公开(公告)号：US20240370999A1

公开(公告)日：2024-11-07

申请号：US18652286

申请日：2024-05-01

Applicant: Siemens Healthineers AG

Inventor： Philipp HOELZER ,  Tobias HECKEL ,  Stefan ASSMANN ,  Ayse KARABAYIR ,  Torbjoern KLATT ,  Robin GUTSCHE ,  Sebastian SCHMIDT ,  Ali KAMEN ,  Vivek SINGH ,  Alexander BROST ,  Matthias SIEBERT ,  Jonathan SPERL

IPC: G06T7/00

Abstract: A computer-implemented method of adjudicating an imaged lesion, comprising: receiving a diagnostic image showing a lesion; processing the diagnostic image in a machine learning algorithm previously trained to classify the lesion and to propose, based on a lesion class for the lesion, a blood test panel suited to adjudicate the lesion; and outputting the proposed blood test panel to a user.

公开(公告)号：US20240331803A1

公开(公告)日：2024-10-03

申请号：US18616898

申请日：2024-03-26

Applicant: Siemens Healthineers AG

Inventor： Matthias SIEBERT

IPC: G16B30/00 ,  G16B5/00 ,  G16B50/30 ,  G16H50/20

CPC classification number: G16B30/00 ,  G16B5/00 ,  G16B50/30 ,  G16H50/20

Abstract: A computer-implemented method for analyzing genomic sequence data comprises: obtaining genomic sequence data; obtaining data from three-dimensional protein structures; mapping the genomic sequence data on the protein structures; inputting the mapped genomic sequence data into a trained graph neural network; and deriving a diagnostic, prognostic and/or predictive conclusion output with respect to said disease or medical condition. The architecture of the graph neural network is based on the three-dimensional protein structure. The graph neural network is trained based on genomic sequence data from a cohort of subjects affected by a disease or medical condition mapped to the three-dimensional protein structures and corresponding diagnostic, prognostic and/or predictive conclusions in the context of the disease or medical condition.