- 专利标题: Neural network classification of osteolysis and synovitis near metal implants
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申请号: US16975910申请日: 2019-03-04
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公开(公告)号: US11969265B2公开(公告)日: 2024-04-30
- 发明人: Kevin M. Koch , Andrew S. Nencka , Robin A. Karr , Bradley J. Swearingen , Hollis Potter , Matthew F. Koff
- 申请人: THE MEDICAL COLLEGE OF WISCONSIN, INC. , New York Society for the Relief of the Ruptured and Crippled, Maintaining the Hospital for Special Surgery
- 申请人地址: US WI Milwaukee
- 专利权人: The Medical College of Wisconsin, Inc.,New York Society for the Relief of the Ruptured and Crippled, Maintaining the Hospital for Special Surgery
- 当前专利权人: The Medical College of Wisconsin, Inc.,New York Society for the Relief of the Ruptured and Crippled, Maintaining the Hospital for Special Surgery
- 当前专利权人地址: US WI Milwaukee; US NY New York
- 代理机构: Quarles & Brady LLP
- 国际申请: PCT/US2019/020595 2019.03.04
- 国际公布: WO2019/169403A 2019.09.06
- 进入国家日期: 2020-08-26
- 主分类号: A61B5/00
- IPC分类号: A61B5/00 ; A61B5/055 ; G06F18/213 ; G06F18/24 ; G06N3/045 ; G06N3/088 ; G06T7/00 ; G06T7/11 ; G16H30/20 ; G16H30/40 ; G16H50/20 ; G16H50/30 ; G16H50/50 ; G16H50/70
摘要:
Systems and methods for training and implementing a machine learning algorithm to generate feature maps depicting spatial patterns of features associated with osteolysis, synovitis, or both. MRI data, including multispectral imaging data, are input to the trained machine learning algorithm to generate the feature maps, which may indicate features such as a location and probability of a pathology classification, a severity of synovitis, a type of synovitis, a synovial membrane thickness, and other features associated with osteolysis or synovitis. In some implementations, synovial anatomy are segmented in the MRI data before inputting the MRI data to the machine learning algorithm. These segmented MRI data may be generated using another trained machine learning algorithm.
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