US 12,224,067 B1
Network for medical image analysis, decision support system, and related graphical user interface (GUI) applications
Mark R. Baker, New York, NY (US)
Assigned to Progenics Pharmaceuticals, Inc., N. Billerica, MA (US)
Filed by Progenics Pharmaceuticals, Inc., N. Billerica, MA (US)
Filed on Oct. 31, 2024, as Appl. No. 18/934,152.
Application 18/934,152 is a continuation of application No. 18/543,113, filed on Dec. 18, 2023.
Application 18/543,113 is a continuation of application No. 17/862,528, filed on Jul. 12, 2022, granted, now 11,894,141, issued on Feb. 6, 2024.
Application 17/862,528 is a continuation of application No. 16/938,488, filed on Jul. 24, 2020, granted, now 11,424,035, issued on Aug. 23, 2022.
Application 16/938,488 is a continuation of application No. 16/418,527, filed on May 21, 2019, granted, now 10,762,993, issued on Sep. 1, 2020.
Application 16/418,527 is a continuation of application No. 15/794,220, filed on Oct. 26, 2017, granted, now 10,340,046, issued on Jul. 2, 2019.
Claims priority of provisional application 62/413,936, filed on Oct. 27, 2016.
This patent is subject to a terminal disclaimer.
Int. Cl. G06K 9/00 (2022.01); G01N 33/574 (2006.01); G06Q 10/00 (2023.01); G16H 15/00 (2018.01); G16H 30/20 (2018.01); G16H 30/40 (2018.01); G16H 50/20 (2018.01); G16H 50/30 (2018.01); G16H 50/70 (2018.01)
CPC G16H 50/20 (2018.01) [G01N 33/57434 (2013.01); G16H 15/00 (2018.01); G16H 30/20 (2018.01); G16H 30/40 (2018.01); G16H 50/30 (2018.01); G16H 50/70 (2018.01)] 34 Claims
OG exemplary drawing
 
1. A network-based decision support system comprising:
a processor; and
a memory having instructions stored thereon, wherein the instructions, when executed by the processor, cause the processor to:
(i) receive and store a plurality of medical images in a database, each medical image associated with a corresponding patient;
(ii) access one or more of the medical images associated with a first patient from the database, wherein the one or more medical images comprises a computed tomography (CT) scan of the first patient and a positron emission tomography (PET) scan of the first patient;
(iii) automatically segment the CT scan using a convolutional neural network (CNN) to identify 3D boundaries of one or more region(s) of imaged tissue; and
(iv) transfer the identified 3D boundaries to the PET scan, identify one or more hotspots within the PET scan, and compute, using the PET scan and the identified 3D boundaries of the one or more region(s) of imaged tissue, one or more risk index values, each risk index value indicative of a cancer state or progression in the first patient, wherein the one or more risk index values comprises a first risk index value based on a total volume of identified hotspots within one or more of the identified 3D boundaries.