US 12,004,860 B2
Cardiac function assessment using machine-learning algorithms
Paul Klein, Princeton, NJ (US); Ingo Schmuecking, Yardley, PA (US); Costin Florian Ciusdel, Azuga (RO); Lucian Mihai Itu, Brasov (RO); Tiziano Passerini, Plainsboro, NJ (US); and Puneet Sharma, Princeton Junction, NJ (US)
Assigned to Siemens Healthineers AG, Forchheim (DE)
Filed by SIEMENS HEALTHINEERS AG, Forchheim (DE)
Filed on Jul. 7, 2021, as Appl. No. 17/305,391.
Claims priority of application No. 102020209696.1 (DE), filed on Jul. 31, 2020; and application No. 20465550 (EP), filed on Jul. 31, 2020.
Prior Publication US 2022/0031218 A1, Feb. 3, 2022
Int. Cl. A61B 5/308 (2021.01); A61B 5/00 (2006.01); A61B 5/026 (2006.01)
CPC A61B 5/308 (2021.01) [A61B 5/026 (2013.01); A61B 5/7267 (2013.01)] 22 Claims
OG exemplary drawing
 
1. A method, comprising:
processing at least one input dataset using a multi-level machine learning processing algorithm, one or more of the at least one input dataset comprising imaging data of a cardiovascular system of a patient,
wherein the multi-level processing algorithm comprises a multi-task level and a consolidation-task level, wherein an input of the consolidation-task level is coupled to an output of the multi-task level, wherein the multi-task level is configured to determine multiple diagnostic metrics of the cardiovascular system based on the at least one input dataset, wherein the consolidation-task level is configured to determine a fitness of the cardiovascular system of the patient,
wherein the multi-level processing algorithm further comprises a pre-processing level, the pre-processing level being configured to determine a quality index for each one of the at least one input dataset, the pre-processing level further configured to filter at least one of the at least one input dataset based on the quality index and/or determine a confidence level of the fitness based on the quality indexes.