US 12,484,774 B2
Spectrally adjustable ocular photosensitivity analyzer
Jean Marie Parel, Miami, FL (US); Cornelis Rowaan, Miami, FL (US); Alex Gonzalez, Miami, FL (US); Juan Silgado, Miami, FL (US); Mariela C. Aguilar, Miami, FL (US); and Yu-Cherng Channing Chang, Miami, FL (US)
Assigned to University of Miami, Miami, FL (US)
Appl. No. 17/778,293
Filed by University of Miami, Miami, FL (US)
PCT Filed Nov. 19, 2020, PCT No. PCT/US2020/061336
§ 371(c)(1), (2) Date May 19, 2022,
PCT Pub. No. WO2021/102169, PCT Pub. Date May 27, 2021.
Claims priority of provisional application 62/939,318, filed on Nov. 22, 2019.
Claims priority of provisional application 62/938,805, filed on Nov. 21, 2019.
Prior Publication US 2022/0409041 A1, Dec. 29, 2022
Int. Cl. A61B 3/06 (2006.01); A61B 3/00 (2006.01); A61B 3/14 (2006.01); A61B 5/00 (2006.01); A61B 5/0533 (2021.01); A61B 5/296 (2021.01); A61B 5/395 (2021.01)
CPC A61B 3/063 (2013.01) [A61B 3/0008 (2013.01); A61B 3/0025 (2013.01); A61B 3/0033 (2013.01); A61B 3/0083 (2013.01); A61B 3/14 (2013.01); A61B 5/0077 (2013.01); A61B 5/0533 (2013.01); A61B 5/296 (2021.01); A61B 5/395 (2021.01); A61B 5/4824 (2013.01); A61B 5/7264 (2013.01); A61B 2576/02 (2013.01)] 18 Claims
OG exemplary drawing
 
1. An ocular photosensitivity analyzer comprising:
at least one hardware processor;
a programmable light source comprising a plurality of multi-spectra light modules configured to emit light at a range of wavelengths;
a sensing system comprising one or more sensors; and
one or more software modules that are configured to, when executed by the at least one hardware processor,
receive an indication of a lighting condition comprising one or more wavelengths of light,
configure the programmable light source to emit light according to the lighting condition, and,
for each of one or more iterations,
activate the programmable light source to emit the light according to the lighting condition,
collect a response, by a subject, to the emitted light via the sensing system, and
after collecting the response, analyze the response to determine whether or not the response represents discomfort or pain, wherein analyzing the response comprises applying a machine-learning algorithm, which has been trained to classify responses according to a plurality of classes, to the response to classify the response into one of the plurality of classes, wherein the plurality of classes comprises one or both of a class representing discomfort or a class representing pain.