US 12,239,453 B2
System and method for automatic personalized assessment of human body surface conditions
Matthew Toews, Pointe-Claire (CA); James Lee, Pointe-Claire (CA); and Mohsen BenLazreg, Trois-Rivieres (CA)
Assigned to LITTLE ANGEL MEDICAL INC., Pointe-Claire (CA)
Filed by Little Angel Medical Inc., Pointe-Claire (CA)
Filed on Jan. 27, 2023, as Appl. No. 18/102,253.
Claims priority of provisional application 63/308,188, filed on Feb. 9, 2022.
Prior Publication US 2023/0284968 A1, Sep. 14, 2023
Int. Cl. A61B 5/00 (2006.01)
CPC A61B 5/445 (2013.01) [A61B 5/0077 (2013.01); A61B 5/6898 (2013.01); A61B 2503/04 (2013.01)] 9 Claims
OG exemplary drawing
 
1. A system for identifying an abnormal human body surface condition from image data, the system adapted to:
guide a user to acquire at least one first image from at least one location of interest on a body surface of an individual subject when the individual subject is in a normal healthy condition;
a mobile data capture device for use by the user in acquiring the at least one first image under data acquisition criteria to standardize data acquisition, the at least one location of interest selected from skin, throat and ear, and the data acquisition criteria comprising a set distance and relative orientation between the mobile data capture device and the at least one location of interest during data capture;
utilize the at least one first image to obtain a first classification vector according to a number of conditions of interest given the at least one location of interest via a convolutional neural network trained on image data acquired from at least one same location of interest;
maintain a normal model of classification output vectors for the at least one location of interest for the individual subject acquired under the normal healthy condition, the normal model parameterized by a mean vector and covariance matrix;
utilize at least one second image of the at least one location of interest on the individual subject acquired under the data acquisition criteria subsequent to the acquisition of the first image, the at least one second image defined by a second classification vector obtained via the convolutional neural network;
estimate a Mahalanobis distance between the mean vector and the second classification vectors;
compare the Mahalanobis distance against a set threshold indicative of abnormal unhealthy skin condition of the individual subject; and
if the Mahalanobis distance is above the set threshold outputting an indication of the abnormal human body surface condition for the individual subject.