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Article: Artificial intelligence–assisted real-time monitoring of effective withdrawal time during colonoscopy: a novel quality marker of colonoscopy

TitleArtificial intelligence–assisted real-time monitoring of effective withdrawal time during colonoscopy: a novel quality marker of colonoscopy
Authors
Issue Date17-Oct-2023
PublisherElsevier
Citation
Gastrointestinal Endoscopy, 2023, v. 99, n. 3, p. 419-427 How to Cite?
Abstract

Background and Aims

The importance of withdrawal time during colonoscopy cannot be overstated in mitigating the risk of missed lesions and postcolonoscopy colorectal cancer. We evaluated a novel colonoscopy quality metric called the effective withdrawal time (EWT), which is an artificial intelligence (AI)-derived quantitative measure of quality withdrawal time, and its association with various colonic lesion detection rates as compared with standard withdrawal time (SWT).

Methods

Three hundred fifty video recordings of colonoscopy withdrawal (from the cecum to the anus) were assessed by the new AI model. The primary outcome was adenoma detection rate (ADR) according to different quintiles of EWT. Multivariate logistic regression, adjusting for baseline covariates, was used to determine the adjusted odd ratios (ORs) for EWT on lesion detection rates, with the lowest quintile as reference. The area under the receiver-operating characteristic curve of EWT was compared with SWT.

Results

The crude ADR in different quintiles of EWT, from lowest to highest, was 10.0%, 31.4%, 33.3%, 53.5%, and 85.7%. The ORs of detecting adenomas and polyps were significantly higher in all top 4 quintiles when compared with the lowest quintile. Each minute increase in EWT was associated with a 49% increase in ADR (aOR, 1.49; 95% confidence interval [CI], 1.36-1.65). The area under the receiver-operating characteristic curve of EWT was also significantly higher than SWT on adenoma detection (.80 [95% CI, .75-.84] vs .70 [95% CI, .64-.74], P < .01).

Conclusions

AI-derived monitoring of EWT is a promising novel quality indicator for colonoscopy, which is more associated with ADR than SWT.


Persistent Identifierhttp://hdl.handle.net/10722/339674
ISSN
2023 Impact Factor: 6.7
2023 SCImago Journal Rankings: 1.749

 

DC FieldValueLanguage
dc.contributor.authorLui, Thomas KL-
dc.contributor.authorKo, Michael KL-
dc.contributor.authorLiu, Jing Jia, Xiao, Xiao-
dc.contributor.authorLeung, Wai K-
dc.date.accessioned2024-03-11T10:38:27Z-
dc.date.available2024-03-11T10:38:27Z-
dc.date.issued2023-10-17-
dc.identifier.citationGastrointestinal Endoscopy, 2023, v. 99, n. 3, p. 419-427-
dc.identifier.issn0016-5107-
dc.identifier.urihttp://hdl.handle.net/10722/339674-
dc.description.abstract<h3>Background and Aims</h3><p>The importance of withdrawal time during colonoscopy cannot be overstated in mitigating the risk of missed lesions and postcolonoscopy colorectal cancer. We evaluated a novel colonoscopy quality metric called the effective withdrawal time (EWT), which is an artificial intelligence (AI)-derived quantitative measure of quality withdrawal time, and its association with various colonic lesion detection rates as compared with standard withdrawal time (SWT).</p><h3>Methods</h3><p>Three hundred fifty video recordings of colonoscopy withdrawal (from the cecum to the anus) were assessed by the new AI model. The primary outcome was adenoma detection rate (ADR) according to different quintiles of EWT. Multivariate logistic regression, adjusting for baseline covariates, was used to determine the adjusted odd ratios (ORs) for EWT on lesion detection rates, with the lowest quintile as reference. The area under the receiver-operating characteristic curve of EWT was compared with SWT.</p><h3>Results</h3><p>The crude ADR in different quintiles of EWT, from lowest to highest, was 10.0%, 31.4%, 33.3%, 53.5%, and 85.7%. The ORs of detecting adenomas and polyps were significantly higher in all top 4 quintiles when compared with the lowest quintile. Each minute increase in EWT was associated with a 49% increase in ADR (aOR, 1.49; 95% confidence interval [CI], 1.36-1.65). The area under the receiver-operating characteristic curve of EWT was also significantly higher than SWT on adenoma detection (.80 [95% CI, .75-.84] vs .70 [95% CI, .64-.74], <em>P</em> < .01).</p><h3>Conclusions</h3><p>AI-derived monitoring of EWT is a promising novel quality indicator for colonoscopy, which is more associated with ADR than SWT.</p>-
dc.languageeng-
dc.publisherElsevier-
dc.relation.ispartofGastrointestinal Endoscopy-
dc.titleArtificial intelligence–assisted real-time monitoring of effective withdrawal time during colonoscopy: a novel quality marker of colonoscopy-
dc.typeArticle-
dc.identifier.doi10.1016/j.gie.2023.10.035-
dc.identifier.volume99-
dc.identifier.issue3-
dc.identifier.spage419-
dc.identifier.epage427-
dc.identifier.eissn1097-6779-
dc.identifier.issnl0016-5107-

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