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Real-time, computer-aided, detection-assisted colonoscopy eliminates differences in adenoma detection rate between trainee and experienced endoscopists
Background and study aims Adenoma detection rate (ADR) is a well-accepted quality indicator of screening colonoscopy. In recent years, the added value of artificial intelligence (AI) has been demonstrated in terms of ADR and adenoma miss rate (AMR). To date, there are no studies evaluating the impa...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Georg Thieme Verlag KG
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9106428/ https://www.ncbi.nlm.nih.gov/pubmed/35571479 http://dx.doi.org/10.1055/a-1783-9678 |
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author | Biscaglia, Giuseppe Cocomazzi, Francesco Gentile, Marco Loconte, Ilaria Mileti, Alessia Paolillo, Rosa Marra, Antonella Castellana, Stefano Mazza, Tommaso Di Leo, Alfredo Perri, Francesco |
author_facet | Biscaglia, Giuseppe Cocomazzi, Francesco Gentile, Marco Loconte, Ilaria Mileti, Alessia Paolillo, Rosa Marra, Antonella Castellana, Stefano Mazza, Tommaso Di Leo, Alfredo Perri, Francesco |
author_sort | Biscaglia, Giuseppe |
collection | PubMed |
description | Background and study aims Adenoma detection rate (ADR) is a well-accepted quality indicator of screening colonoscopy. In recent years, the added value of artificial intelligence (AI) has been demonstrated in terms of ADR and adenoma miss rate (AMR). To date, there are no studies evaluating the impact of AI on the performance of trainee endoscopists (TEs). This study aimed to assess whether AI might eliminate any difference in ADR or AMR between TEs and experienced endoscopists (EEs). Patients and methods We performed a prospective observational study in 45 subjects referred for screening colonoscopy. A same-day tandem examination was carried out for each patient by a TE with the AI assistance and subsequently by an EE unaware of the lesions detected by the TE. Besides ADR and AMR, we also calculated for each subgroup of endoscopists the adenoma per colonoscopy (APC), polyp detection rate (PDR), polyp per colonoscopy (PPC) and polyp miss rate (PMR). Subgroup analyses according to size, morphology, and site were also performed. Results ADR, APC, PDR, and PPC of AI-supported TEs were 38 %, 0.93, 62 %, 1.93, respectively. The corresponding parameters for EEs were 40 %, 1.07, 58 %, 2.22. No significant difference was found for each analysis between the two groups ( P > 0.05). AMR and PMR for AI-assisted TEs were 12.5 % and 13 %, respectively. Sub-analyses did not show any significant difference ( P > 0.05) between the two categories of operators. Conclusions In this single-center prospective study, the possible impact of AI on endoscopist quality training was demonstrated. In the future, this could result in better efficacy of screening colonoscopy by reducing the incidence of interval or missed cancers. |
format | Online Article Text |
id | pubmed-9106428 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Georg Thieme Verlag KG |
record_format | MEDLINE/PubMed |
spelling | pubmed-91064282022-05-14 Real-time, computer-aided, detection-assisted colonoscopy eliminates differences in adenoma detection rate between trainee and experienced endoscopists Biscaglia, Giuseppe Cocomazzi, Francesco Gentile, Marco Loconte, Ilaria Mileti, Alessia Paolillo, Rosa Marra, Antonella Castellana, Stefano Mazza, Tommaso Di Leo, Alfredo Perri, Francesco Endosc Int Open Background and study aims Adenoma detection rate (ADR) is a well-accepted quality indicator of screening colonoscopy. In recent years, the added value of artificial intelligence (AI) has been demonstrated in terms of ADR and adenoma miss rate (AMR). To date, there are no studies evaluating the impact of AI on the performance of trainee endoscopists (TEs). This study aimed to assess whether AI might eliminate any difference in ADR or AMR between TEs and experienced endoscopists (EEs). Patients and methods We performed a prospective observational study in 45 subjects referred for screening colonoscopy. A same-day tandem examination was carried out for each patient by a TE with the AI assistance and subsequently by an EE unaware of the lesions detected by the TE. Besides ADR and AMR, we also calculated for each subgroup of endoscopists the adenoma per colonoscopy (APC), polyp detection rate (PDR), polyp per colonoscopy (PPC) and polyp miss rate (PMR). Subgroup analyses according to size, morphology, and site were also performed. Results ADR, APC, PDR, and PPC of AI-supported TEs were 38 %, 0.93, 62 %, 1.93, respectively. The corresponding parameters for EEs were 40 %, 1.07, 58 %, 2.22. No significant difference was found for each analysis between the two groups ( P > 0.05). AMR and PMR for AI-assisted TEs were 12.5 % and 13 %, respectively. Sub-analyses did not show any significant difference ( P > 0.05) between the two categories of operators. Conclusions In this single-center prospective study, the possible impact of AI on endoscopist quality training was demonstrated. In the future, this could result in better efficacy of screening colonoscopy by reducing the incidence of interval or missed cancers. Georg Thieme Verlag KG 2022-05-13 /pmc/articles/PMC9106428/ /pubmed/35571479 http://dx.doi.org/10.1055/a-1783-9678 Text en The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License, which permits unrestricted reproduction and distribution, for non-commercial purposes only; and use and reproduction, but not distribution, of adapted material for non-commercial purposes only, provided the original work is properly cited. |
spellingShingle | Biscaglia, Giuseppe Cocomazzi, Francesco Gentile, Marco Loconte, Ilaria Mileti, Alessia Paolillo, Rosa Marra, Antonella Castellana, Stefano Mazza, Tommaso Di Leo, Alfredo Perri, Francesco Real-time, computer-aided, detection-assisted colonoscopy eliminates differences in adenoma detection rate between trainee and experienced endoscopists |
title | Real-time, computer-aided, detection-assisted colonoscopy eliminates differences in adenoma detection rate between trainee and experienced endoscopists |
title_full | Real-time, computer-aided, detection-assisted colonoscopy eliminates differences in adenoma detection rate between trainee and experienced endoscopists |
title_fullStr | Real-time, computer-aided, detection-assisted colonoscopy eliminates differences in adenoma detection rate between trainee and experienced endoscopists |
title_full_unstemmed | Real-time, computer-aided, detection-assisted colonoscopy eliminates differences in adenoma detection rate between trainee and experienced endoscopists |
title_short | Real-time, computer-aided, detection-assisted colonoscopy eliminates differences in adenoma detection rate between trainee and experienced endoscopists |
title_sort | real-time, computer-aided, detection-assisted colonoscopy eliminates differences in adenoma detection rate between trainee and experienced endoscopists |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9106428/ https://www.ncbi.nlm.nih.gov/pubmed/35571479 http://dx.doi.org/10.1055/a-1783-9678 |
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