General Electric Company

University Health Network

Albany Medical College

Ge Intellectual Property Licensing, LLC

Radhika Madhavan

Suresh Emmanuel Joel

Jeffrey Michael Ashe

Andres M Lozano

Ileana Hancu

Alexandre Boutet

Hariharan Ravishankar

Arathi Sreekumari

Julie Pilitsis

Gavin Elias

Marisa DiMarzio

A method for controlling a physical process includes receiving an input dataset corresponding to the physical process. The method further includes determining a data model based on the input dataset. The data model includes a plurality of latent space variables of a machine learning model. The method also includes receiving a plurality of reference models corresponding to a plurality of classes. Each of the plurality of reference models includes a corresponding plurality of latent space variables. The method includes comparing the data model with each of the plurality of reference models to generate a plurality of distance metric values. The method further includes selecting a reference model among the plurality of reference models based on the plurality of distance metric values. The method also includes controlling the physical process based on the selected reference model.

System and method for class specific deep learning

A system and method for identifying a patient-specific neurosurgery target location is provided. The system receives brain imaging data for a patient that includes tracts and networks in the patient brain, accesses a quantitative connectome atlas comprising population-based, disease-specific structural and functional connectivity maps comprising a pattern of tracts and networks associated with an optimal target area (OTA) identified from a population of patients, and defines the patient-specific neurosurgery target location based on a comparison between a pattern of the tracts and networks from the brain imaging data for the patient and the pattern of tracts and networks associated with the OTA identified from the population of patients in the quantitative connectome atlas. The quantitative connectome atlas comprises a disease-specific, population-based quantitative connectome atlas that identifies an optimal target location for treatment associated with a maximal clinical improvement for each disease in the population of patients.

Brain connectivity atlas for personalized functional neurosurgery targeting and brain stimulation programming

A system and method for optimizing parameters of a DBS pulse signal for treatment of a patient is provided. In predicting optimal DBS parameters, functional brain data is input into a predictor system, the functional brain data acquired responsive to a sweeping across a multi-dimensional parameter space of one or more DBS parameters. Statistical metrics of brain response are extracted from the functional brain data for one or more ROIs or voxels of the brain via the predictor system, and a DBS functional atlas is accessed, via the predictor system, that comprises disease-specific brain response maps derived from DBS treatment at optimal DBS parameter settings for a plurality of diseases or neurological conditions. One or more optimal DBS parameters are predicted for the patient based on the statistical metrics of brain response and the DBS functional atlas via the predictor system.

Systems and methods for predicting optimal deep brain stimulation parameters

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Bengaluru, India

Radhika Madhavan