US 11,864,728 B2
Characterization of tissue irregularities through the use of mono-chromatic light refractivity
Frederick E. Shelton, IV, Hillsboro, OH (US); Jason L. Harris, Lebanon, OH (US); Tamara Widenhouse, Clarksville, OH (US); and David C. Yates, Morrow, OH (US)
Assigned to Cilag GmbH International, Zug (CH)
Filed by Cilag GmbH International, Zug (CH)
Filed on Mar. 29, 2018, as Appl. No. 15/940,722.
Claims priority of provisional application 62/649,291, filed on Mar. 28, 2018.
Claims priority of provisional application 62/611,339, filed on Dec. 28, 2017.
Claims priority of provisional application 62/611,341, filed on Dec. 28, 2017.
Claims priority of provisional application 62/611,340, filed on Dec. 28, 2017.
Prior Publication US 2019/0200905 A1, Jul. 4, 2019
Int. Cl. A61B 5/1459 (2006.01); A61B 5/00 (2006.01); A61B 1/00 (2006.01); A61B 18/12 (2006.01); A61B 90/00 (2016.01); A61B 90/30 (2016.01); A61B 17/072 (2006.01); A61B 5/0538 (2021.01); G06T 7/00 (2017.01); A61B 1/04 (2006.01); A61B 1/06 (2006.01); G06T 7/529 (2017.01); G01N 21/27 (2006.01); A61B 5/107 (2006.01); A61B 18/00 (2006.01); A61B 17/00 (2006.01); G16H 40/63 (2018.01); A61B 5/1455 (2006.01); A61B 5/026 (2006.01); A61B 17/32 (2006.01); G16H 40/67 (2018.01); A61B 18/14 (2006.01); A61B 17/068 (2006.01); A61B 34/30 (2016.01); G16H 30/20 (2018.01); A61B 90/60 (2016.01)
CPC A61B 1/000094 (2022.02) [A61B 1/00011 (2013.01); A61B 1/044 (2022.02); A61B 1/0655 (2022.02); A61B 5/0033 (2013.01); A61B 5/0073 (2013.01); A61B 5/0075 (2013.01); A61B 5/0086 (2013.01); A61B 5/0538 (2013.01); A61B 5/1459 (2013.01); A61B 17/07207 (2013.01); A61B 18/1206 (2013.01); A61B 90/30 (2016.02); A61B 90/361 (2016.02); G01N 21/27 (2013.01); G06T 7/0012 (2013.01); G06T 7/529 (2017.01); A61B 5/0066 (2013.01); A61B 5/0261 (2013.01); A61B 5/1076 (2013.01); A61B 5/14551 (2013.01); A61B 17/068 (2013.01); A61B 17/320068 (2013.01); A61B 18/14 (2013.01); A61B 34/30 (2016.02); A61B 90/60 (2016.02); A61B 2017/00017 (2013.01); A61B 2017/00022 (2013.01); A61B 2017/00061 (2013.01); A61B 2017/00398 (2013.01); A61B 2017/00809 (2013.01); A61B 2017/07257 (2013.01); A61B 2017/07285 (2013.01); A61B 2018/0063 (2013.01); A61B 2018/0066 (2013.01); A61B 2018/00601 (2013.01); A61B 2018/00636 (2013.01); A61B 2018/00642 (2013.01); A61B 2018/00708 (2013.01); A61B 2018/00785 (2013.01); A61B 2018/00827 (2013.01); A61B 2018/00982 (2013.01); A61B 2034/303 (2016.02); A61B 2090/064 (2016.02); A61B 2090/065 (2016.02); A61B 2218/002 (2013.01); A61B 2218/008 (2013.01); A61B 2505/05 (2013.01); G16H 30/20 (2018.01); G16H 40/63 (2018.01); G16H 40/67 (2018.01)] 10 Claims
OG exemplary drawing
 
1. A surgical image acquisition system comprising:
an imaging device comprising:
an illumination source to emit light having a specified central wavelength; and
a light sensor to receive a portion of light reflected from a tissue sample;
a surgical hub comprising a situational awareness module; and
a computing system comprising a processor and a memory coupled to the processor, wherein the memory stores machine executable instructions that when executed by the processor cause the processor to:
receive, from the imaging device, imaging data based on the light reflected from the tissue sample;
calculate tissue refractive index data from the imaging data;
calculate structural data related to a characteristic of a structure within the tissue sample based on the tissue refractive index data; and
transmit the imaging data to the situational awareness module,
wherein the situational awareness module comprises an artificial intelligence module trained to determine a position of the imaging device based on the imaging data, and
wherein the characteristic is a surface characteristic or a structure composition.