US 12,257,063 B2
Objective evaluation of neurological movement disorders from medical imaging
Kristina Simonyan, Weston, MA (US); and Davide Valeriani, Boston, MA (US)
Assigned to MASSACHUSETTS EYE AND EAR INFIRMARY, Boston, MA (US)
Appl. No. 17/765,177
Filed by MASSACHUSETTS EYE AND EAR INFIRMARY, Boston, MA (US)
PCT Filed Sep. 30, 2020, PCT No. PCT/US2020/053571
§ 371(c)(1), (2) Date Mar. 30, 2022,
PCT Pub. No. WO2021/067457, PCT Pub. Date Apr. 8, 2021.
Claims priority of provisional application 62/964,469, filed on Jan. 22, 2020.
Claims priority of provisional application 62/908,448, filed on Sep. 30, 2019.
Prior Publication US 2022/0369999 A1, Nov. 24, 2022
Int. Cl. A61B 5/00 (2006.01); A61B 5/055 (2006.01); G06T 7/00 (2017.01)
CPC A61B 5/4082 (2013.01) [A61B 5/0042 (2013.01); A61B 5/055 (2013.01); A61B 5/4839 (2013.01); G06T 7/0012 (2013.01); G06T 2207/10088 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01)] 10 Claims
OG exemplary drawing
 
1. A system for evaluating a patient for a dystonia comprising:
a processor; and
a non-transitory computer readable medium storing instructions for evaluating a raw magnetic resonance (MR) image of a brain of the patient, the instructions being executable by the processor to provide:
an imager interface that receives the raw MR image from an associated imager and provides the raw MR image to the convolutional neural network, such that no preprocessing is applied to the image prior to providing the image to the convolutional neural network;
a convolutional neural network that provides a set of output values from the raw MR image of the brain of the patient; and a machine learning model that provides a clinical parameter relating to the dystonia for the patient from the set of output values.