Artificial intelligence technologies for the detection of colorectal lesions: The future is now

Simona Attardo; Viveksandeep Thoguluva Chandrasekar; Marco Spadaccini; Roberta Maselli; Harsh K. Patel; Madhav Desai; Antonio Capogreco; Matteo Badalamenti; Piera Alessia Galtieri; Gaia Pellegatta; Alessandro Fugazza; Silvia Carrara; Andrea Anderloni; Pietro Occhipinti; Cesare Hassan; Prateek Sharma; Alessandro Repici

doi:10.3748/wjg.v26.i37.5606

Artificial intelligence technologies for the detection of colorectal lesions: The future is now

Simona Attardo
, Viveksandeep Thoguluva Chandrasekar
, Marco Spadaccini
, Roberta Maselli
, Harsh K. Patel
, Madhav Desai
, Antonio Capogreco
, Matteo Badalamenti
, Piera Alessia Galtieri
, Gaia Pellegatta
, Alessandro Fugazza
, Silvia Carrara
, Andrea Anderloni
, Pietro Occhipinti
, Cesare Hassan
, Prateek Sharma
, Alessandro Repici

Research output: Contribution to journal › Review article › peer-review

26 Scopus citations

Abstract

Several studies have shown a significant adenoma miss rate up to 35% during screening colonoscopy, especially in patients with diminutive adenomas. The use of artificial intelligence (AI) in colonoscopy has been gaining popularity by helping endoscopists in polyp detection, with the aim to increase their adenoma detection rate (ADR) and polyp detection rate (PDR) in order to reduce the incidence of interval cancers. The efficacy of deep convolutional neural network (DCNN)-based AI system for polyp detection has been trained and tested in ex vivo settings such as colonoscopy still images or videos. Recent trials have evaluated the real-time efficacy of DCNN-based systems showing promising results in term of improved ADR and PDR. In this review we reported data from the preliminary ex vivo experiences and summarized the results of the initial randomized controlled trials.

Original language	English (US)
Pages (from-to)	5606-5616
Number of pages	11
Journal	World journal of gastroenterology
Volume	26
Issue number	37
DOIs	https://doi.org/10.3748/wjg.v26.i37.5606
State	Published - Oct 7 2020
Externally published	Yes

Bibliographical note

Publisher Copyright:
©The Author(s) 2020.

UN SDGs

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

SDG 3 Good Health and Well-being

Keywords

Artificial intelligence
Colonoscopy
Endoscopy
Quality
Screening
Surveillance
Technology

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10.3748/wjg.v26.i37.5606

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Attardo, S., Chandrasekar, V. T., Spadaccini, M., Maselli, R., Patel, H. K., Desai, M., Capogreco, A., Badalamenti, M., Galtieri, P. A., Pellegatta, G., Fugazza, A., Carrara, S., Anderloni, A., Occhipinti, P., Hassan, C., Sharma, P., & Repici, A. (2020). Artificial intelligence technologies for the detection of colorectal lesions: The future is now. World journal of gastroenterology, 26(37), 5606-5616. https://doi.org/10.3748/wjg.v26.i37.5606

Attardo, S, Chandrasekar, VT, Spadaccini, M, Maselli, R, Patel, HK, Desai, M, Capogreco, A, Badalamenti, M, Galtieri, PA, Pellegatta, G, Fugazza, A, Carrara, S, Anderloni, A, Occhipinti, P, Hassan, C, Sharma, P & Repici, A 2020, 'Artificial intelligence technologies for the detection of colorectal lesions: The future is now', World journal of gastroenterology, vol. 26, no. 37, pp. 5606-5616. https://doi.org/10.3748/wjg.v26.i37.5606

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abstract = "Several studies have shown a significant adenoma miss rate up to 35\% during screening colonoscopy, especially in patients with diminutive adenomas. The use of artificial intelligence (AI) in colonoscopy has been gaining popularity by helping endoscopists in polyp detection, with the aim to increase their adenoma detection rate (ADR) and polyp detection rate (PDR) in order to reduce the incidence of interval cancers. The efficacy of deep convolutional neural network (DCNN)-based AI system for polyp detection has been trained and tested in ex vivo settings such as colonoscopy still images or videos. Recent trials have evaluated the real-time efficacy of DCNN-based systems showing promising results in term of improved ADR and PDR. In this review we reported data from the preliminary ex vivo experiences and summarized the results of the initial randomized controlled trials.",

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doi = "10.3748/wjg.v26.i37.5606",

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AU - Fugazza, Alessandro

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AU - Repici, Alessandro

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KW - Quality

KW - Screening

KW - Surveillance

KW - Technology

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Artificial intelligence technologies for the detection of colorectal lesions: The future is now

Abstract

Bibliographical note

UN SDGs

Keywords

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