US 12,243,644 B2
Medical diagnosis assistance system and method
Thomas Engel, Aalen (DE); Gaby Marquardt, Hausen (DE); and Jens-Peter Brock, Langenzenn (DE)
Assigned to Siemens Healthcare Diagnostics Inc., Tarrytown, NY (US)
Filed by Siemens Healthcare Diagnostics Inc., Tarrytown, NY (US)
Filed on Jul. 19, 2022, as Appl. No. 17/868,605.
Claims priority of application No. 21186626 (EP), filed on Jul. 20, 2021.
Prior Publication US 2023/0025181 A1, Jan. 26, 2023
Int. Cl. G16H 30/40 (2018.01); G06T 7/00 (2017.01); G16H 50/20 (2018.01)
CPC G16H 50/20 (2018.01) [G06T 7/0012 (2013.01); G16H 30/40 (2018.01); G06T 2207/20081 (2013.01); G06T 2207/30024 (2013.01)] 13 Claims
OG exemplary drawing
 
1. A medical diagnosis assistance system, comprising:
an input interface configured to receive medical image data of a patient;
a computing device configured to implement:
a classification module configured to classify parts of interest, POI, comprising objects of interest, OOI, or regions of interest, ROI, within the received medical image data, and to assign a corresponding reliability metric to each of the classified POI; wherein:
the classification module is configured to implement a classifying artificial intelligence entity, CAIE, which is trained and configured to receive at least a portion of the received medical image data and to generate, based thereon, a CAIE output classifying the POI or the corresponding reliability metric,
the CAIE comprises a first-level classifying artificial intelligence sub-entity, FLCAISE, and a second-level artificial intelligence sub-entity, SLCAISE,
the FLCAISE is trained and configured to receive, as its input, at least a portion of the received medical image data and to generate, based thereon, a FLCAISE output classifying at least a part of the POI according to a broad classification scheme; and
the SLCAISE is trained and configured to receive, as its input, at least a portion of the received medical image data and the FLCAISE output, and to further classify at least the part of the POI according to a refined classification scheme, and
an analysis module configured to determine, based on the POI and the assigned reliability metric, an analysis of the medical image data; and
an output interface configured to output an output signal indicating the analysis.