US 12,268,473 B2
Device for non-invasive detection of skin problems associated with diabetes mellitus
Mike Van Snellenberg, Seattle, WA (US); Anne Weiler, Seattle, WA (US); Luke Feaster, Seattle, WA (US); Ben Spencer, Seattle, WA (US); Jahyen Chung, Bellevue, WA (US); Sara Hansen-Lund, Seattle, WA (US); Soma Mandal, Seattle, WA (US); Josh Bishop, Seattle, WA (US); and Gavin Ray, Seattle, WA (US)
Assigned to Signify Health, LLC, Dallas, TX (US)
Filed by Signify Health, LLC, Dallas, TX (US)
Filed on May 24, 2022, as Appl. No. 17/752,755.
Application 17/752,755 is a division of application No. 16/044,248, filed on Jul. 24, 2018, abandoned.
Claims priority of provisional application 62/536,388, filed on Jul. 24, 2017.
Prior Publication US 2022/0280100 A1, Sep. 8, 2022
Int. Cl. A61B 5/00 (2006.01); A61B 5/01 (2006.01); G01G 19/50 (2006.01); G06T 7/00 (2017.01); G16H 30/20 (2018.01); G16H 30/40 (2018.01); G16H 40/63 (2018.01); G16H 50/20 (2018.01); H04N 5/33 (2023.01); G06F 18/24 (2023.01); G06T 7/70 (2017.01)
CPC A61B 5/0077 (2013.01) [A61B 5/0035 (2013.01); A61B 5/0053 (2013.01); A61B 5/015 (2013.01); A61B 5/4041 (2013.01); A61B 5/441 (2013.01); A61B 5/445 (2013.01); A61B 5/4827 (2013.01); A61B 5/6829 (2013.01); A61B 5/6887 (2013.01); A61B 5/7267 (2013.01); A61B 5/741 (2013.01); G01G 19/50 (2013.01); G06T 7/0012 (2013.01); G16H 30/20 (2018.01); G16H 30/40 (2018.01); G16H 40/63 (2018.01); G16H 50/20 (2018.01); H04N 5/33 (2013.01); A61B 5/706 (2013.01); A61B 5/7405 (2013.01); A61B 5/742 (2013.01); A61B 2562/0252 (2013.01); G06F 18/24 (2023.01); G06T 7/70 (2017.01); 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)] 18 Claims
OG exemplary drawing
 
1. A method for automated diagnosis of a diabetic foot condition, the method comprising:
capturing, by one or more image capture devices of a medical diagnostic apparatus, optical image data of a target area of a foot;
collecting, by a touch sensitivity testing device of the medical diagnostic apparatus, physical touch sensitivity data for the target area of the foot;
transmitting, by the medical diagnostic apparatus, the optical image data and the physical touch sensitivity data to an analysis engine, wherein the analysis engine comprises a neural network classifier pipeline; and
outputting, by the analysis engine, one or more indications of a diabetic foot condition,
wherein the neural network classifier pipeline of the analysis engine automatically performs classification steps on the optical image data to generate the one or more indications of a diabetic foot condition, and wherein the classification steps performed by the neural network classifier pipeline include:
detecting presence, position, and orientation of the foot;
detecting presence and location of a skin abnormality on the foot; and
classifying the detected skin abnormality as a diabetic foot ulcer.
 
7. A non-transitory computer-readable medium having stored therein instructions configured to cause one or more computing devices to cause steps to be performed for automated diagnosis of a diabetic foot condition, the steps comprising:
capturing, by one or more image capture devices of a medical diagnostic apparatus, optical image data of a target area of a foot;
collecting, by a touch sensitivity testing device of the medical diagnostic apparatus, physical touch sensitivity data for the target area of the foot;
transmitting, by the medical diagnostic apparatus, the optical image data and the physical touch sensitivity data to an analysis engine, wherein the analysis engine comprises a neural network classifier pipeline; and
outputting, by the analysis engine, one or more indications of a diabetic foot condition,
wherein the neural network classifier pipeline of the analysis engine automatically performs classification steps on the optical image data to generate the one or more indications of a diabetic foot condition, and wherein the classification steps performed by the neural network classifier pipeline include:
detecting presence, position, and orientation of the foot;
detecting presence and location of a skin abnormality on the foot; and
classifying the detected skin abnormality as a diabetic foot ulcer.
 
13. A computer system comprising one or more computing devices programmed to perform steps for automated diagnosis of a diabetic foot condition, the steps comprising:
causing one or more image capture devices of a medical diagnostic apparatus to capture optical image data of a target area of a foot;
causing a touch sensitivity testing device of the medical diagnostic apparatus to collect physical touch sensitivity data for the target area of the foot;
causing the medical diagnostic apparatus to transmit the optical image data and the physical touch sensitivity data to an analysis engine, wherein the analysis engine comprises a neural network classifier pipeline; and
obtaining one or more indications of a diabetic foot condition from the analysis engine,
wherein the neural network classifier pipeline of the analysis engine automatically performs classification steps on the optical image data to generate the one or more indications of a diabetic foot condition, and wherein the classification steps performed by the neural network classifier pipeline include:
detecting presence, position, and orientation of the foot;
detecting presence and location of a skin abnormality on the foot; and
classifying the detected skin abnormality as a diabetic foot ulcer.