TY - GEN
T1 - Analysis of big data in colonoscopy to determine whether inverval lesions are de novo, missed or incompletely removed
AU - de Groen, Piet C.
AU - Tavanapong, Wallapak
AU - Oh, Junghwan
AU - Wong, Johnny
PY - 2018
Y1 - 2018
N2 - We have created a system that automatically records the inside-the-patient images of each colonoscopy in de-identified fashion. At present this “big data” database contains around 100 TB of de-identified endoscopy data. Interval colorectal cancers (CRCs) are CRCs that develop despite periodic colonoscopy and are due to de novo tumor growth, a missed lesion or incomplete lesion removal. Using a combination of location, date, time and image information we were able to find a video file within our de-identified big data from a prior colonoscopy that belonged to a patient with a recently diagnosed large interval lesion. Analysis of the video file showed that a large lesion was incompletely removed. Analysis of big endoscopy datasets has the potential to resolve the cause of most if not all interval lesions and CRCs and can provide specific, focused education to endoscopists related to their individual limitations.
AB - We have created a system that automatically records the inside-the-patient images of each colonoscopy in de-identified fashion. At present this “big data” database contains around 100 TB of de-identified endoscopy data. Interval colorectal cancers (CRCs) are CRCs that develop despite periodic colonoscopy and are due to de novo tumor growth, a missed lesion or incomplete lesion removal. Using a combination of location, date, time and image information we were able to find a video file within our de-identified big data from a prior colonoscopy that belonged to a patient with a recently diagnosed large interval lesion. Analysis of the video file showed that a large lesion was incompletely removed. Analysis of big endoscopy datasets has the potential to resolve the cause of most if not all interval lesions and CRCs and can provide specific, focused education to endoscopists related to their individual limitations.
KW - Colonoscopy
KW - Education
KW - Interval colorectal cancer
KW - Quality features
KW - Video stream analysis
UR - http://www.scopus.com/inward/record.url?scp=85020460374&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85020460374&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-59451-4_31
DO - 10.1007/978-3-319-59451-4_31
M3 - Conference contribution
AN - SCOPUS:85020460374
SN - 9783319594507
VL - 75
T3 - Smart Innovation, Systems and Technologies
SP - 310
EP - 320
BT - Smart Education and e-Learning 2017
PB - Springer Science and Business Media Deutschland GmbH
T2 - 4th International KES conference on Smart Education and Smart e-Learning, SEEL 2017
Y2 - 21 June 2017 through 23 June 2017
ER -