An artificial intelligence system for diagnosing coronary artery stenosis from myocardial artery scans using Spect and Spect / Ct imaging techniques.

Abstract

OCR 09WP 12 / 01 / 2569 An artificial intelligence system for diagnosing coronary artery stenosis from imaging. Cardiac arterial blood flow scans are performed using Spect and Spect / Ct machines. Features include a cardiac ischemia imaging device (1000). It includes a gamma-ray imaging device (Spect) which scans images to examine blood supply conditions. Cardiac muscle, which is the first piece of information, is used for diagnostic imaging of the blood vessels. Cardiac blood in nuclear medicine for diagnostic purposes and functional diagnostic scanning. Cardiac spectroscopy (Spect / Ct) is used to create images for diagnosing the function of coronary arteries. Heart blood, which is the second data point, along with the first and second data points, were imported through the data input section. It consists of a set of computer program instructions, the first (2000), which serves as a transmission part. Data One and Data Two, Computer Program Instruction Set One (2000) connected to The second set of computer program instructions (4000) serves to support the transmission of the first data and data. The second set of instructions is sent to the second computer program (4000) to perform reading and analysis. Data one and data two are used to predict blood vessel stenosis using a programmed set of instructions. The second computer (4000) creates an image model (Rmpi) from the first data and the data from the first. Secondly, match the image model (Rmpi) with the model for predicting vascular stenosis. (4400) which generates predictive data on blood vessel stenosis in advance and displays the results as Polar Map

Claims

OCR 09WP 12 / 01 / 2569 1. An artificial intelligence system for diagnosing coronary artery stenosis from imaging. Cardiac arterial blood flow scans are performed using Spect and Spect / Ct machines. Characteristics that include: Cardiac ischemic imaging device (1000) consisting of -A gamma radiation imaging device (SPECT) scans images to examine blood supply conditions. The heart muscle is the first piece of information. - Cardiovascular imaging equipment for diagnostic testing in medicine. Nuclear technology for diagnostic purposes and scanning to assess cardiovascular function. (Spect / Ct) functions to create images for diagnosing the function of coronary arteries. Second piece of information Data one and data two are imported through the integrated data input section. The first computer program instruction set (2000) serves as the first data transmission part, and Second data, Computer Program Instruction Set One (2000) connected to Program Instruction Set. The second computer (4000) acts as a receiver for the transmission of data from the first and second computers to The second set of computer program instructions (4000) to read and analyze the first data. And the second piece of information is to predict the narrowing of blood vessels. The second set of computer program instructions (4000) is responsible for creating the image model (Rmpi) from Data one and data two are used to match the image model (RMPI) with the prediction model. The vascular stenosis (4400) project provides predictive data on vascular stenosis. It is generated in advance and displayed as a polar map.

2. An artificial intelligence system for diagnosing coronary artery stenosis from imaging. Cardiac ischemia scan using a Spect (Spect) and Spect / Ct (Spect / Ct) machine. Under claim 1, where the predictive model for vascular stenosis (4400) generates data Predicting blood vessel stenosis in advance, categorized by type of stenosis. At least one or more characteristics of coronary artery stenosis are present in the heart from the model. The image (Rmpi) is obtained from the second set of computer program instructions (4000).

3. An artificial intelligence system for diagnosing coronary artery stenosis from imaging. Cardiac ischemia scan using a Spect (Spect) and Spect / Ct (Spect / Ct) machine. Under claim 1, where the second set of computer program instructions (4000) is connected to the data. Patients (3000) to import patient data (3000) to match with the Polar Map image. The processing is performed using a computer program with the following steps: Step 1: Extract quantitative data from the polar map image using... Optical character reading (Ocr) technique (4200) to extract quantitative information contained within. A polar map image that shows the severity score and performance value. heart Step 2: Divide the image into specific sections using a polar map. Blood vessels (Per-Vessel Image Segmentation) (4300) to perform polar map image segmentation. (Polar Map) Divide the area into several sections or segments.

4. An artificial intelligence system for diagnosing coronary artery stenosis from imaging. Cardiac ischemia scan using a Spect (Spect) and Spect / Ct (Spect / Ct) machine. Under claim 3, where the second set of computer program instructions (4000) is connected to the database. The system (4500) takes the data processed in step 1 and step 2 and saves it in System database (4500) 5. An artificial intelligence system for diagnosing coronary artery stenosis from imaging. Cardiac ischemia scan using a Spect (Spect) and Spect / Ct (Spect / Ct) machine. Under claim 3, where the second set of computer program instructions (4000) brings the processed data. Follow steps 1 and 2 and compare them to the predictive model for tube blockage. Blood (4400) that generates predictive information about blood vessel stenosis in advance, within It includes models that offer the best accuracy in predicting stenosis. Blood vessel conditions are analyzed, and the prediction of blood vessel stenosis is categorized into two parts. Part 1: Quantitative Data Processing (Quantitative Analysis) Forecasting with... A predictive model for blood vessel stenosis using pedagogical learning techniques. Non-Deep Learning is used to analyze and interpret results. Part 2: Qualitative Data Processing (Qualitative Analysis) Forecasting with... A predictive model for blood vessel stenosis using pedagogical learning techniques. Deep learning is used to analyze and interpret the results.

6. An artificial intelligence system for diagnosing coronary artery stenosis from imaging. Cardiac ischemia scan using a Spect (Spect) and Spect / Ct (Spect / Ct) machine. Under claim 5, where the second set of computer program instructions (4000) brings the processed data. Combining the data from Part 1 and Part 2 will result in a predictive analysis. The final stage of coronary artery stenosis involves at least one or more characteristics of blockage. Most accurate, and forwarded to the section for generating diagnostic forecasts for stenosis. Coronary arteries (4420) 7. An artificial intelligence system for diagnosing coronary artery stenosis from imaging. Cardiac ischemia scan using a Spect (Spect) and Spect / Ct (Spect / Ct) machine. Under any one of the claims 1-6, where a second set of computer program instructions (4000) and The predictive model for early vascular stenosis (4400) is connected to the flow pathway system. Data (Data Flow) (4410) is responsible for bringing diagnostic information from doctors into the system. and patient data (3000) consisting of medical record data (3000) and quantitative data (4200). and qualitative data (4300) were used to improve the model for predicting arterial stenosis (4400). After that, record the data in the database (4411) for storing information about the system. Data Flow Pathways (4410)

Images