The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Mar. 04, 2025

Filed:

Jun. 14, 2023
Applicant:

Exini Diagnostics Ab, Lund, SE;

Assignee:
Attorneys:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G16H 30/40 (2018.01); G06T 7/00 (2017.01); G06T 7/11 (2017.01); G16H 50/20 (2018.01); G16H 50/30 (2018.01);
U.S. Cl.
CPC ...
G16H 30/40 (2018.01); G06T 7/0012 (2013.01); G06T 7/11 (2017.01); G16H 50/20 (2018.01); G16H 50/30 (2018.01); G06T 2207/10072 (2013.01); G06T 2207/30056 (2013.01); G06T 2207/30096 (2013.01);
Abstract

Presented herein are systems and methods that provide for improved detection and characterization of lesions within a subject via automated analysis of nuclear medicine images, such as positron emission tomography (PET) and single photon emission computed tomography (SPECT) images. In particular, in certain embodiments, the approaches described herein leverage artificial intelligence (AI) to detect regions of 3D nuclear medicine images corresponding to hotspots that represent potential cancerous lesions in the subject. The machine learning modules may be used not only to detect presence and locations of such regions within an image, but also to segment the region corresponding to the lesion and/or classify such hotspots based on the likelihood that they are indicative of a true, underlying cancerous lesion. This AI-based lesion detection, segmentation, and classification can provide a basis for further characterization of lesions, overall tumor burden, and estimation of disease severity and risk.


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