IPC분류정보
국가/구분 |
United States(US) Patent
등록
|
국제특허분류(IPC7판) |
|
출원번호 |
US-0228126
(2005-09-16)
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등록번호 |
US-8600124
(2013-12-03)
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발명자
/ 주소 |
- Arnaud, Claude Donald
- Lang, Philipp
- Liew, Siau-Way
- Steines, Daniel
- Vargas-Voracek, Rene
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출원인 / 주소 |
|
대리인 / 주소 |
Sunstein Kann Murphy & Timbers LLP
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인용정보 |
피인용 횟수 :
13 인용 특허 :
131 |
초록
▼
Methods of predicting fracture risk of a patient include: obtaining an image of a bone of the patient; determining one or more bone structure parameters; predicting a fracture line with the bone structure parameter; predicting a fracture load at which a fracture will happen; estimating body habitus
Methods of predicting fracture risk of a patient include: obtaining an image of a bone of the patient; determining one or more bone structure parameters; predicting a fracture line with the bone structure parameter; predicting a fracture load at which a fracture will happen; estimating body habitus of the patient; calculating a peak impact force on the bone when the patient falls; and predicting a fracture risk by calculating the ratio between the peak impact force and the fracture load. Inventive methods also includes determining the effect of a candidate agent on any subject's risk of fracture.
대표청구항
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1. A method for predicting fracture risk using an image of a part of a skeleton of a target in a computer system, the method comprising: locating at least one region of interest on the image of the target;extracting image data from the image of the target;performing a sliding window analysis to gene
1. A method for predicting fracture risk using an image of a part of a skeleton of a target in a computer system, the method comprising: locating at least one region of interest on the image of the target;extracting image data from the image of the target;performing a sliding window analysis to generate at least one bone structure parameter corresponding to at least a portion of the target;generating a parameter map from the at least one bone structure parameter to predict a fracture line;analyzing the at least one bone structure parameter along the predicted fracture line to predict a fracture load at which a fracture will occur;estimating a body habitus of the target; andcalculating a peak impact force on the part of the skeleton of the target when the target falls. 2. The method of claim 1, further comprising: comparing the at least one bone structure parameter to a reference parameter to identify a likely location of a fracture. 3. The method of claim 2, further comprising predicting the fracture load of the target with the target's bone structure parameter and the correlation of the reference's bone structure parameter and the reference's fracture load. 4. The method of claim 2, further comprising: generating bone parameter data corresponding to a bone parameter map of at least a portion of the target. 5. The method of claim 4, wherein the data is stored based on clinical risk factors. 6. The method of claim 4, further comprising storing the bone parameter data in a database of bone parameter data. 7. The method of claim 1, wherein the at least one bone structure parameter is at least one parameter from the group of area ratio and trabecular perimeter. 8. The method of claim 7, wherein the at least one bone structure parameter further comprises a first bone parameter being area ratio and a second bone parameter being trabecular perimeter. 9. The method of claim 1, wherein the parameter map is derived using statistical comparisons of the derived bone structure parameter to a reference population. 10. The method of claim 1, further comprising identifying local abnormalities of bone structure from the parameter map. 11. The method of claim 1, further comprising: tracing low values or high values on the parameter map; and determining a potential fracture line from the low values or high values. 12. The method of claim 1, further comprising using watershed segmentation of parameter maps to identify the fracture line. 13. The method of claim 1, wherein the at least one bone structure parameter includes at least first and second bone structure parameters; and further comprising calculating a fracture load from the first and second bone structure parameters. 14. The method according to claim 1, wherein the body habitus is related to a soft tissue thickness of the target. 15. The method according to claim 1, wherein the body habitus is related to a standing height of the target. 16. The method according to claim 1, wherein the body habitus is related to a body mass of the target. 17. The method of claim 1, further comprising predicting a fracture risk by calculating the ratio between the peak impact force and the fracture load. 18. The method of claim 1, wherein the bone structure parameter is a bone micro-structure parameter. 19. The method of claim 1, wherein the bone structure parameter is a bone macro-structure parameter. 20. The method of claim 1, wherein the at least one region of interest is located automatically. 21. The method of claim 1, wherein the image is selected from the group consisting of x-ray images, x-ray tomosynthesis, ultrasound, computed tomography, magnetic resonance imaging, optical coherence tomography, single photon emission tomography, and positron emission tomography. 22. The method of claim 1, wherein the image is a 2D image. 23. The method of claim 1, wherein the image is a 3D image. 24. The method of claim 1, wherein the image is a 4D image. 25. The method of claim 1, further comprising converting the image to an image of more dimensions. 26. The method of claim 1, further comprising obtaining an image of the target. 27. The method of claim 1, further comprising repeating the step for locating at least one region of interest. 28. The method of claim 1, further comprising repeating the step for extracting image data from the image. 29. The method of claim 1, further comprising: transmitting the image to a second location; converting the image to a pattern of normal or diseased using the bone structure parameter; and analyzing the converted image. 30. The method of claim 29, further comprising transmitting the pattern to a third location for analyzing. 31. A system for analyzing musculoskeletal-related data of a target using a computer, comprising: means for receiving an image of a part of a skeleton of the target; means for deriving at least one bone structure parameter from the image; means for performing a sliding window analysis; means for calculating a possibility of a fracture of a target using a bone structure parameter of the target; means for generating a parameter map from the target's bone structure parameter for presenting a possible fracture line; means for analyzing the target's bone structure parameter along the possible fracture line to calculate a fracture load at which a fracture will occur; means for estimating a body habitus of the target; and means for calculating a peak impact force on the skeleton part when the target falls. 32. The system according to claim 31, further comprising: means for obtaining a fracture load of a skeleton part of a reference; and means for correlating a bone structure parameter of the reference to the fracture load of the reference. 33. The system according to claim 32, further comprising means for receiving the target's bone structure parameter and the correlation of the reference's bone structure parameter and the reference's fracture load, and calculating the fracture load of the target. 34. The system according to claim 33 further comprising means for storing the correlation of the reference's bone structure parameter and the reference's fracture load. 35. The system according to claim 34, wherein the means for storing also receives clinical risk factors of the reference. 36. The system of claim 31, wherein the body habitus is related to a soft tissue thickness of the target. 37. The system of claim 31, wherein the body habitus is related to a standing height of the target. 38. The system of claim 31, wherein the body habitus is related to a body mass of the target. 39. The system of claim 31, further comprising means for calculating the ratio between the peak impact force and the fracture load of the target. 40. The system of claim 31, wherein the bone structure parameter is a bone microstructure parameter. 41. The system of claim 31, wherein the bone structure parameter is a bone macro-structure parameter.
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