{"title":"[人工智能辅助识别系统对结肠镜检查质量的影响]。","authors":"B Jin, L Huang, S Liu, B Lyu, Y Hu","doi":"10.3760/cma.j.cn112138-20240216-00109","DOIUrl":null,"url":null,"abstract":"<p><p><b>Objective:</b> To explore the value of the artificial intelligence (AI)-assisted recognition system in the detection quality of colonoscopy. <b>Methods:</b> From January 2023, the data on 700 patients who underwent colonoscopy in the Digestive Endoscopy Center of the First Affiliated Hospital of Zhejiang Chinese Medical University were collected prospectively. Based on a computerized number method, patients were divided into the AI assistance group and control group. The detection rate of adenomas (ADR) and polyps, number and size of adenomas, Boston bowel preparation scale (BBPS), intubation time, withdrawal time, and cecal intubation rate were compared between groups. Normally distributed data were analyzed with the <i>t</i>-test for independent samples. Non-normally distributed data were analyzed with the Rank sum test. Categorical data were analyzed with the Chi-square test. <b>Results:</b> In total, 691 patients were included in the analysis. According to the intention to treat (ITT) analysis and per-protocol (PP) analysis, the withdrawal time of the AI group was higher than that of the control group (ITT:436 (305, 620) vs 368 (265, 510) s, <i>Z</i>=-4.24, <i>P</i><0.001;PP:439 (306, 618) vs 364 (262, 500) s,<i>t</i>=-4.50, <i>P</i><0.001); however, there were no significant differences in the ADR (ITT:123(35.5%) vs 111(32.2%), <i>χ</i><sup>2</sup>=0.88, <i>P</i>=0.349;PP:108(34.2%) vs 99(31.1%), <i>χ</i><sup>2</sup>=0.67, <i>P</i>=0.414), the number of adenomas (ITT:0(0, 1) vs 0(0, 1),<i>Z</i>=-1.08, <i>P</i>=0.282;PP:0(0, 1) vs 0(0, 1),<i>Z</i>=-0.87, <i>P</i>=0.387), the polyp detection rate (ITT:85(24.6%) vs 85(24.6%),<i>χ</i><sup>2</sup>=0.001, <i>P</i>=0.983;PP:79(25.0%) vs 77(24.2%),<i>χ</i><sup>2</sup>=0.05, <i>P</i>=0.818), BBPS (ITT:6.5±0.9 vs 6.5±0.7,<i>t</i>=-0.59, <i>P</i>=0.555;PP:6.7±0.6 vs 6.6±0.6,<i>t</i>=-1.83, P=0.068), and cecal intubation rate (ITT:346(100.0%) vs 343(99.4%), <i>χ</i><sup>2</sup>=0.50, <i>P</i>=0.478) between these two groups. After excluding inadequate bowel preparation and failed cecal intubation cases, the AI-assisted system was found to significantly improve the detection rate of small adenomas (≤5 mm) (PP:27.8%(88/316)vs 21.1%(67/318), <i>χ</i><sup>2</sup>=3.94, <i>P</i>=0.047). <b>Conclusions:</b> The application of an AI-assisted system in colonoscopy can increase the withdrawal time and improve the detection rate of small adenomas.</p>","PeriodicalId":68309,"journal":{"name":"中华内科杂志","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"[Effect of an artificial intelligence-assisted recognition system on colonoscopy quality].\",\"authors\":\"B Jin, L Huang, S Liu, B Lyu, Y Hu\",\"doi\":\"10.3760/cma.j.cn112138-20240216-00109\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p><b>Objective:</b> To explore the value of the artificial intelligence (AI)-assisted recognition system in the detection quality of colonoscopy. <b>Methods:</b> From January 2023, the data on 700 patients who underwent colonoscopy in the Digestive Endoscopy Center of the First Affiliated Hospital of Zhejiang Chinese Medical University were collected prospectively. Based on a computerized number method, patients were divided into the AI assistance group and control group. The detection rate of adenomas (ADR) and polyps, number and size of adenomas, Boston bowel preparation scale (BBPS), intubation time, withdrawal time, and cecal intubation rate were compared between groups. Normally distributed data were analyzed with the <i>t</i>-test for independent samples. Non-normally distributed data were analyzed with the Rank sum test. Categorical data were analyzed with the Chi-square test. <b>Results:</b> In total, 691 patients were included in the analysis. According to the intention to treat (ITT) analysis and per-protocol (PP) analysis, the withdrawal time of the AI group was higher than that of the control group (ITT:436 (305, 620) vs 368 (265, 510) s, <i>Z</i>=-4.24, <i>P</i><0.001;PP:439 (306, 618) vs 364 (262, 500) s,<i>t</i>=-4.50, <i>P</i><0.001); however, there were no significant differences in the ADR (ITT:123(35.5%) vs 111(32.2%), <i>χ</i><sup>2</sup>=0.88, <i>P</i>=0.349;PP:108(34.2%) vs 99(31.1%), <i>χ</i><sup>2</sup>=0.67, <i>P</i>=0.414), the number of adenomas (ITT:0(0, 1) vs 0(0, 1),<i>Z</i>=-1.08, <i>P</i>=0.282;PP:0(0, 1) vs 0(0, 1),<i>Z</i>=-0.87, <i>P</i>=0.387), the polyp detection rate (ITT:85(24.6%) vs 85(24.6%),<i>χ</i><sup>2</sup>=0.001, <i>P</i>=0.983;PP:79(25.0%) vs 77(24.2%),<i>χ</i><sup>2</sup>=0.05, <i>P</i>=0.818), BBPS (ITT:6.5±0.9 vs 6.5±0.7,<i>t</i>=-0.59, <i>P</i>=0.555;PP:6.7±0.6 vs 6.6±0.6,<i>t</i>=-1.83, P=0.068), and cecal intubation rate (ITT:346(100.0%) vs 343(99.4%), <i>χ</i><sup>2</sup>=0.50, <i>P</i>=0.478) between these two groups. After excluding inadequate bowel preparation and failed cecal intubation cases, the AI-assisted system was found to significantly improve the detection rate of small adenomas (≤5 mm) (PP:27.8%(88/316)vs 21.1%(67/318), <i>χ</i><sup>2</sup>=3.94, <i>P</i>=0.047). <b>Conclusions:</b> The application of an AI-assisted system in colonoscopy can increase the withdrawal time and improve the detection rate of small adenomas.</p>\",\"PeriodicalId\":68309,\"journal\":{\"name\":\"中华内科杂志\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"中华内科杂志\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.3760/cma.j.cn112138-20240216-00109\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"中华内科杂志","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.3760/cma.j.cn112138-20240216-00109","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
目的探讨人工智能（AI）辅助识别系统在结肠镜检查检测质量中的价值。方法自 2023 年 1 月起，前瞻性地收集了在浙江中医药大学附属第一医院消化内镜中心接受结肠镜检查的 700 例患者的数据。根据计算机编号法，将患者分为人工智能辅助组和对照组。比较两组患者的腺瘤（ADR）和息肉检出率、腺瘤数量和大小、波士顿肠道准备量表（BBPS）、插管时间、撤管时间和盲肠插管率。正态分布数据采用独立样本 t 检验进行分析。非正态分布数据采用秩和检验进行分析。分类数据采用卡方检验进行分析。结果共有 691 名患者纳入分析。根据意向治疗（ITT）分析和每方案（PP）分析，人工智能组的停药时间高于对照组（ITT:436 (305, 620) vs 368 (265, 510) s, Z=-4.24，Pt=-4.50，Pχ2=0.88，P=0.349；PP:108（34.2%）vs 99（31.1%），χ2=0.67，P=0.414），腺瘤数目（ITT:0（0，1）vs 0（0，1）,Z=-1.08，P=0.282；PP：0（0，1） vs 0（0，1），Z=-0.87，P=0.387）、息肉检出率（ITT：85（24.6%） vs 85（24.6%），χ2=0.001，P=0.983；PP：79（25.0%） vs 77（24.2%），χ2=0.05，P=0.818）、BBPS（ITT：6.5±0.9 vs 6.5±0.7,t=-0.59, P=0.555；PP:6.7±0.6 vs 6.6±0.6,t=-1.83,P=0.068）和盲肠插管率（ITT:346(100.0%) vs 343(99.4%),χ2=0.50,P=0.478）。排除肠道准备不足和盲肠插管失败的病例后，发现人工智能辅助系统显著提高了小腺瘤（≤5 mm）的检出率（PP:27.8%(88/316)vs 21.1%(67/318)，χ2=3.94，P=0.047）。结论在结肠镜检查中应用人工智能辅助系统可以延长撤镜时间，提高小腺瘤的检出率。
[Effect of an artificial intelligence-assisted recognition system on colonoscopy quality].
Objective: To explore the value of the artificial intelligence (AI)-assisted recognition system in the detection quality of colonoscopy. Methods: From January 2023, the data on 700 patients who underwent colonoscopy in the Digestive Endoscopy Center of the First Affiliated Hospital of Zhejiang Chinese Medical University were collected prospectively. Based on a computerized number method, patients were divided into the AI assistance group and control group. The detection rate of adenomas (ADR) and polyps, number and size of adenomas, Boston bowel preparation scale (BBPS), intubation time, withdrawal time, and cecal intubation rate were compared between groups. Normally distributed data were analyzed with the t-test for independent samples. Non-normally distributed data were analyzed with the Rank sum test. Categorical data were analyzed with the Chi-square test. Results: In total, 691 patients were included in the analysis. According to the intention to treat (ITT) analysis and per-protocol (PP) analysis, the withdrawal time of the AI group was higher than that of the control group (ITT:436 (305, 620) vs 368 (265, 510) s, Z=-4.24, P<0.001;PP:439 (306, 618) vs 364 (262, 500) s,t=-4.50, P<0.001); however, there were no significant differences in the ADR (ITT:123(35.5%) vs 111(32.2%), χ2=0.88, P=0.349;PP:108(34.2%) vs 99(31.1%), χ2=0.67, P=0.414), the number of adenomas (ITT:0(0, 1) vs 0(0, 1),Z=-1.08, P=0.282;PP:0(0, 1) vs 0(0, 1),Z=-0.87, P=0.387), the polyp detection rate (ITT:85(24.6%) vs 85(24.6%),χ2=0.001, P=0.983;PP:79(25.0%) vs 77(24.2%),χ2=0.05, P=0.818), BBPS (ITT:6.5±0.9 vs 6.5±0.7,t=-0.59, P=0.555;PP:6.7±0.6 vs 6.6±0.6,t=-1.83, P=0.068), and cecal intubation rate (ITT:346(100.0%) vs 343(99.4%), χ2=0.50, P=0.478) between these two groups. After excluding inadequate bowel preparation and failed cecal intubation cases, the AI-assisted system was found to significantly improve the detection rate of small adenomas (≤5 mm) (PP:27.8%(88/316)vs 21.1%(67/318), χ2=3.94, P=0.047). Conclusions: The application of an AI-assisted system in colonoscopy can increase the withdrawal time and improve the detection rate of small adenomas.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
