information of the patient to be diagnosed.

9. In paragraph 2, The above patient data communication unit is an otitis media diagnosis assistance system that receives the reading results from the hospital server and outputs them to the user device through the data output unit.

10. A step of generating image data by photographing the inside of the ear of a patient to be diagnosed using an otolaryngeal camera; A step of constructing a dynamic knowledge base with medical data related to the eardrum to diagnose a disease of the above-mentioned patient; A step of recognizing the eardrum inside the ear of the patient to be diagnosed based on the image data by using the inference engine of the otitis media diagnosis unit that has learned medical data related to the eardrum of the above dynamic knowledge base, and diagnosing a disease according to the abnormal condition of the eardrum; A step of recommending a hospital in order of proximity from the location of the patient to be diagnosed through the medical assistance unit when using the otitis media diagnosis assistance system for personal use based on the diagnosis result of the otitis media diagnosis unit, or requesting a remote interpretation through the medical assistance unit when using the otitis media diagnosis assistance system for medical staff; A step of confirming whether the patient subject to the above diagnosis has made a reservation for treatment at the above-mentioned recommended hospital using the treatment request confirmation form or confirming whether the above-mentioned interpretation request has been made using the treatment request confirmation form; When the above treatment reservation or interpretation request is confirmed in the above treatment request confirmation section, a step of transmitting the diagnosis result and the image data of the patient to be diagnosed from the patient data communication section to the hospital server; and A method for assisting in the diagnosis of otitis media, comprising: receiving a reading result from the patient data communication unit at the hospital server and providing the reading result to the user through a data output unit.

11. In Article 10, The above dynamic knowledge base is an auxiliary method for diagnosing otitis media, which updates data added to existing data through at least one medical professional.

12. In paragraph 10, The above inference engine is an assistive method for diagnosing otitis media by using a CNN (Convolutional Neural Network) or Transformer model to diagnose the above disease of the patient to be diagnosed.

13. In paragraph 10, The process of diagnosing the disease of the patient to be diagnosed using the above inference engine of the above otitis media diagnosis department is as follows: A step of extracting the medical data related to the eardrum from the above dynamic knowledge base; A step of configuring the medical data related to the eardrum as learning data and training the inference engine based on the learning data; and A method for assisting in the diagnosis of otitis media, comprising: a step of analyzing image data captured by the otoscopic camera using the inference engine that has learned the medical data to recognize the eardrum, diagnosing a disease of the patient to be diagnosed based on an abnormal condition of the eardrum, and outputting the diagnosis result.

14. In paragraph 10, The above otitis media diagnosis section is, A method for diagnosing otitis media using the above personal otitis media diagnosis assistance system, wherein a self-diagnosis process is performed, and the otitis media diagnosis unit outputs one of the diagnosis results as normal or a treatment request based on the abnormal state of the tympanic membrane using the inference engine.

15. In paragraph 10, The above otitis media diagnosis section is, A method for assisting in the diagnosis of otitis media, wherein, when the above otitis media diagnosis assistance system is used for the medical staff, a hospital diagnosis process is performed, and the otitis media diagnosis unit outputs at least one of normal, acute otitis media, effusion otitis media, and chronic otitis media as a diagnosis result according to the abnormal state of the tympanic membrane using the inference engine.

16. In paragraph 10, The above medical assistance department, When a request for treatment is output based on the above diagnosis result of the above self-diagnosis process in the above otitis media diagnosis department, hospitals are recommended in order of proximity based on the location of the patient to be diagnosed, or An otitis media diagnosis assistance method for remotely requesting interpretation by inputting whether the medical staff wishes to request interpretation when at least one of acute otitis media, otitis media with effusion, and chronic otitis media is output as the diagnosis result of the hospital diagnosis process in the otitis media diagnosis section.

17. In paragraph 10, The above-mentioned diagnosis assistance method for otitis media refers to the GPS (Global Positioning System) information of the above-mentioned diagnosis subject patient and recommends at least one hospital in order of proximity to the above-mentioned diagnosis subject patient.

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