US 12,329,546 B2
Stress prediction based on neural network
Timothy K. Chung, Pittsburgh, PA (US); and David A. Vorp, Pittsburgh, PA (US)
Assigned to University of Pittsburgh—Of the Commonwealth System of Higher Education, Pittsburgh, PA (US)
Appl. No. 17/769,327
Filed by University of Pittsburgh—Of the Commonwealth System of Higher Education, Pittsburgh, PA (US)
PCT Filed Oct. 14, 2020, PCT No. PCT/US2020/055511
§ 371(c)(1), (2) Date Apr. 14, 2022,
PCT Pub. No. WO2021/076575, PCT Pub. Date Apr. 22, 2021.
Claims priority of provisional application 62/915,565, filed on Oct. 15, 2019.
Prior Publication US 2024/0303804 A1, Sep. 12, 2024
Int. Cl. A61B 5/00 (2006.01); G06T 7/00 (2017.01); G06T 7/13 (2017.01); G06T 7/60 (2017.01); G06T 17/00 (2006.01); G06V 10/26 (2022.01); G06V 10/75 (2022.01); G06V 20/64 (2022.01)
CPC A61B 5/7267 (2013.01) [G06T 7/0012 (2013.01); G06T 7/13 (2017.01); G06T 7/60 (2013.01); G06T 17/00 (2013.01); G06V 10/26 (2022.01); G06V 10/754 (2022.01); G06V 20/653 (2022.01); G06T 2207/20084 (2013.01); G06T 2207/30048 (2013.01); G06T 2207/30101 (2013.01)] 19 Claims
OG exemplary drawing
 
1. A system comprising:
(a) a first neural network configured to:
detect, from a first image of a first cross section of a body part and its surrounding body part, a first outer boundary of the first cross section of the body part and a first inner boundary of the first cross section of the body part, and
detect, from a second image of a second cross section of the body part and the surrounding body part, a second outer boundary of the second cross section of the body part and a second inner boundary of the second cross section of the body part;
(b) a model generator configured to:
generate a three dimensional model of the body part by connecting points on the first outer boundary and the second outer boundary, and connecting points on the first inner boundary and the second inner boundary, wherein the three dimensional model comprises shape indices and location information of the shape indices;
(c) a second neural network configured to:
predict wall stress of the body part, according to the three dimensional model generated based on the first outer boundary, the first inner boundary, the second outer boundary, and the second inner boundary; and
(d) a risk determinator configured to:
compare, for each point of the three dimensional model, the wall stress to a threshold, and
provide an indication associated with the three dimensional model indicative of a risk of rupture according to the wall stress exceeding the threshold.