IPC분류정보
국가/구분 |
United States(US) Patent
등록
|
국제특허분류(IPC7판) |
|
출원번호 |
UP-0876803
(2004-06-25)
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등록번호 |
US-7693315
(2010-05-20)
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발명자
/ 주소 |
- Krishnan, Sriram
- Rao, R. Bharat
- Bennett, Richard M.
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출원인 / 주소 |
- Siemens Medical Solutions USA, Inc.
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대리인 / 주소 |
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인용정보 |
피인용 횟수 :
11 인용 특허 :
12 |
초록
▼
Systems and methods are provided for automated assessment of regional myocardial function using wall motion analysis methods that analyze various features/parameters of patient information (image data and non-image data) obtained from medical records of a patient. For example, a method for providing
Systems and methods are provided for automated assessment of regional myocardial function using wall motion analysis methods that analyze various features/parameters of patient information (image data and non-image data) obtained from medical records of a patient. For example, a method for providing automatic diagnostic support for cardiac imaging generally comprises obtaining image data of a heart of a patient, obtaining features from the image data of the heart, which are related to motion of the myocardium of the heart, and automatically assessing regional myocardial function of one or more regions of a myocardial wall using the obtained features.
대표청구항
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What is claimed is: 1. A method for providing automatic diagnostic support for cardiac imaging, comprising: obtaining information from image data of a heart of a patient, wherein obtaining information from image data comprises automatically extracting myocardial wall motion data and myocardial wall
What is claimed is: 1. A method for providing automatic diagnostic support for cardiac imaging, comprising: obtaining information from image data of a heart of a patient, wherein obtaining information from image data comprises automatically extracting myocardial wall motion data and myocardial wall thickening data from the image data; obtaining information from clinical data records of the patient; combining one or more features from the obtained information from the image data with one or more features from the obtained clinical data records to obtain a combined set of extracted features; and automatically generating a wall motion score for one or more regions of a myocardial wall using the combined set of extracted features; wherein automatically generating a wall motion score for the one or more regions of the myocardial wall comprises implementing a method for classifying regional myocardial function that is trained to analyze wall motion using the combined set of extracted features, and wherein the method for classifying regional myocardial function comprises a machine learning method. 2. The method of claim 1, wherein the image data comprises cardiac ultrasound image data. 3. The method of claim 1, wherein obtaining information from clinical data records comprises automatically extracting clinical data from structured and/or unstructured data sources comprising the clinical data records of the patient. 4. The method of claim 1, wherein automatically generating a wall motion score for one or more regions of the myocardial wall comprises automatically assessing a condition of myocardial tissue of the one or more regions of a myocardial wall using the combined set of extracted features. 5. The method of claim 1, wherein the wall motion score is based on a standardized scoring scheme. 6. The method of claim 5, wherein the standard scoring scheme is specified by the ASE (American Society of Echocardiography). 7. The method of claim 1, further comprising automatically determining a measure of confidence for the wall motion score for the one or more regions of a myocardial wall. 8. The method of claim 1, further comprising retraining the method of classifying regional myocardial function on a continual or periodic basis using expert data and/or data learned from a plurality of case studies. 9. A method for providing automatic diagnostic support for cardiac imaging, comprising: obtaining image data of a heart; obtaining one or more features from the image data of the heart, wherein the features comprise features that are related to motion of the myocardium of the heart; combining the one or more features from the obtained image data with one or more features from obtained clinical data records to obtain a combined set of extracted features; automatically generating a wall motion score for one or more regions of a myocardial wall using the combined set of extracted features, wherein obtaining features from the image data of the heart which are related to motion of the myocardium of the heart comprises: automatically obtaining myocardial wall motion data from the image data of the heart; and automatically obtaining myocardial wall thickening data from the image data of the heart; wherein obtaining features comprises obtaining features for each of a plurality of segments of a myocardial wall and wherein automatically generating a wall motion score for one or more regions of the myocardial wall comprises automatically classifying a condition of myocardial tissue for each segment of said plurality of segments of the myocardial wall, and wherein automatically classifying a condition of myocardial tissue for each segment of said plurality of segments of the myocardial wall comprises generating an indicator that indicates whether the myocardial tissue for each segment is normal or abnormal. 10. The method of claim 9, wherein the image data comprises MR (magnetic resonance) image data. 11. The method of claim 9, wherein the image data comprises CT (computed tomography) image data. 12. The method of claim 9, wherein the image data comprises ultrasound image data. 13. The method of claim 12, wherein the ultrasound image data comprises image data acquired in three dimensions. 14. The method of claim 9, wherein obtaining features from the image data of the heart comprises obtaining features related to myocardial perfusion and/or obtaining features from image data of a coronary artery tree. 15. The method of claim 9, wherein the myocardial wall comprises an endocardial wall of a left ventricle of the heart. 16. The method of claim 9, wherein automatically generating the wall motion score comprises automatically classifying the condition of the myocardial tissue of one or more regions of the myocardial wall using a method that is trained to assess myocardial function based on the combined set of extracted features. 17. The method of claim 16, wherein automatically classifying is performed using a machine learning method. 18. The method of claim 16, wherein obtaining features comprises obtaining features for each of a plurality of segments of a myocardial wall and wherein automatically generating a wall motion score for one or more regions of the myocardial wall comprises automatically classifying a condition of myocardial tissue for each segment of said plurality of segments of the myocardial wall. 19. The method of claim 18, wherein automatically classifying a condition of myocardial tissue for each segment of said plurality of segments of the myocardial wall comprises generating an indicator that indicates whether the myocardial tissue for each segment is normal or abnormal. 20. The method of claim 9, wherein automatically classifying a condition of myocardial tissue for each segment of said plurality of segments of the myocardial wall comprises generating a wall motion score for each segment based on a standard specified by the ASE (American Society of Echocardiography). 21. The method of claim 9, further comprising automatically determining a measure of confidence for each classified condition of myocardial tissue for each segment of said plurality of segments of the myocardial wall. 22. The method of claim 9, further comprising obtaining a global parameter from the image data of the heart, which provides a global indicator of heart function, and wherein the step of automatically generating a wall motion score is performed using the obtained features and an obtained global parameter. 23. The method of claim 22, wherein the global parameter comprises left ventricular volume, left ventricular ejection fraction, left ventricular wall thickness, left ventricular wall mass, or diastolic function indicators such as the E/A ratio. 24. The method of claim 9, further comprising obtaining one or more regional parameters from the image data of the heart, including tissue velocity data, strain data, strain rate data, perfusion data, or timing data, and wherein the step of automatically generating a wall motion score is performed using the using the obtained features and the obtained one or more regional parameters. 25. The method of claim 9, further comprising obtaining clinical data from clinical data records of the patient, and wherein the step of automatically generating a wall motion score is performed using the using the obtained features and the obtained clinical data. 26. The method of claim 9, further comprising automatically diagnosing a medical condition using results obtained from the step of automatically generating a wall motion score. 27. The method of claim 26, wherein automatically diagnosing a medical condition is performed using a classification method. 28. The method of claim 26, additionally comprising automatically determining a probability of diagnosis of a heart disease or condition or automatically determining a probability of developing a heart disease or condition in the future based on the combined set of extracted features. 29. The method of claim 28, wherein automatically determining a probability of diagnosis of a heart disease or condition further comprises automatically determining one or more additional features that would increase a confidence of said probability of diagnosis. 30. The method of claim 29, wherein automatically determining one or more additional features further comprises determining for each of said one or more additional features, a measure of usefulness in increasing said confidence of diagnosis. 31. The method of claim 28, wherein automatically determining a probability of diagnosis of a heart disease or condition further comprises automatically determining one or more additional cardiac imaging tests that would increase a confidence of said probability of diagnosis. 32. The method of claim 31, wherein automatically determining one or more additional cardiac imaging tests further comprises determining for each of said one or more additional tests, a measure of usefulness in increasing said confidence of diagnosis. 33. The method of claim 9, wherein automatically assessing a condition of myocardial tissue comprises automatically identifying one or more previously diagnosed cases that are similar to the current case. 34. The method of claim 33, comprising displaying the one or more identified similar cases.
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