Impact of Withdrawal Time on Adenoma Detection Rates: Analysis of Average Risk Screening Colonoscopy Examinations

Abstract Background and study aims Training future endoscopists is essential to meet rising demands for screening and surveillance colonoscopies. Studies have shown conflicting results regarding the influence of trainees on adenoma detection rates (ADR). It is unclear whether trainee participation during screening adversely affects ADR at subsequent surveillance and whether it alters surveillance recommendations. Patients and methods A retrospective analysis of average-risk screening colonoscopies and surveillance exams over a subsequent 10-year period was performed. The initial inclusion criteria were met by 5208 screening and 2285 surveillance exams. Patients with poor preparation were excluded. The final analysis included 7106 procedures, including 4922 screening colonoscopies and 2184 surveillance exams. Data were collected from pathology and endoscopy electronic databases. The primary outcome was the ADR with and without trainee participation. Surveillance recommendations were analyzed as a secondary outcome. Results Trainees participated in 1131 (23 %) screening and in 232 (11 %) surveillance exams. ADR did not significantly differ (P = 0.19) for screening exams with trainee participation (19.5 %) or those without (21.4 %). ADRs were higher at surveillance exams with (22.4 %) and without (27.5 %) trainee participation. ADR at surveillance was not adversely affected by trainee participation during the previous colonoscopy. Shorter surveillance intervals were given more frequently if trainees participated during the initial screening procedure (P = 0.0001). Conclusions ADR did not significantly differ in screening or surveillance colonoscopies with or without trainee participation. ADR at surveillance was not adversely affected by trainee participation during the previous screening exam. However, trainee participation may result in shorter surveillance recommendations.

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Impact of real-time use of artificial intelligence in improving adenoma detection during colonoscopy: A systematic review and meta-analysis

Endoscopy International Open ◽

10.1055/a-1341-0457 ◽

2021 ◽

Vol 09 (04) ◽

pp. E513-E521

Author(s):

Munish Ashat ◽

Jagpal Singh Klair ◽

Dhruv Singh ◽

Arvind Rangarajan Murali ◽

Rajesh Krishnamoorthi

Keyword(s):

Artificial Intelligence ◽

Systematic Review ◽

Meta Analysis ◽

Screening Colonoscopy ◽

Average Risk ◽

Polyp Detection ◽

Withdrawal Time ◽

Detection Rates ◽

Adenoma Detection ◽

The Impact

Abstract Background and study aims With the advent of deep neural networks (DNN) learning, the field of artificial intelligence (AI) is rapidly evolving. Recent randomized controlled trials (RCT) have investigated the influence of integrating AI in colonoscopy and its impact on adenoma detection rates (ADRs) and polyp detection rates (PDRs). We performed a systematic review and meta-analysis to reliably assess if the impact is statistically significant enough to warrant the adoption of AI -assisted colonoscopy (AIAC) in clinical practice. Methods We conducted a comprehensive search of multiple electronic databases and conference proceedings to identify RCTs that compared outcomes between AIAC and conventional colonoscopy (CC). The primary outcome was ADR. The secondary outcomes were PDR and total withdrawal time (WT). Results Six RCTs (comparing AIAC vs CC) with 5058 individuals undergoing average-risk screening colonoscopy were included in the meta-analysis. ADR was significantly higher with AIAC compared to CC (33.7 % versus 22.9 %; odds ratio (OR) 1.76, 95 % confidence interval (CI) 1.55–2.00; I2 = 28 %). Similarly, PDR was significantly higher with AIAC (45.6 % versus 30.6 %; OR 1.90, 95 %CI, 1.68–2.15, I2 = 0 %). The overall WT was higher for AIAC compared to CC (mean difference [MD] 0.46 (0.00–0.92) minutes, I2 = 94 %). Conclusions There is an increase in adenoma and polyp detection with the utilization of AIAC.

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