US 12,014,828 B2
Using a set of machine learning diagnostic models to determine a diagnosis based on a skin tone of a patient
Elliot Swart, Phoenix, AZ (US); Elektra Efstratiou Alivisatos, Cambridge, MA (US); Joseph Ferrante, Cambridge, MA (US); and Elizabeth Asai, Boston, MA (US)
Assigned to Digital Diagnostics Inc., Coralville, IA (US)
Appl. No. 17/619,173
Filed by Digital Diagnostics Inc., Coralville, IA (US)
PCT Filed Jun. 15, 2020, PCT No. PCT/US2020/037765
§ 371(c)(1), (2) Date Dec. 14, 2021,
PCT Pub. No. WO2020/257108, PCT Pub. Date Dec. 24, 2020.
Claims priority of provisional application 62/862,844, filed on Jun. 18, 2019.
Prior Publication US 2022/0309668 A1, Sep. 29, 2022
Int. Cl. 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)
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)] 17 Claims
OG exemplary drawing
 
1. A method for determining a diagnosis based on a base skin tone of a patient, the method comprising:
receiving a base skin tone image of a patient;
generating a calibrated base skin tone image by calibrating the base skin tone image using a reference calibration profile;
determining a base skin tone of the patient based on the calibrated base skin tone image;
receiving a concern image of a portion of the patient's skin;
selecting 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;
inputting the base skin tone and the concern image into at least one classifier of the selected set of machine learning diagnostic models; and
receiving, as output from the at least one classifier, information from which the diagnosis is derived.