公开(公告)号：US20240169525A1

公开(公告)日：2024-05-23

申请号：US18354921

申请日：2023-07-19

申请人： Case Western Reserve University ,  UH Cleveland Medical Center

发明人： David L. Wilson ,  Ammar Hoori ,  Tao Hu ,  Yingnan Song ,  Hao Wu ,  Juhwan Lee ,  Sadeer Al-Kindi ,  Sanjay Rajagopalan

IPC分类号： G06T7/00 ,  G06T7/11 ,  G06T7/30 ,  G06V10/26 ,  G06V10/44 ,  G06V10/766 ,  G06V10/774 ,  G06V20/50 ,  G16H50/30 ,  G16H50/70

CPC分类号： G06T7/0012 ,  G06T7/11 ,  G06T7/30 ,  G06V10/26 ,  G06V10/44 ,  G06V10/766 ,  G06V10/774 ,  G06V20/50 ,  G16H50/30 ,  G16H50/70 ,  G06T2207/10081 ,  G06T2207/20081 ,  G06T2207/30048 ,  G06V2201/031

摘要： The present disclosure, in some embodiments, relates to a method of generating a prognosis for a patient. The method includes accessing automatically segmented pericoronary adipose tissue (PCAT) corresponding to a patient within an electronic memory. A plurality of non-confounding PCAT features are generated by measuring values of Hounsfield units for an imaging unit within the PCAT. The measured values of the Hounsfield units are predominately free of iodine confounding and artifacts. The plurality of non-confounding PCAT features are provided to a regression model. The regression model is configured to generate a prognosis for the patient using the plurality of non-confounding PCAT features

公开(公告)号：US20240321461A1

公开(公告)日：2024-09-26

申请号：US18609005

申请日：2024-03-19

申请人： Case Western Reserve University

发明人： David L. Wilson ,  Sadeer Al-Kindi ,  Yingnan Song ,  Ammar Hoori ,  Hao Wu ,  Yiqiao Liu

IPC分类号： G16H50/30 ,  G06T7/00 ,  G16H15/00 ,  G16H50/20

CPC分类号： G16H50/30 ,  G06T7/0002 ,  G16H15/00 ,  G16H50/20 ,  G06T2207/10081 ,  G06T2207/20081 ,  G06T2207/30048 ,  G06T2207/30056 ,  G06T2207/30101

摘要： Systems, methods, and apparatus are provided for determining a risk prediction for major adverse cardiovascular event (MACE) for a patient based on a computed tomography (CT) calcium score image of the patient's chest. In one example, a method includes receiving a computed tomography (CT) calcium score image of a chest; identifying tissue of interest in the CT calcium score image; analyzing the CT calcium score image to determine features of the identified tissue of interest; and determining a risk prediction of MACE based on the features.