research-article

Designing Visual Markers for Continuous Artificial Intelligence Support: A Colonoscopy Case Study

Authors:

Niels van Berkel,

Danail Stoyanov,

Laurence Lovat,

Ann BlandfordAuthors Info & Claims

ACM Transactions on Computing for Healthcare , Volume 2, Issue 1

Article No.: 7, Pages 1 - 24

https://doi.org/10.1145/3422156

Published: 30 December 2020 Publication History

Abstract

Colonoscopy, the visual inspection of the large bowel using an endoscope, offers protection against colorectal cancer by allowing for the detection and removal of pre-cancerous polyps. The literature on polyp detection shows widely varying miss rates among clinicians, with averages ranging around 22%--27%. While recent work has considered the use of AI support systems for polyp detection, how to visualise and integrate these systems into clinical practice is an open question. In this work, we explore the design of visual markers as used in an AI support system for colonoscopy. Supported by the gastroenterologists in our team, we designed seven unique visual markers and rendered them on real-life patient video footage. Through an online survey targeting relevant clinical staff (N = 36), we evaluated these designs and obtained initial insights and understanding into the way in which clinical staff envision AI to integrate in their daily work-environment. Our results provide concrete recommendations for the future deployment of AI support systems in continuous, adaptive scenarios.

References

[1]

Omer F. Ahmad, Antonio S. Soares, Evangelos Mazomenos, Patrick Brandao, Roser Vega, Edward Seward, Danail Stoyanov, Manish Chand, and Laurence B. Lovat. 2019. Artificial intelligence and computer-aided diagnosis in colonoscopy: Current evidence and future directions. Lancet Gastroenterol. Hepatol. 4, 1 (2019), 71--80.

[2]

Saleema Amershi, Dan Weld, Mihaela Vorvoreanu, Adam Fourney, Besmira Nushi, Penny Collisson, Jina Suh, Shamsi Iqbal, Paul N. Bennett, Kori Inkpen, Jaime Teevan, Ruth Kikin-Gil, and Eric Horvitz. 2019. Guidelines for human-AI interaction. In Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI’19). ACM, New York, NY.

[3]

J. Bernal, J. Sánchez, and F. Vilariño. 2012. Towards automatic polyp detection with a polyp appearance model. Pattern Recog. 45, 9 (2012), 3166--3182.

Digital Library

[4]

Reuben Binns, Max Van Kleek, Michael Veale, Ulrik Lyngs, Jun Zhao, and Nigel Shadbolt. 2018. “It’s reducing a human being to a percentage”: Perceptions of justice in algorithmic decisions. In Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI’18). ACM, New York, NY.

[5]

Florian Block, Victoria Hodge, Stephen Hobson, Nick Sephton, Sam Devlin, Marian F. Ursu, Anders Drachen, and Peter I. Cowling. 2018. Narrative bytes: Data-driven content production in esports. In Proceedings of the ACM International Conference on Interactive Experiences for TV and Online Video (TVX’18). ACM, New York, NY, 29--41.

[6]

G. Bradski. 2000. The OpenCV library. Dr. Dobb’s Journal of Software Tools 25 (2000), 120--125.

[7]

F. Bray, J. Ferlay, I. Soerjomataram, R. L. Siegel, L. A. Torre, and A. Jemal. 2018. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: Canc. J. Clinic. 68, 6 (11 2018), 394--424.

[8]

Amaury Bréhéret. 2017. Pixel Annotation Tool. Retrieved from https://github.com/abreheret/PixelAnnotationTool.

[9]

Carrie J. Cai, Emily Reif, Narayan Hegde, Jason Hipp, Been Kim, Daniel Smilkov, Martin Wattenberg, Fernanda Viegas, Greg S. Corrado, Martin C. Stumpe, and Michael Terry. 2019. Human-centered tools for coping with imperfect algorithms during medical decision-making. In Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI’19). ACM, New York, NY.

[10]

Paul Cairns. 2019. Doing Better Statistics in Human-Computer Interaction. Cambridge University Press.

[11]

S. C. Chen and D. K. Rex. 2007. Endoscopist can be more powerful than age and male gender in predicting adenoma detection at colonoscopy. Amer. J. Gastroenterol. 102, 4 (Apr. 2007), 856--861.

[12]

Eli P. Cox. 1980. The optimal number of response alternatives for a scale: A review. J. Market. Res. 17, 4 (1980), 407--422. Retrieved from http://www.jstor.org/stable/3150495.

[13]

S. S. Deeb. 2005. The molecular basis of variation in human color vision. Clin. Gen. 67, 5 (May 2005), 369--377.

[14]

Alan Dix, Janet Finlay, Gregory D. Abowd, and Russell Beale. 2004. Human-Computer Interaction. Pearson/Prentice-Hall, Harlow, England New York.

[15]

Endoscopic Classification Review Group. 2005. Update on the Paris classification of superficial neoplastic lesions in the digestive tract. Endoscopy 37, 6 (2005), 570--578.

[16]

European Colorectal Cancer Screening Guidelines Working Group. 2013. European guidelines for quality assurance in colorectal cancer screening and diagnosis: Overview and introduction to the full Supplement publication. Endoscopy 45, 01 (2013), 51--59.

[17]

Kraig Finstad. 2010. Response interpolation and scale sensitivity: Evidence against 5-point scales. J. Usab. Stud. 5, 3 (May 2010), 104--110.

[18]

M. Ganz, X. Yang, and G. Slabaugh. 2012. Automatic segmentation of polyps in colonoscopic narrow-band imaging data. IEEE Trans. Biomed. Eng. 59, 8 (Aug. 2012), 2144--2151.

[19]

Kelly Creighton Graham and Maria Cvach. 2010. Monitor alarm fatigue: Standardizing use of physiological monitors and decreasing nuisance alarms. Amer. J. Crit. Care 19, 1 (2010), 28--34.

[20]

Hajime Hata, Hideki Koike, and Yoichi Sato. 2016. Visual guidance with unnoticed blur effect. In Proceedings of the International Working Conference on Advanced Visual Interfaces (AVI’16). ACM, New York, NY, 28--35.

Digital Library

[21]

Andreas Holzinger, Chris Biemann, Constantinos S. Pattichis, and Douglas B. Kell. 2017. What do we need to build explainable AI systems for the medical domain? arXiv e-prints (Dec 2017), arXiv:1712.09923.

[22]

Andreas Holzinger, Georg Langs, Helmut Denk, Kurt Zatloukal, and Heimo Müller. 2019. Causability and explainability of artificial intelligence in medicine. Wiley Interdisc. Rev.: Data Mining Knowl. Discov. 9, 4 (2019), e1312.

[23]

Ahmed Hosny, Chintan Parmar, John Quackenbush, Lawrence H. Schwartz, and Hugo J. W. L. Aerts. 2018. Artificial intelligence in radiology. Nat. Rev. Canc. 18, 8 (2018), 500--510.

[24]

N. Howlader, A. M. Noone, M. Krapcho, D. Miller, A. Brest, M. Yu, J. Ruhl, Z. Tatalovich, A. Mariotto, D. R. Lewis, H. S. Chen, E. J. Feuer, and K. A. Cronin. 2018. SEER Cancer Statistics Review, 1975--2016, National Cancer Institute. Retrieved from https://seer.cancer.gov/csr/1975_2016/.

[25]

Djenaba A. Joseph, Reinier G. S. Meester, Ann G. Zauber, Diane L. Manninen, Linda Winges, Fred B. Dong, Brandy Peaker, and Marjolein van Ballegooijen. 2016. Colorectal cancer screening: Estimated future colonoscopy need and current volume and capacity. Cancer 122, 16 (2016), 2479--2486.

[26]

Maurits Clemens Kaptein, Clifford Nass, and Panos Markopoulos. 2010. Powerful and consistent analysis of Likert-type rating scales. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI’10). ACM, New York, NY, 2391--2394.

Digital Library

[27]

Matthew Kay and Jacob O. Wobbrock. 2019. ARTool: Aligned Rank Transform for Nonparametric Factorial ANOVAs. R package version 0.10.6.

[28]

Jacob Kittley-Davies, Ahmed Alqaraawi, Rayoung Yang, Enrico Costanza, Alex Rogers, and Sebastian Stein. 2019. Evaluating the effect of feedback from different computer vision processing stages: A comparative lab study. In Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI’19). ACM, New York, NY.

[29]

Thomas R. Knapp. 1990. Treating ordinal scales as interval scales: An attempt to resolve the controversy. Nurs. Res. 39, 2 (1990), 121--123.

[30]

Chang Kyun Lee, Dong Il Park, Suck-Ho Lee, Young Hwangbo, Chang Soo Eun, Dong Soo Han, Jae Myung Cha, Bo-In Lee, and Jeong Eun Shin. 2011. Participation by experienced endoscopy nurses increases the detection rate of colon polyps during a screening colonoscopy: A multicenter, prospective, randomized study. Gastroint. Endosc. 74, 5 (2011), 1094--1102.

[31]

A. M. Leufkens, M. G. van Oijen, F. P. Vleggaar, and P. D. Siersema. 2012. Factors influencing the miss rate of polyps in a back-to-back colonoscopy study. Endoscopy 44, 5 (May 2012), 470--475.

[32]

J. S. Mandel, J. H. Bond, T. R. Church, D. C. Snover, G. M. Bradley, L. M. Schuman, and F. Ederer. 1993. Reducing mortality from colorectal cancer by screening for fecal occult blood. Minnesota Colon Cancer Control Study. New Eng. J. Med. 328, 19 (May 1993), 1365--1371.

[33]

Gary Marcus and Ernest Davis. 2019. Rebooting AI: Building Artificial Intelligence We Can Trust. Pantheon.

[34]

Jon McCormack, Toby Gifford, Patrick Hutchings, Maria Teresa Llano Rodriguez, Matthew Yee-King, and Mark d’Inverno. 2019. In a silent way: Communication between AI and improvising musicians beyond sound. In Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI’19). ACM, New York, NY.

[35]

R. A. Miller and F. E. Masarie. 1990. The demise of the “Greek Oracle” model for medical diagnostic systems. Meth. Inf. Med. 29, 1 (Jan. 1990), 1--2.

[36]

Masashi Misawa, Shin-ei Kudo, Yuichi Mori, Tomonari Cho, Shinichi Kataoka, Akihiro Yamauchi, Yushi Ogawa, Yasuharu Maeda, Kenichi Takeda, Katsuro Ichimasa, et al. 2018. Artificial intelligence-assisted polyp detection for colonoscopy: Initial experience. Gastroenterology 154, 8 (2018), 2027--2029.

[37]

Mark A. Musen, Blackford Middleton, and Robert A. Greenes. 2014. Clinical Decision-Support Systems. Springer London, London, 643--674.

[38]

Joshua Newn, Ronal Singh, Fraser Allison, Prashan Madumal, Eduardo Velloso, and Frank Vetere. 2019. Designing interactions with intention-aware gaze-enabled artificial agents. In Human-Computer Interaction -- INTERACT 2019. Springer International Publishing, Cham, 255--281.

[39]

Changhoon Oh, Jungwoo Song, Jinhan Choi, Seonghyeon Kim, Sungwoo Lee, and Bongwon Suh. 2018. I lead, you help but only with enough details: Understanding user experience of co-creation with artificial intelligence. In Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI’18). ACM, New York, NY.

[40]

Minna Pakanen, Jussi Huhtala, and Jonna Häkkilä. 2011. Location visualization in social media applications. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI’11). ACM, New York, NY, 2439--2448.

Digital Library

[41]

S. Y. Park, D. Sargent, I. Spofford, K. G. Vosburgh, and Y. A.-Rahim. 2012. A colon video analysis framework for polyp detection. IEEE Trans. Biomed. Eng. 59, 5 (May 2012), 1408--1418.

[42]

Hans-Peter Piepho. 2004. An algorithm for a letter-based representation of all-pairwise comparisons. J. Computat. Graph. Statist. 13, 2 (2004), 456--466.

[43]

C. Pox, W. Schmiegel, and M. Classen. 2007. Current status of screening colonoscopy in Europe and in the United States. Endoscopy 39, 2 (2007), 168--173.

[44]

Pranav Rajpurkar, Jeremy Irvin, Kaylie Zhu, Brand on Yang, Hershel Mehta, Tony Duan, Daisy Ding, Aarti Bagul, Curtis Langlotz, Katie Shpanskaya, Matthew P. Lungren, and Andrew Y. Ng. 2017. CheXNet: Radiologist-level pneumonia detection on chest X-rays with deep learning. arXiv e-prints (Nov. 2017), arXiv:1711.05225.

[45]

Colin J. Rees, Siwan Thomas Gibson, Matt D. Rutter, Phil Baragwanath, Rupert Pullan, Mark Feeney, and Neil Haslam. 2016. UK key performance indicators and quality assurance standards for colonoscopy. Gut 65, 12 (2016), 1923--1929.

[46]

Douglas K. Rex. 2017. Polyp detection at colonoscopy: Endoscopist and technical factors. Best Pract. Res. Clin. Gastroent. 31, 4 (2017), 425--433.

[47]

Shihab Sarwar, Anglin Dent, Kevin Faust, Maxime Richer, Ugljesa Djuric, Randy Van Ommeren, and Phedias Diamandis. 2019. Physician perspectives on integration of artificial intelligence into diagnostic pathology. npj Digital Medicine 2, 1 (2019), 28.

[48]

Graham Thomas. 2007. Real-time camera tracking using sports pitch markings. J. Real-time Image Proc. 2, 2 (1 Nov. 2007), 117--132.

[49]

Niels van Berkel, Jorge Goncalves, Danula Hettiachchi, Senuri Wijenayake, Ryan M. Kelly, and Vassilis Kostakos. 2019. Crowdsourcing perceptions of fair predictors for machine learning: A recidivism case study. Proc. ACM Hum.-comput. Interact. 3, CSCW (2019), 21.

[50]

Niels van Berkel, Lefteris Papachristos, Anastasia Giachanou, Simo Hosio, and Mikael B. Skov. 2020. A systematic assessment of national artificial intelligence policies: Perspectives from the Nordics and beyond. In Proceedings of the 11th Nordic Conference on Human-computer Interaction (NordiCHI’20). 1--18.

[51]

Jeroen C. van Rijn, Johannes B. Reitsma, Jaap Stoker, Patrick M. Bossuyt, Sander J. van Deventer, and Evelien Dekker. 2006. Polyp miss rate determined by tandem colonoscopy: A systematic review. Amer. J. Gastroent. 101, 2 (2006).

[52]

Jonathan B. VanGeest, Timothy P. Johnson, and Verna L. Welch. 2007. Methodologies for improving response rates in surveys of physicians: A systematic review. Eval. Health Prof. 30, 4 (2007), 303--321.

[53]

Michael Veale, Max Van Kleek, and Reuben Binns. 2018. Fairness and accountability design needs for algorithmic support in high-stakes public sector decision-making. In Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI’18). ACM, New York, NY.

[54]

Danding Wang, Qian Yang, Ashraf Abdul, and Brian Y. Lim. 2019. Designing theory-driven user-centric explainable AI. In Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI’19). ACM, New York, NY.

[55]

Pu Wang, Tyler M. Berzin, Jeremy Romek Glissen Brown, Shishira Bharadwaj, Aymeric Becq, Xun Xiao, Peixi Liu, Liangping Li, Yan Song, Di Zhang, Yi Li, Guangre Xu, Mengtian Tu, and Xiaogang Liu. 2019. Real-time automatic detection system increases colonoscopic polyp and adenoma detection rates: A prospective randomised controlled study. Gut 68, 10 (2019), 1813--1819.

[56]

Jacob O. Wobbrock, Leah Findlater, Darren Gergle, and James J. Higgins. 2011. The aligned rank transform for nonparametric factorial analyses using only ANOVA procedures. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI’11). ACM, New York, NY, 143--146.

Digital Library

[57]

Jeremy M. Wolfe and Todd S. Horowitz. 2004. What attributes guide the deployment of visual attention and how do they do it? Nat. Rev. Neurosci. 5, 6 (2004), 495--501.

[58]

Jeremy M. Wolfe and Todd S. Horowitz. 2017. Five factors that guide attention in visual search. Nat. Hum. Behav. 1, 3 (2017), 0058.

[59]

Qian Yang, Aaron Steinfeld, and John Zimmerman. 2019. Unremarkable AI: Fitting intelligent decision support into critical, clinical decision-making processes. In Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI’19). ACM, New York, NY.

[60]

Kun-Hsing Yu, Andrew L. Beam, and Isaac S. Kohane. 2018. Artificial intelligence in healthcare. Nat. Biomed. Eng. 2, 10 (2018), 719.

[61]

Shengbing Zhao, Shuling Wang, Peng Pan, Tian Xia, Xin Chang, Xia Yang, Liliangzi Guo, Qianqian Meng, Fan Yang, Wei Qian, Zhichao Xu, Yuanqiong Wang, Zhijie Wang, Lun Gu, Rundong Wang, Fangzhou Jia, Jun Yao, Zhaoshen Li, and Yu Bai. 2019. Magnitude, risk factors, and factors associated with adenoma miss rate of tandem colonoscopy: A systematic review and meta-analysis. Gastroenterology 156, 6 (1 May 2019), 1661--1674.e11.

[62]

Haiyi Zhu, Bowen Yu, Aaron Halfaker, and Loren Terveen. 2018. Value-sensitive algorithm design: Method, case study, and lessons. Proc. ACM Hum.-comput. Interact. 2, CSCW (Nov. 2018).

Cited By

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Index Terms

Designing Visual Markers for Continuous Artificial Intelligence Support: A Colonoscopy Case Study
1. Applied computing
  1. Life and medical sciences
2. Human-centered computing
  1. Human computer interaction (HCI)
  2. Interaction design
    1. Empirical studies in interaction design

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cover image ACM Transactions on Computing for Healthcare

ACM Transactions on Computing for Healthcare Volume 2, Issue 1

Special Issue on Wearable Technologies for Smart Health: Part 2 and Regular Papers

January 2021

204 pages

EISSN:2637-8051

DOI:10.1145/3446563

Editors:
Insup Lee
University of Pennsylvania, USA
,
John A. Stankovic
University of Virginia, USA

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Publication History

Published: 30 December 2020

Accepted: 01 July 2020

Revised: 01 April 2020

Received: 01 January 2020

Published in HEALTH Volume 2, Issue 1

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Zhang ZFeger SDullenkopf LLiao RSüsslin LLiu YButz A(2024)Beyond Recommendations: From Backward to Forward AI Support of Pilots' Decision-Making ProcessProceedings of the ACM on Human-Computer Interaction10.1145/36870248:CSCW2(1-32)Online publication date: 8-Nov-2024
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