目的探讨常规电子结肠镜检查中结肠息肉的漏诊情况,包括漏诊率、漏诊息肉大小、分型、部位和病理状况。方法回顾3年间在120 d 内接受多次结肠镜检查的结肠息肉患者的病例资料,记录患者前2次肠镜检查的息肉数,漏诊息肉数,计算漏诊率。结...目的探讨常规电子结肠镜检查中结肠息肉的漏诊情况,包括漏诊率、漏诊息肉大小、分型、部位和病理状况。方法回顾3年间在120 d 内接受多次结肠镜检查的结肠息肉患者的病例资料,记录患者前2次肠镜检查的息肉数,漏诊息肉数,计算漏诊率。结果符合要求的患者共143例,男92例,女51例,年龄23～82岁;漏诊息肉数共126枚,漏诊率22.5%;在漏诊的息肉中,<5mm、5～9 mm 和≥10 mm 息肉分别占80.2%、18.3%和1.6%,各项间比较差异有统计学意义(P<0.01);山田Ⅰ、Ⅱ、Ⅲ和Ⅳ型息肉分别占87.3%、8.7%、3.2%和0.8%,各项间比较差异有统计学意义(P<0.01);直肠、乙状结肠、降结肠、横结肠、肝曲、升结肠和盲肠分别占11.1%、27.0%、12.7%、19.1%、10.3%、15.1%和4.8%,各项间比较差异有统计学意义(P<0.01);漏诊的晚期腺瘤在漏诊的腺瘤性息肉中占14.8%;漏诊息肉数与基础息肉数间相关分析显示有显著的相关性(r=0.674,P<0.01)。结论常规电子结肠镜检查中结肠息肉有较高的漏诊率,各段结肠间漏诊率不同,且漏诊息肉绝大多数为<5 mm 的Ⅰ型息肉。患者罹患息肉越多,漏诊越多。展开更多
Artificial intelligence(AI) enables machines to provide unparalleled value in a myriad of industries and applications. In recent years, researchers have harnessed artificial intelligence to analyze large-volume, unstr...Artificial intelligence(AI) enables machines to provide unparalleled value in a myriad of industries and applications. In recent years, researchers have harnessed artificial intelligence to analyze large-volume, unstructured medical data and perform clinical tasks, such as the identification of diabetic retinopathy or the diagnosis of cutaneous malignancies. Applications of artificial intelligence techniques, specifically machine learning and more recently deep learning, are beginning to emerge in gastrointestinal endoscopy. The most promising of these efforts have been in computeraided detection and computer-aided diagnosis of colorectal polyps, with recent systems demonstrating high sensitivity and accuracy even when compared to expert human endoscopists. AI has also been utilized to identify gastrointestinal bleeding, to detect areas of inflammation, and even to diagnose certain gastrointestinal infections. Future work in the field should concentrate on creating seamless integration of AI systems with current endoscopy platforms and electronic medical records, developing training modules to teach clinicians how to use AI tools, and determining the best means for regulation and approval of new AI technology.展开更多
文摘目的探讨常规电子结肠镜检查中结肠息肉的漏诊情况,包括漏诊率、漏诊息肉大小、分型、部位和病理状况。方法回顾3年间在120 d 内接受多次结肠镜检查的结肠息肉患者的病例资料,记录患者前2次肠镜检查的息肉数,漏诊息肉数,计算漏诊率。结果符合要求的患者共143例,男92例,女51例,年龄23～82岁;漏诊息肉数共126枚,漏诊率22.5%;在漏诊的息肉中,<5mm、5～9 mm 和≥10 mm 息肉分别占80.2%、18.3%和1.6%,各项间比较差异有统计学意义(P<0.01);山田Ⅰ、Ⅱ、Ⅲ和Ⅳ型息肉分别占87.3%、8.7%、3.2%和0.8%,各项间比较差异有统计学意义(P<0.01);直肠、乙状结肠、降结肠、横结肠、肝曲、升结肠和盲肠分别占11.1%、27.0%、12.7%、19.1%、10.3%、15.1%和4.8%,各项间比较差异有统计学意义(P<0.01);漏诊的晚期腺瘤在漏诊的腺瘤性息肉中占14.8%;漏诊息肉数与基础息肉数间相关分析显示有显著的相关性(r=0.674,P<0.01)。结论常规电子结肠镜检查中结肠息肉有较高的漏诊率,各段结肠间漏诊率不同,且漏诊息肉绝大多数为<5 mm 的Ⅰ型息肉。患者罹患息肉越多,漏诊越多。
文摘Artificial intelligence(AI) enables machines to provide unparalleled value in a myriad of industries and applications. In recent years, researchers have harnessed artificial intelligence to analyze large-volume, unstructured medical data and perform clinical tasks, such as the identification of diabetic retinopathy or the diagnosis of cutaneous malignancies. Applications of artificial intelligence techniques, specifically machine learning and more recently deep learning, are beginning to emerge in gastrointestinal endoscopy. The most promising of these efforts have been in computeraided detection and computer-aided diagnosis of colorectal polyps, with recent systems demonstrating high sensitivity and accuracy even when compared to expert human endoscopists. AI has also been utilized to identify gastrointestinal bleeding, to detect areas of inflammation, and even to diagnose certain gastrointestinal infections. Future work in the field should concentrate on creating seamless integration of AI systems with current endoscopy platforms and electronic medical records, developing training modules to teach clinicians how to use AI tools, and determining the best means for regulation and approval of new AI technology.
