US 12,033,755 B2
Method and arrangement for identifying similar pre-stored medical datasets
David Jean Winkel, Basel (CH); Bin Lou, Princeton Junction, NJ (US); Dorin Comaniciu, Princeton Junction, NJ (US); and Ali Kamen, Skillman, NJ (US)
Assigned to Siemens Healthineers AG, Forchheim (DE)
Filed by SIEMENS HEALTHINEERS AG, Forchheim (DE)
Filed on Mar. 11, 2021, as Appl. No. 17/198,318.
Claims priority of application No. 102020207943.9 (DE), filed on Jun. 26, 2020.
Prior Publication US 2021/0407674 A1, Dec. 30, 2021
Int. Cl. G16H 50/20 (2018.01); G16H 10/40 (2018.01); G16H 20/30 (2018.01); G16H 30/00 (2018.01)
CPC G16H 50/20 (2018.01) [G16H 10/40 (2018.01); G16H 20/30 (2018.01); G16H 30/00 (2018.01)] 13 Claims
OG exemplary drawing
 
1. A method for identifying similar pre-stored medical datasets for comparison with a current case dataset, the method comprising:
providing a current case dataset comprising radiological data of a patient;
providing a number of pre-stored medical datasets each comprising radiological data of other patients;
obtaining features for the current case dataset and each of the number of pre-stored medical datasets based on an evaluation of the current case dataset and the number of pre-stored medical datasets according to a predefined AI-based method comprising a convolutional neural network configured to evaluate datasets to obtain a vector of definitive abstract features, the convolutional neural network trained by comparing a predicted risk factor with a histologically determined Gleason score as ground truth;
identifying a number of pre-stored medical datasets most similar to the current case dataset based on a comparison of the features of the current case dataset with the features of each of the number of pre-stored medical datasets; and
outputting the identified number of most similar pre-stored medical datasets.