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人工智能对内镜医师疲劳状态下结直肠腺瘤检出率的影响

Impact of artificial intelligence on colorectal adenoma detection rate in fatigue state of endoscopists

    摘要
  • 摘要:
    目的 分析人工智能(AI)对内镜医师疲劳状态下结直肠腺瘤检出率(ADR)的影响。
    方法 选取在河北省衡水市人民医院内镜中心接受结肠镜检查的患者784例为研究对象。依据患者操作时间段分为A组(n=405, 14: 00—15: 59)和B组(n=379, 16: 00—17: 30)。A组、B组中使用AI辅助检查的纳入AI亚组，未使用AI辅助检查的患者纳入无AI亚组。收集患者年龄、性别、肠道准备质量情况波士顿肠道准备量表(BBPS)评分等基线资料。统计A组、B组的结直肠ADR和结直肠息肉检出率(PDR)。比较A组、B组在有无AI辅助情况下的ADR。
    结果 A组总ADR为32.10%, 高于B组的26.91%, 但差异无统计学意义(P>0.05)。未使用AI辅助时, A组的ADR为25.85%, 高于B组的17.30%, 差异有统计学意义(P < 0.05)。B组使用AI辅助后, 其ADR高于A组未使用AI辅助，差异有统计学意义(P < 0.05); 未使用AI辅助时, A组PDR为33.17%, 高于B组的22.70%, 差异有统计学意义(P < 0.05)。使用AI辅助后, 2组PDR比较，差异无统计学意义(P>0.05)。在A组中, AI亚组(n=200)的ADR为38.50%, 无AI亚组(n=205)的ADR为25.80%, 差异有统计学意义(P=0.006)。在B组中, AI亚组(n=194)的ADR为36.08%, 无AI亚组(n=185)的ADR为17.30%, 差异有统计学意义(P < 0.001)。
    结论 AI辅助下的结肠镜检查可有效提高疲劳状态下内镜医师的结直肠ADR。

     

    Abstract:
    Objective To analyze the impact of artificial intelligence (AI) on colorectal adenoma detection rate (ADR) in fatigue state of endoscopists.
    Methods A total of 784 patients undergoing colonoscopy at the Endoscopy Center of Hengshui People's Hospital in Hebei Province were enrolled. Patients were divided into group A (n=405, time from 14: 00 to 15: 59) and group B (n=379, time from 16: 00 to 17: 30) based on the operation time. Patients in both groups who underwent AI-assisted examination were included in AI subgroup, while those without AI assistance were included in non-AI subgroup. Baseline data, including age, gender and bowel preparation quality assessed by the Boston Bowel Preparation Scale (BBPS) score were collected. The ADR and polyp detection rate (PDR) for colorectal lesions were calculated for both groups, and the ADR was compared between groups with and without AI assistance.
    Results The overall ADR was 32.10% in the group A, which was higher than 26.91% in the group B, but the difference was not statistically significant (P>0.05). Without AI assistance, the ADR in the group A was 25.85%, which was significantly higher than 17.30% in the group B (P < 0.05). In the group B with AI assistance, the ADR was significantly higher than that in the group A without AI assistance (P < 0.05). Without AI assistance, the PDR in the group A was 33.17%, which was significantly higher than 22.70% in the group B (P < 0.05). After using AI assistance, there was no significant difference in PDR between the two groups (P>0.05). In the group A, the ADR was 38.50% in the AI subgroup (n=200) and 25.80% in the non-AI subgroup (n=205), with a statistically significant difference (P=0.006). In the group B, the ADR was 36.08% in the AI subgroup (n=194) and 17.30% in the non-AI subgroup (n=185), with a statistically significant difference (P < 0.001).
    Conclusion AI-assisted colonoscopy can effectively improve the ADR of endoscopists under fatigue conditions.

     

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