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Background <p>Artificial intelligence (AI) is a&#xa0;promising tool to achieve a&#xa0;high adenoma detection rate (ADR). The aim of this study is to evaluate the impact of a&#xa0;computer-aided detection (CADe) device on the ADRs of endoscopists with different levels of expertise.</p> Methods <p>Data were collected from patients who underwent colonoscopy with CADe within a&#xa0;12-month period. Endoscopists were divided into three groups, a&#xa0;trainee group (&lt; 500 colonoscopies), an intermediate group (500–1000 colonoscopies) and an expert group (&gt; 2000 colonoscopies). Endoscopists with the same definition of experience without CADe support served as the control cohort. For the differences in ADR between the groups a&#xa0;2-sided 95% confidence interval (CI) and odds ratios (OR) were calculated.</p> Results <p>In this study 335 patients (155 females, 177 males) with a mean age 62.1 years (SD ± 16.2 years) were included in the CADe cohort. In this cohort 508 polyps were resected. The ADRs for the groups and control groups (without CADe) were as follows: 42.9% (95% CI: 28.5–57.2%) and 21.5% (95% CI: 11.3–31.8%) in the trainee group, 41.3% (95% CI: 33.5–49.0%) and 36.8% (95% CI: 27.9–45.6%) in the intermediate group and 39.8% (95% CI: 30.9–48.8%) and 33.3% (95% CI: 26.3–40.4%) in the expert group. There were no significant differences among the CADe groups when trainees were compared to experts (<i>p</i> = 0.72, OR&#xa0;1.13, 95% CI: 0.58–2.16) or when intermediate endoscopists were compared to experts (<i>p</i> = 0.81, OR&#xa0;1.06, 95% CI: 0.65–1.74).</p> Conclusion <p>The use of AI appears to provide an opportunity to match the ADR-based quality of colonoscopy at an early stage of endoscopy training with experts.</p>

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The impact of artificial intelligence on the adenoma detection rate

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年6月25日

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  • Sebastian Bernhofer,
  • Julian Prosenz,
  • David Venturi,
  • Andreas Maieron

摘要

Background

Artificial intelligence (AI) is a promising tool to achieve a high adenoma detection rate (ADR). The aim of this study is to evaluate the impact of a computer-aided detection (CADe) device on the ADRs of endoscopists with different levels of expertise.

Methods

Data were collected from patients who underwent colonoscopy with CADe within a 12-month period. Endoscopists were divided into three groups, a trainee group (< 500 colonoscopies), an intermediate group (500–1000 colonoscopies) and an expert group (> 2000 colonoscopies). Endoscopists with the same definition of experience without CADe support served as the control cohort. For the differences in ADR between the groups a 2-sided 95% confidence interval (CI) and odds ratios (OR) were calculated.

Results

In this study 335 patients (155 females, 177 males) with a mean age 62.1 years (SD ± 16.2 years) were included in the CADe cohort. In this cohort 508 polyps were resected. The ADRs for the groups and control groups (without CADe) were as follows: 42.9% (95% CI: 28.5–57.2%) and 21.5% (95% CI: 11.3–31.8%) in the trainee group, 41.3% (95% CI: 33.5–49.0%) and 36.8% (95% CI: 27.9–45.6%) in the intermediate group and 39.8% (95% CI: 30.9–48.8%) and 33.3% (95% CI: 26.3–40.4%) in the expert group. There were no significant differences among the CADe groups when trainees were compared to experts (p = 0.72, OR 1.13, 95% CI: 0.58–2.16) or when intermediate endoscopists were compared to experts (p = 0.81, OR 1.06, 95% CI: 0.65–1.74).

Conclusion

The use of AI appears to provide an opportunity to match the ADR-based quality of colonoscopy at an early stage of endoscopy training with experts.