目的 调查深圳地区综合性医院门诊幽门螺杆菌（Hp）对8种常见抗菌药物的耐药情况。方法 采集13C呼气试验阳性的患者胃黏膜标本313例，进行Hp分离培养及抗菌药物敏感性试验。结果 313例患者分离培养得到247例Hp菌株，培养阳性率78.91%，不同性别、不同年龄患者Hp分离培养阳性率比较差异无统计学意义（P＞0.05）。Hp对甲硝唑、克拉霉素、左氧氟沙星、利福平、阿莫西林、四环素、呋喃唑酮、庆大霉素耐药率依次为88.66%（219/247）、38.46%（95/247）、38.06%（94/247）、4.05%（10/247）、1.21%（3/247）、0.40%（1/247）、0.40%（1/247）、0（0/247）。双重耐药率为38.46%（95/247），其中Hp对克拉霉素+甲硝唑组合耐药率最高（18.62%，46/247），对甲硝唑+左氧氟沙星耐药率居其次（17.00%，42/247）。多重耐药率为19.84%（49/247）。不同年龄、性别患者双重耐药率、多重耐药率比较差异均无统计学意义（P＞0.05）。结论 深圳地区分离的Hp菌株对甲硝唑、克拉霉素、左氧氟沙星耐药率相对更高，且双重耐药、多重耐药情况严重。
Objective To investigate the antibiotic resistance of Helicobacter pylori（Hp）to eight commonly used antibiotics in outpatients of general hospitals in Shenzhen．Methods Gastric mucosal samples were collected from 313 patients who tested positive for the 13C breath test，and Hp strains were isolated and cultured．Antibiotic susceptibility testing was performed on the isolated Hp strains．Results Of the 313 patients，247 Hp strains were isolated，with a culture-positive rate of 78.91%．There was no significant difference in culture-positive rates between different genders and age groups（P＞0.05）．The resistance rates to metronidazole，clarithromycin，levofloxacin，rifampicin，amoxicillin，tetracycline，furazolidone，and gentamicin were 88.66%（219/247），38.46%（95/247），38.06%（94/247），4.05%（10/247），1.21%（3/247），0.40%（1/247），0.40%（1/247），0（0/247），respectively．The dual resistance rate was 38.46%（95/247），with the highest combination resistance observed in clarithromycin + metronidazole（18.62%，46/247），followed by metronidazole + levofloxacin（17.00%，42/247）．The multi-drug resistance rate was 19.84%（49/247）．There were no significant differences in dual resistance rates（P＞0.05）or multiple resistance rates（P＞0.05）between different age groups and genders．Conclusions The Hp strains isolated in Shenzhen exhibited relatively higher resistance rates to metronidazole，clarithromycin，and levofloxacin，with substantial dual and multi-drug resistance．
传统的结肠镜检查质量评估方式具有主观性强、费时费力等缺点。近年来,人工智能（AI）技术在结肠镜检查质量控制方面展现出客观性、即时性、全面性等优势,具有广阔的应用前景。文章全面探讨了AI在结肠镜检查质量控制中的多个应用场景,包括评估肠道准备质量、记录退镜时间、息肉识别和分类、预测早期结直肠癌浸润深度等方面,并通过具体的研究案例和数据分析了AI技术的有效性和优势。AI技术有望在提升结肠镜检查质量、促进结直肠癌的早诊早治方面发挥更加重要的作用,但面对技术、伦理及法规等多方面的挑战,未来需要持续努力,不断优化算法,加强跨学科合作,推动AI技术在医疗领域的健康、快速发展。
Traditional colonoscopy quality assessment methods have strong subjectivity and are time-consuming．In recent years,artificial intelligence technology has shown objectivity,timeliness,and comprehensiveness in colonoscopy quality control,with broad application prospects．This article comprehensively explores multiple application scenarios of AI in colonoscopy quality control,encompassing assessments of bowel preparation quality,recording of withdrawal times,polyp identification and classification,and prediction of early colorectal cancer invasion depth．Through specific research cases and data analysis,the effectiveness and advantages of AI technology are elucidated．AI technology is expected to play an increasingly significant role in enhancing the quality of colonoscopy and promoting early diagnosis and treatment of colorectal cancer．However,facing challenges from technology,ethics,regulations,and other aspects,continued efforts are needed in the future to continuously optimize algorithms,strengthen interdisciplinary collaboration,and promote the healthy and rapid development of AI technology in the medical field．
传统的结肠镜检查质量评估方式具有主观性强、费时费力等缺点。近年来，人工智能（AI）技术在结肠镜检查质量控制方面展现出客观性、即时性、全面性等优势，具有广阔的应用前景。文章全面探讨了AI在结肠镜检查质量控制中的多个应用场景，包括评估肠道准备质量、记录退镜时间、息肉识别和分类、预测早期结直肠癌浸润深度等方面，并通过具体的研究案例和数据分析了AI技术的有效性和优势。AI技术有望在提升结肠镜检查质量、促进结直肠癌的早诊早治方面发挥更加重要的作用，但面对技术、伦理及法规等多方面的挑战，未来需要持续努力，不断优化算法，加强跨学科合作，推动AI技术在医疗领域的健康、快速发展。
Traditional colonoscopy quality assessment methods have strong subjectivity and are time-consuming．In recent years，artificial intelligence technology has shown objectivity，timeliness，and comprehensiveness in colonoscopy quality control，with broad application prospects．This article comprehensively explores multiple application scenarios of AI in colonoscopy quality control，encompassing assessments of bowel preparation quality，recording of withdrawal times，polyp identification and classification，and prediction of early colorectal cancer invasion depth．Through specific research cases and data analysis，the effectiveness and advantages of AI technology are elucidated．AI technology is expected to play an increasingly significant role in enhancing the quality of colonoscopy and promoting early diagnosis and treatment of colorectal cancer．However，facing challenges from technology，ethics，regulations，and other aspects，continued efforts are needed in the future to continuously optimize algorithms，strengthen interdisciplinary collaboration，and promote the healthy and rapid development of AI technology in the medical field．
