Key research questions for implementation of artificial intelligence in capsule endoscopy
(2022) In Therapeutic Advances in Gastroenterology 15.- Abstract
Background: Artificial intelligence (AI) is rapidly infiltrating multiple areas in medicine, with gastrointestinal endoscopy paving the way in both research and clinical applications. Multiple challenges associated with the incorporation of AI in endoscopy are being addressed in recent consensus documents. Objectives: In the current paper, we aimed to map future challenges and areas of research for the incorporation of AI in capsule endoscopy (CE) practice. Design: Modified three-round Delphi consensus online survey. Methods: The study design was based on a modified three-round Delphi consensus online survey distributed to a group of CE and AI experts. Round one aimed to map out key research statements and challenges for the... (More)
Background: Artificial intelligence (AI) is rapidly infiltrating multiple areas in medicine, with gastrointestinal endoscopy paving the way in both research and clinical applications. Multiple challenges associated with the incorporation of AI in endoscopy are being addressed in recent consensus documents. Objectives: In the current paper, we aimed to map future challenges and areas of research for the incorporation of AI in capsule endoscopy (CE) practice. Design: Modified three-round Delphi consensus online survey. Methods: The study design was based on a modified three-round Delphi consensus online survey distributed to a group of CE and AI experts. Round one aimed to map out key research statements and challenges for the implementation of AI in CE. All queries addressing the same questions were merged into a single issue. The second round aimed to rank all generated questions during round one and to identify the top-ranked statements with the highest total score. Finally, the third round aimed to redistribute and rescore the top-ranked statements. Results: Twenty-one (16 gastroenterologists and 5 data scientists) experts participated in the survey. In the first round, 48 statements divided into seven themes were generated. After scoring all statements and rescoring the top 12, the question of AI use for identification and grading of small bowel pathologies was scored the highest (mean score 9.15), correlation of AI and human expert reading-second (9.05), and real-life feasibility-third (9.0). Conclusion: In summary, our current study points out a roadmap for future challenges and research areas on our way to fully incorporating AI in CE reading.
(Less)
- author
- organization
- publishing date
- 2022
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- artificial intelligence, capsule endoscopy, research
- in
- Therapeutic Advances in Gastroenterology
- volume
- 15
- publisher
- SAGE Publications
- external identifiers
-
- pmid:36338789
- scopus:85141792354
- ISSN
- 1756-283X
- DOI
- 10.1177/17562848221132683
- language
- English
- LU publication?
- yes
- id
- 76a648ad-a44b-4939-a082-f6f5e784f3d2
- date added to LUP
- 2022-12-05 12:44:24
- date last changed
- 2024-04-18 15:50:21
@article{76a648ad-a44b-4939-a082-f6f5e784f3d2,
abstract = {{<p>Background: Artificial intelligence (AI) is rapidly infiltrating multiple areas in medicine, with gastrointestinal endoscopy paving the way in both research and clinical applications. Multiple challenges associated with the incorporation of AI in endoscopy are being addressed in recent consensus documents. Objectives: In the current paper, we aimed to map future challenges and areas of research for the incorporation of AI in capsule endoscopy (CE) practice. Design: Modified three-round Delphi consensus online survey. Methods: The study design was based on a modified three-round Delphi consensus online survey distributed to a group of CE and AI experts. Round one aimed to map out key research statements and challenges for the implementation of AI in CE. All queries addressing the same questions were merged into a single issue. The second round aimed to rank all generated questions during round one and to identify the top-ranked statements with the highest total score. Finally, the third round aimed to redistribute and rescore the top-ranked statements. Results: Twenty-one (16 gastroenterologists and 5 data scientists) experts participated in the survey. In the first round, 48 statements divided into seven themes were generated. After scoring all statements and rescoring the top 12, the question of AI use for identification and grading of small bowel pathologies was scored the highest (mean score 9.15), correlation of AI and human expert reading-second (9.05), and real-life feasibility-third (9.0). Conclusion: In summary, our current study points out a roadmap for future challenges and research areas on our way to fully incorporating AI in CE reading.</p>}},
author = {{Leenhardt, Romain and Koulaouzidis, Anastasios and Histace, Aymeric and Baatrup, Gunnar and Beg, Sabina and Bourreille, Arnaud and de Lange, Thomas and Eliakim, Rami and Iakovidis, Dimitris and Dam Jensen, Michael and Keuchel, Martin and Margalit Yehuda, Reuma and McNamara, Deirdre and Mascarenhas, Miguel and Spada, Cristiano and Segui, Santi and Smedsrud, Pia and Toth, Ervin and Tontini, Gian Eugenio and Klang, Eyal and Dray, Xavier and Kopylov, Uri}},
issn = {{1756-283X}},
keywords = {{artificial intelligence; capsule endoscopy; research}},
language = {{eng}},
publisher = {{SAGE Publications}},
series = {{Therapeutic Advances in Gastroenterology}},
title = {{Key research questions for implementation of artificial intelligence in capsule endoscopy}},
url = {{http://dx.doi.org/10.1177/17562848221132683}},
doi = {{10.1177/17562848221132683}},
volume = {{15}},
year = {{2022}},
}