公开(公告)号：US20250087355A1

公开(公告)日：2025-03-13

申请号：US18720485

申请日：2022-12-16

Applicant: Peter Koulen ,  Amirfarhang Mehdizadeh ,  Mohan Kumar Gajendran

Inventor： Peter Koulen ,  Amirfarhang Mehdizadeh ,  Mohan Kumar Gajendran

IPC: G16H50/20 ,  A61B5/398

Abstract: A method of diagnosing glaucoma using machine learning methods comprises determining a labeled training data set. The labeled training data set comprises electroretinography (ERG) signals measured from a group of subjects. The ERG signals are labeled either glaucomatous or non-glaucomatous based on the subject from which each ERG signal was measured. The training data set is used to train a machine learning model, WNW such as a decision tree model, a discriminant model, a support vector machine, a nearest neighbor algorithm, or an ensemble classifier. The resulting trained machine learning model is configured to classify an ERG signal input as glaucomatous or non-glaucomatous. The model can be employed by measuring an ERG from a subject and inputting the measured ERG into the trained machine learning model. The subject can be diagnosed as having glaucoma based on an output classification of glaucomatous.