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:
May. 06, 2025

Filed:

Nov. 27, 2023
Applicant:

Dartmouth-hitchcock Clinic, Lebanon, NH (US);

Inventors:

Matthew LeBoeuf, Hanover, NH (US);

Louis J. Vaickus, Etna, NH (US);

Joshua J. Levy, Lebanon, NH (US);

Assignee:

Dartmouth-Hitchcock Clinic, Lebanon, NH (US);

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
A61B 6/58 (2023.12); A61B 34/10 (2015.12); G06N 3/08 (2022.12); G06Q 20/10 (2011.12); G06Q 30/018 (2022.12); G06T 7/00 (2016.12); G16H 10/40 (2017.12); G16H 15/00 (2017.12); G16H 30/40 (2017.12); G16H 50/20 (2017.12); G16H 50/50 (2017.12); G16H 50/70 (2017.12); G16H 70/60 (2017.12); H04L 9/40 (2021.12);
U.S. Cl.
CPC ...
G16H 50/20 (2017.12); A61B 34/10 (2016.01); G06N 3/08 (2012.12); G06Q 20/102 (2012.12); G06Q 30/0185 (2012.12); G06T 7/0012 (2012.12); G16H 10/40 (2017.12); G16H 15/00 (2017.12); G16H 30/40 (2017.12); G16H 50/50 (2017.12); G16H 50/70 (2017.12); G16H 70/60 (2017.12); H04L 63/08 (2012.12); G06T 2207/20081 (2012.12); G06T 2207/30024 (2012.12); G06T 2207/30096 (2012.12);
Abstract

This invention provides a histologic system and method for rapidly and accurately assessing tumor margins for the presence or absence of tumor using machine learning algorithms. This affords a rapid and accurate histologic tumor readout and increase process efficiency and decreases the chance for human error. Advantageously and uniquely, the system and method allows for analyzing the tissue section as complete or incomplete as the first criteria to determine whether a tissue section is clear of tumor. A machine learning process receives whole slide images (WSI) of tissue and determines (a) if each image of the WSI contains complete/incomplete tissue samples and (b) if each image of the WSI contains tumorous tissue or an absence thereof. A reconstruction process generates a model of the tissue that maps types of tissue therein, and a display process provides results of the model or report for use and manipulation by a user.


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