Quality assessment in screening colonoscopy: a computer will be watching you

Chiorean, Michael

doi:10.1016/j.gie.2022.01.016

Cited by 1 publication

(1 citation statement)

References 11 publications

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“…14,15 Nevertheless, the occurrence of variances in blurring, periods of inactivity, or cleaning durations during colonoscopy poses challenges when attempting to precisely eliminate these factors in manual WT calculations. 16 Earlier studies have advocated for a minimum WT duration of no less than 6 min, 13,17 although certain investigations have proposed durations of 7 min, 8 min, or even longer. [18][19][20][21] In clinical practice, procedures such as polypectomy and biopsy are frequently performed during colonoscopy, rendering the 6-min standard inappropriate for certain scenarios.…”

Section: Introductionmentioning

confidence: 99%

Development and validation of a novel colonoscopy withdrawal time indicator based on YOLOv5

Li,

Peng,

Wang

et al. 2024

J of Gastro and Hepatol

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Background and AimThe study aims to introduce a novel indicator, effective withdrawal time (WTS), which measures the time spent actively searching for suspicious lesions during colonoscopy and to compare WTS and the conventional withdrawal time (WT).MethodsColonoscopy video data from 472 patients across two hospitals were retrospectively analyzed. WTS was computed through a combination of artificial intelligence (AI) and manual verification. The results obtained through WTS were compared with those generated by the AI system. Patients were categorized into four groups based on the presence of polyps and whether resections or biopsies were performed. Bland Altman plots were utilized to compare AI‐computed WTS with manually verified WTS. Scatterplots were used to illustrate WTS within the four groups, among different hospitals, and across various physicians. A parallel box plot was employed to depict the proportions of WTS relative to WT within each of the four groups.ResultsThe study included 472 patients, with a median age of 55 years, and 57.8% were male. A significant correlation with manually verified WTS (r = 0.918) was observed in AI‐computed WTS. Significant differences in WTS/WT among the four groups were revealed by the parallel box plot (P < 0.001). The group with no detected polyps had the highest WTS/WT, with a median of 0.69 (interquartile range: 0.40, 0.97). WTS patterns were found to be varied between the two hospitals and among senior and junior physicians.ConclusionsA promising alternative to traditional WT for quality control and training assessment in colonoscopy is offered by AI‐assisted computation of WTS.

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