A medical diagnostic system and methodology that identifies rheumatic heart disease using artificial intelligence.

Abstract

The invention relates to a medical diagnostic system and working method that uses artificial intelligence to diagnose rheumatic heart disease (RHD) by displaying ECG measurements taken from three extremities in real time on the screen and determining RHD or a healthy condition after a 10-second measurement; extracting features from the ECG signal to determine the presence of pathology; displaying patient information and diagnostic data on the screen simultaneously with the ECG signal; and storing and transferring the measurements to digital media via USB connection.

Description

1 TARIFF DETECTING RHEUMATIC HEART DISEASE USING ARTIFICIAL INTELLIGENCE A MEDICAL DIAGNOSTIC SYSTEM AND ITS WORKING METHOD The technical field to which the invention relates: The invention provides real-time electrocardiogram (ECG) measurements from three extremities. The image is displayed on the screen and after a 10-second measurement, rheumatic heart disease is detected. Detecting disease (RDD) or a healthy state is done using deep learning-based methods. A convolutional neural network (CNN) algorithm extracts features from ECG signals to identify pathologies. It determines whether or not it is present, and simultaneously displays patient information while the ECG signal is shown. The diagnostic results are also displayed on the screen, and the measurements are stored and 10 The ECG signal, which can be transferred to digital media via USB connection, is a diagnostic tool. RKH is a medical diagnosis determined using artificial intelligence (AI). It relates to the system and its working method. State of the art: Rheumatic heart disease (RHD) is an immune system disorder that usually occurs in young people, around 15 years of age. It is a heart disease originating from the respiratory system. Diagnosis is usually made when symptoms become apparent. that is, the patient's heart sounds are heard or certain signs of heart failure appear. The diagnosis is made upon the onset of symptoms. However, diagnosis is often delayed in this process, and the disease... More serious complications occur in the advanced stages. The current standard diagnosis... Although echocardiography (ECHO) is the method, most patients have an asymptomatic course. 20 Because of this, this test is usually done when the disease has reached advanced stages. Furthermore, the fact that ECHO is not always accessible and requires expertise makes diagnosis difficult. These are other factors that complicate the process. Echocardiography is a commonly used method to assess the structure and function of the heart. Although it is an imaging technique, the diagnostic process is operator-dependent and the technique is 25 This can involve challenges. The results of EKO depend on the practitioner's experience and the equipment. This can vary depending on the quality. This can result in false positives or false negatives. This can lead to undesirable outcomes, thus hindering accurate diagnosis. Furthermore, echocardiography (ECHO) is only performed when significant symptoms or clinical signs appear. 2 Another important factor that makes it difficult to detect the disease in its early stages is... It is an obstacle. At this point, AI-based diagnostic systems can improve the accuracy of RHD diagnosis and early detection. It has the potential to detect disease in this period. AI, especially in big data analysis and It is powerful in pattern recognition. Such systems analyze echocardiography data. 5 by observing subtle changes in heart tissue and early pathological findings more clearly. It can detect with high precision. Furthermore, AI algorithms are operator-independent. This allows for more consistent and objective results. This also helps in the human element of the diagnostic process. It minimizes errors and subjective interpretations. AI-based systems offer a significant advantage, especially in resource-scarce regions. It can be said that echocardiography equipment and specialists are inaccessible. In these places, RKH is used with AI-powered mobile applications or simpler devices. It can be detected in the early stages. For example, portable devices equipped with AI algorithms. In practice, the devices can be integrated into a wider healthcare network, and doctors This enables them to diagnose diseases more quickly and accurately. There are 15 such examples. Although these systems require technological infrastructure, they are an important part of the healthcare system. It has the potential to create equality. In conclusion, "A Method for Identifying Rheumatic Heart Disease Using Artificial Intelligence" Advances in "Medical Diagnostic Systems" have led to improvements in early diagnosis and treatment processes. It can revolutionize things. AI, especially with its operator independence and high accuracy rates, is 20%. With advantages such as low-cost implementation, RKH is growing faster worldwide. and enables accurate detection. However, this technology... Extensive validation studies are needed before it can be used in clinical applications. Regulatory approvals are required. Nevertheless, given its potential, AI's It is expected to play a significant role in future healthcare solutions. 25 Various proposals and applications for medical diagnostic systems at the current state of the art. Although improvements have been made, these improvements are insufficient. Developed for this purpose... Some examples of applications for inventions are given below. Patent application number “KR2560516B1”, which exists in the known state of the art, Using electrocardiogram (ECG) data and body information, 30 diagnoses of heart disease can be made. It relates to a device and method that enables prediction. The application is deep learning. 3 By using a model based on these techniques, and analyzing ECG data and body information, It offers a method for calculating the probability of heart disease. This model uses residuals. It is based on a convolutional neural network (CNN) with a block implemented, and the initial probability second information obtained from the deep learning model configuration automation module By integrating probability information, the combined probability of onset of heart disease is 5. It aims to perform the calculation. Patent application number “CN111329467A”, which exists in the known state of the art, It includes an artificial intelligence-based assisted detection method for heart disease. The method, with data obtained from three-dimensional images such as electrokinetic cardiogram, a training a convolutional neural network (CNN) model, transferring this model to migration learning (transfer 10 (learning), optimizing with techniques such as the Monte Carlo tree search algorithm and specific By combining entropy analyses of heart diseases, a comprehensive diagnostic result is obtained. This application aims to perform dynamic monitoring of continuous cardiac electrical activity. techniques such as signal image processing and combined evaluation of their different outputs By resolving the challenges, automatic detection and accurate diagnosis of heart diseases 15 It ensures that it is placed. Patent application number “CN111261277A”, which exists in the known state of the art, Using electrocardiogram (ECG) data to detect heart diseases It focuses on developing a deep learning-based recognition model. The method, By processing the nonlinear system dynamics and vector data of the ECG, the heart... 20 It aims to produce highly accurate diagnostic results for the disease. The model uses different data. Technical aspects of integrating and processing information from various sources. By overcoming these challenges, it offers automated data extraction and intelligent diagnostic capabilities. In this way, doctors can intervene earlier and provide more precise treatment to patients. By enabling the implementation of these strategies, efficiency in the detection and treatment of heart disease increases by 25%. and the aim is to increase accuracy. In the current state of the technique, AI improves diagnostic accuracy using ECHO. While algorithms exist, they are operator-independent, low-cost, and easy to use. a medical professional who can diagnose RHD using an easily accessible and practical ECG signal. A diagnostic system and working method are needed. 30 4 In conclusion, due to the negative aspects described above and the current solutions, the subject matter... Due to its shortcomings, an improvement is needed in the relevant technical field. It has been made. The purpose of the invention: The main goal of the invention is to diagnose rheumatic heart disease more quickly and accurately. 5 by enabling diagnosis, the treatment process becomes more efficient and patients The aim is to improve the quality of life. Another aim of the invention is to develop an objective diagnostic system that is independent of subjectivity. The goal is to minimize human error. Another aim of the invention is to integrate it into healthcare services, reaching a wider user base. The goal is to ensure that it reaches the target audience. Early detection of rheumatic heart disease. This helps to reduce treatment costs. Another aim of the invention is to use electrocardiogram (ECG) signals to generate artificial intelligence. with the help of a supportive system, the diagnosis of rheumatic heart disease (RCD) and Deep Using learning (CNN) based algorithms, pathological status can be determined from ECG signals. (RKH) aims to enable detection. Another aim of the invention is the real-time display of a single-channel ECG signal and the diagnosis is displayed on the screen after the measurement, and the measurement results are stored in memory. The purpose is to store it and enable digital access via USB connection. Description of the figures: 20 FIGURE 1; Invention that identifies rheumatic heart disease (RHD) using artificial intelligence. This is a drawing showing a front view of a medical diagnostic system. FIGURE 2; Invention that identifies rheumatic heart disease (RHD) using artificial intelligence. This is a drawing showing the rear view of a medical diagnostic system. Reference numbers: 25 1: Electrodes 2: Electrode connection connector 3: Instrument panel 4: Body 5: Button 6: USB connector 5 7: Battery unit Description of the invention: In general terms, the invention will allow for faster and more accurate diagnosis of rheumatic heart disease. by enabling diagnosis, thus making the treatment process more efficient, In general, electrodes (1), electrode connection connector (2), indicator panel (3), housing 10 Rheumatic heart disease including (4), button (5), USB connector (6) and battery unit (7). (RKH) is a medical diagnostic system and study that uses artificial intelligence to determine... It is the method. The invention displays ECG measurements taken from three extremities in real time on a screen. Visualization and measurement after 10 seconds determine whether RKH or a healthy condition is detected. 15 This is done using a deep learning-based convolutional neural network (CNN) algorithm, ECG by extracting features from the signal, it can determine whether there is a pathology, ECG signal. While the images are displayed, patient information and diagnostic results are also shown on the screen. display, storage of measurements and digital transfer via USB connection. 20 It is a diagnostic system that identifies rheumatic heart disease (RHD) using artificial intelligence. The invention aims to diagnose RHD with high accuracy using ECG signals. It includes a medical diagnostic system. It has wireless and digital connectivity features. This system is equipped with a user-friendly design. ECG measurement is performed via the top section. This is done using signals from three extremity derivations, and this data is used as the main 25. It is processed by an artificial intelligence algorithm in the control unit. The system has 12 derivations. It can diagnose RHD with just a single derivation instead of an ECG, which 6 It provides ease of use and speed. Thus, it accelerates diagnostic processes and It makes it more efficient. The invention allows artificial intelligence algorithms to quickly and accurately diagnose ECG signals. This is determined in the artificial intelligence model, especially Convolutional Neural Networks. ECG data is processed using (CNN), and the pathology identification process is successfully completed. 5 This has been accomplished. The layers of the CNN model analyze key features of the ECG signal. by removing and performing normalization and pooling processes, clearer results can be obtained. This enables the rapid diagnosis of cardiovascular diseases such as RKH. It allows them to obtain it. The invention is quite simple and understandable for user interaction. The instrument panel (3) is color 10 and is equipped with a touchscreen, displaying information such as operating status, charging status, and date. The parameters are presented to the user visually. ECG measurement, display. This is done according to the derivation selected from panel (3), and by default DII Derivation is used. The user sees the measurement result displayed on the screen and Patient information can be easily viewed. Also, opening, closing, and taking measurements are 15. The operations can be controlled with a single button (5). The invention enables faster and more accurate diagnosis of RHD in healthcare settings. This diagnostic process, performed via the ECG signal, is particularly important for the device's USB connector. (6) Testing in a wider data pool with data that can be transferred to digital environments via (6) It has been developed in this way. Thanks to this, the device's accessibility for the healthcare sector and its low cost are 20%. Its cost-effectiveness offers a significant advantage by improving diagnostic processes. Electrodes (1) are placed on specific areas of the body to detect the electrical impulses of the heart. conductors that detect these signals and convert them into electrical data, and accurate ECG data is obtained from these devices. They measure heart activity thanks to the surfaces that come into contact with the skin. They are structures that perceive and enable measurement. 25 The electrode connection connector (2) allows the electrodes (1) to be connected to the body (4) and the connection to establish, transmit signals and electrodes (1) to avoid incorrect connections or looseness It is a structure that enables data collection while also providing protection against threats. The instrument panel (3) is colourful, touch-sensitive, to facilitate user interaction and It has a settings menu that allows you to control its functions, date, and charge level. 7 including parameters such as condition and visually displaying ECG measurement results. It is a structure that demonstrates. The body (4) is made of plastic material, its internal components are protected from external factors (moisture, dust It is a structure that protects (etc.). Button (5) has on / off and measurement functions, and when pressed while the system is off, 5 opening it, and when it is open, pressing it starts the measurement process and takes a long time. It is a mechanism that allows the device to be shut down when pressed for a limited time. The USB connector (6) allows connection to digital media, charging and local transferring data stored in memory to a digital environment, transferring it to computers, and performing data analysis. it can connect to systems or other digital platforms and store 10 in the device's memory. exporting stored disease data or measurement results via USB It is the structure that provides. The main control unit diagnoses RKH using an artificial intelligence algorithm (CNN-based model). It is the unit that enables the placement of patients and carries out data processing and diagnostic processes. Battery unit (7) enables its portability and allows it to operate independently by storing energy. It is a power source that makes it possible. A medical diagnostic tool that uses artificial intelligence to identify rheumatic heart disease. The system's operating method includes the following steps:  It is turned on by the user pressing a button,  When switched on, the settings menu on the instrument panel and the basic 20 Displaying parameters (date, battery status, etc.),  Placement of electrodes on the human body,  Indicator for ECG measurement via electrode connection connector Selecting DII derivation as the default via the panel  The user can select DI or DIII 25 from the instrument panel, depending on the usage. Choosing one of its derivations,  Performing a 10-second ECG measurement using the selected derivation,  Transmission of the ECG data, which is the measurement result, to the control unit,  The obtained ECG signal is sent to an artificial intelligence algorithm (CNN). 8  Convolution layers of the artificial intelligence model, normalization, and ECG signal processing with pooling layers,  The artificial intelligence model evaluates the processed ECG data using neural networks,  Using artificial intelligence, ECG data can be categorized into two groups: RHD or healthy. separation, 5  The diagnosis result (RCD or healthy) and the ECG signal are displayed on the instrument panel. displaying patient information along with it  Charging via USB connector and digital transfer of diagnostic results and patient data. transfer of information and data,  Turning it off by the user pressing and holding the button for a long time. 10

Claims

9 REQUESTS 1. A medical diagnostic tool that uses artificial intelligence to identify rheumatic heart disease. It is a system, and its feature is;  Devices placed in specific areas of the body that transmit the electrical impulses of the heart. conductors that detect these signals and convert them into electrical data, and ECG 5 surfaces that come into contact with the skin in order to take accurate measurements on the device thanks to it, it detects and measures heart activity. electrodes (1),  Connecting the electrodes (1) to the body (4) and establishing a connection, transmitting signals and preventing electrodes from being incorrectly connected or loose 10 electrode connector that enables data collection while providing protection against interference. connector (2),  Colorful, touch-sensitive, to facilitate user interaction and a settings menu that allows you to control its functions, Includes parameters such as date and battery status, and displays ECG measurement results on 15 days. instrument panel (3) which also shows visually,  The housing protects the internal components from external factors (4),  Includes on / off and measurement tasks; when pressed while the system is off. opening, and when open, pressing to take measurements Button 20 that enables starting and shutting down when pressed and held. (5),  Connecting to digital devices, charging, and storing in local memory USB connector that enables the transfer of stored data to a digital environment. (6),  Enables portability and allows independent operation by storing energy. 25 The battery unit (7), which is a power source that makes it possible,  Diagnosis of RKH using artificial intelligence algorithm (CNN-based model) data processing and diagnostic processes that enable the identification of the data. It includes the main control unit that performs the operation.

2. Determine the appropriate Rheumatic heart disease for Claim 1 using artificial intelligence. It is a medical diagnostic system, characterized by its body made of plastic material. (4) is included.

3. Using artificial intelligence to identify rheumatic heart disease according to claim 1. It is a medical diagnostic system whose feature is that it protects against external factors such as moisture and dust. It contains a body (4).

4. A medical diagnostic tool that uses artificial intelligence to identify rheumatic heart disease. The working method of the system, its characteristic is; 5  It is turned on by the user pressing a button,  When switched on, the settings menu and basic settings on the instrument panel are displayed. display of parameters,  Placement of electrodes on the human body,  Indicator 10 for performing ECG measurement via electrode connection connector. Selecting DII derivation as the default via the panel  The user can select DI or DIII from the instrument panel, depending on the usage. Choosing one of its derivations,  Performing an ECG measurement using the selected derivation,  Transmission of the ECG data, which is the measurement result, to the control unit, 15  Sending the obtained ECG signal to the artificial intelligence algorithm.  Convolution layers of the artificial intelligence model, normalization, and ECG signal processing with pooling layers,  The artificial intelligence model processes the ECG data using neural networks. evaluation, 20  Using artificial intelligence to separate ECG data into two categories,  The diagnostic result and ECG signal are displayed on the dashboard along with patient information. shown together,  Charging via USB connector and digital transfer of diagnostic results and patient data. transfer of information and data, 25  The process is shut down by the user pressing and holding the button for a long time. It includes the steps.

5. Using artificial intelligence to identify rheumatic heart disease that matches claim 4. It is the working method of a medical diagnostic system, and its characteristic feature is; an indicator when it is switched on. Displaying the settings menu and basic parameters on the control panel 30 The process step includes basic parameters such as date and battery status. 11 6. Using artificial intelligence to identify rheumatic heart disease that meets claim 4. It is the working method of a medical diagnostic system, and its characteristic feature is; Selected derivation. The procedure for taking an ECG measurement involves a 10-second ECG. It includes measurement.

7. 5 that uses artificial intelligence to identify rheumatic heart disease according to claim 4. It is a working method of a medical diagnostic system, characterized by its use of artificial intelligence for ECG. In the process step of separating the data into two categories; Risky Cardiac Disease or It includes two categories that are healthy.

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