Analysis of big data in colonoscopy to determine whether inverval lesions are de novo, missed or incompletely removed

Piet C. de Groen; Wallapak Tavanapong; Junghwan Oh; Johnny Wong

doi:10.1007/978-3-319-59451-4_31

Analysis of big data in colonoscopy to determine whether inverval lesions are de novo, missed or incompletely removed

Piet C. de Groen
, Wallapak Tavanapong
, Junghwan Oh
, Johnny Wong

Medicine - Gastro, Hepatology, Nutrition Division

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

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.

Original language	English (US)
Title of host publication	Smart Education and e-Learning 2017
Editors	Vladimir L. Uskov, Robert J. Howlett, Lakhmi C. Jain, Lakhmi C. Jain, Robert J. Howlett, Lakhmi C. Jain
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	310-320
Number of pages	11
ISBN (Print)	9783319594507
DOIs	https://doi.org/10.1007/978-3-319-59451-4_31
State	Published - 2017
Event	4th International KES conference on Smart Education and Smart e-Learning, SEEL 2017 - Vilamoura, Algarve, Portugal Duration: Jun 21 2017 → Jun 23 2017

Publication series

Name	Smart Innovation, Systems and Technologies
Volume	75
ISSN (Print)	2190-3018
ISSN (Electronic)	2190-3026

Other

Other	4th International KES conference on Smart Education and Smart e-Learning, SEEL 2017
Country/Territory	Portugal
City	Vilamoura, Algarve
Period	6/21/17 → 6/23/17

Bibliographical note

Publisher Copyright:
© Springer International Publishing AG 2018.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Colonoscopy
Education
Interval colorectal cancer
Quality features
Video stream analysis

Publisher link

10.1007/978-3-319-59451-4_31

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de Groen, P. C., Tavanapong, W., Oh, J., & Wong, J. (2017). Analysis of big data in colonoscopy to determine whether inverval lesions are de novo, missed or incompletely removed. In V. L. Uskov, R. J. Howlett, L. C. Jain, L. C. Jain, R. J. Howlett, & L. C. Jain (Eds.), Smart Education and e-Learning 2017 (pp. 310-320). (Smart Innovation, Systems and Technologies; Vol. 75). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-319-59451-4_31

Analysis of big data in colonoscopy to determine whether inverval lesions are de novo, missed or incompletely removed. / de Groen, Piet C.; Tavanapong, Wallapak; Oh, Junghwan et al.
Smart Education and e-Learning 2017. ed. / Vladimir L. Uskov; Robert J. Howlett; Lakhmi C. Jain; Lakhmi C. Jain; Robert J. Howlett; Lakhmi C. Jain. Springer Science and Business Media Deutschland GmbH, 2017. p. 310-320 (Smart Innovation, Systems and Technologies; Vol. 75).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

de Groen, PC, Tavanapong, W, Oh, J & Wong, J 2017, Analysis of big data in colonoscopy to determine whether inverval lesions are de novo, missed or incompletely removed. in VL Uskov, RJ Howlett, LC Jain, LC Jain, RJ Howlett & LC Jain (eds), Smart Education and e-Learning 2017. Smart Innovation, Systems and Technologies, vol. 75, Springer Science and Business Media Deutschland GmbH, pp. 310-320, 4th International KES conference on Smart Education and Smart e-Learning, SEEL 2017, Vilamoura, Algarve, Portugal, 6/21/17. https://doi.org/10.1007/978-3-319-59451-4_31

de Groen PC, Tavanapong W, Oh J, Wong J. Analysis of big data in colonoscopy to determine whether inverval lesions are de novo, missed or incompletely removed. In Uskov VL, Howlett RJ, Jain LC, Jain LC, Howlett RJ, Jain LC, editors, Smart Education and e-Learning 2017. Springer Science and Business Media Deutschland GmbH. 2017. p. 310-320. (Smart Innovation, Systems and Technologies). doi: 10.1007/978-3-319-59451-4_31

de Groen, Piet C. ; Tavanapong, Wallapak ; Oh, Junghwan et al. / Analysis of big data in colonoscopy to determine whether inverval lesions are de novo, missed or incompletely removed. Smart Education and e-Learning 2017. editor / Vladimir L. Uskov ; Robert J. Howlett ; Lakhmi C. Jain ; Lakhmi C. Jain ; Robert J. Howlett ; Lakhmi C. Jain. Springer Science and Business Media Deutschland GmbH, 2017. pp. 310-320 (Smart Innovation, Systems and Technologies).

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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.

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ER -

Analysis of big data in colonoscopy to determine whether inverval lesions are de novo, missed or incompletely removed

Abstract

Publication series

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Bibliographical note

UN SDGs

Keywords

Publisher link

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