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:
Jun. 18, 2024

Filed:

Jun. 15, 2020
Applicant:

Digital Diagnostics Inc., Coralville, IA (US);

Inventors:

Elliot Swart, Phoenix, AZ (US);

Elektra Efstratiou Alivisatos, Cambridge, MA (US);

Joseph Ferrante, Cambridge, MA (US);

Elizabeth Asai, Boston, MA (US);

Assignee:

Digital Diagnostics Inc., Coralville, IA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 7/90 (2017.01); A61B 5/00 (2006.01); A61B 5/103 (2006.01); G06T 7/00 (2017.01); G06V 10/56 (2022.01); G06V 10/72 (2022.01); G06V 10/764 (2022.01); G06V 40/10 (2022.01); G16H 50/20 (2018.01);
U.S. Cl.
CPC ...
G16H 50/20 (2018.01); A61B 5/1032 (2013.01); A61B 5/441 (2013.01); A61B 5/7267 (2013.01); A61B 5/7275 (2013.01); G06T 7/0014 (2013.01); G06T 7/90 (2017.01); G06V 10/56 (2022.01); G06V 10/72 (2022.01); G06V 10/764 (2022.01); G06V 40/10 (2022.01); A61B 2576/02 (2013.01); G06T 2207/10024 (2013.01); G06T 2207/20076 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30088 (2013.01); G06T 2207/30168 (2013.01); G06V 2201/03 (2022.01);
Abstract

Systems and methods are disclosed herein for determining a diagnosis based on a base skin tone of a patient. In an embodiment, the system receives a base skin tone image of a patient, generates a calibrated base skin tone image by calibrating the base skin tone image using a reference calibration profile, and determines a base skin tone of the patient based on the calibrated base skin tone image. The system receives a concern image of a portion of the patient's skin, and selects a set of machine learning diagnostic models from a plurality of sets of candidate machine learning diagnostic models based on the base skin tone of the patient, each of the sets of candidate machine learning diagnostic models trained to receive the concern image and output a diagnosis of a condition of the patient.


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