US 12,274,503 B1
Myopia ocular predictive technology and integrated characterization system
Venkata Narasimham Peri, Plano, TX (US); Naresh Nelaturi, Guntur (IN); Jagannadha Ganti, Hyderabad (IN); Suresh Venkata Satya Attili, Hyderabad (IN); Manoj Ramesh Teltumbade, Pittsburgh, PA (US); Sheena Gill, Centreville, VA (US); Errol R. Norwitz, Auburndale, MA (US); Chetan Chavan, Pune (IN); Prashant Garg, Hyderabad (IN); and Pavan Verkicharla, Hyderabad (IN)
Assigned to COGNITIVECARE INC., Milpitas, CA (US)
Filed by COGNITIVECARE INC., Milpitas, CA (US)
Filed on Jul. 19, 2024, as Appl. No. 18/778,027.
Int. Cl. A61B 3/14 (2006.01); G06T 7/00 (2017.01); G16H 20/00 (2018.01); G16H 30/40 (2018.01); G16H 50/20 (2018.01); G16H 50/30 (2018.01)
CPC A61B 3/14 (2013.01) [G06T 7/0012 (2013.01); G16H 20/00 (2018.01); G16H 30/40 (2018.01); G16H 50/20 (2018.01); G16H 50/30 (2018.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30041 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A system comprising:
a processor storing instructions in non-transitory memory that, when executed, cause the processor to:
receive, via an input module, an input data, wherein the input data comprises an image of an ocular region of a user, clinical data of the user, and external factors;
extract, using an image processing module comprising adaptive filtering techniques, ocular characteristics, wherein the ocular characteristics comprise one or more of Optic Disc Size, Cup Disc Ratio, Optic Disc, Fundus, IOP, Ocular Alignment, Tor CA unaided, BCVA, SPH, Cyl, Tor fr corr axis;
combine, using a multimodal fusion module, the input data to determine a holistic health embedding;
detect, based on a machine learning model and the holistic health embedding, a first output, wherein the first output comprises likelihood of myopia, and severity of myopia;
predict, based on the machine learning model and the holistic health embedding, a second output, wherein the second output comprises an onset of myopia and a progression of myopia in the user;
stratify, risk category of myopia of the user;
display, on a user interface, one or more of the first output and the second output;
receive, a first feedback of the first output and the second output from the machine learning model, to dynamically adjust an accuracy of the first output and the second output from the machine learning model; and
receive, a second feedback from a physician, to dynamically adjust the accuracy of the first output and the second output from the machine learning model; and
wherein the machine learning model is a pre-trained model with a data set comprising the input data from plurality of patients to recognize one or more patterns using the holistic health embedding; and
wherein the system is configured for myopia prognosis powered by multimodal data.