The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.
The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.
Patent No.:
Date of Patent:
Dec. 02, 2025
Filed:
Oct. 27, 2022
GE Precision Healthcare Llc, Milwaukee, WI (US);
University Health Network, Toronto, CA;
Afis Ajala, Schenectady, NY (US);
Jianwei Qiu, Rexford, NY (US);
John Karigiannis, Laval, CA;
Radhika Madhavan, Latham, NY (US);
Desmond Teck Beng Yeo, Clifton Park, NY (US);
Thomas Kwok-Fah Foo, Clifton Park, NY (US);
Andres M. Lozano, Toronto, CA;
Alexandre Boutet, Toronto, CA;
Jurgen Germann, Toronto, CA;
GE PRECISION HEALTHCARE LLC, Waukesha, WI (US);
Abstract
A system for optimizing DBS parameters for a subject includes automatically performing actions via a processor. The actions include obtaining functional MRI data of a brain of the subject acquired utilizing an MRI system during DBS of the brain utilizing a first set of DBS parameters. The actions include generating functional MRI response maps from the functional MRI data. The actions include extracting, utilizing an unsupervised autoencoder-based neural network, features from the functional MRI response maps. The actions include determining, utilizing a deep learning-based DBS parameter classification model, whether the first set of DBS parameters are optimal DBS parameters for the subject based on the features. The actions include, when the first set of DBS parameters are not the optimal DBS parameters, predicting, utilizing a deep learning-based DBS parameter prediction model, a second set of DBS parameters that are the optimal DBS parameters for the subject based on the features.