人工智能对内镜医师染色放大内镜下胃癌识别能力的影响研究
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1.北京大学肿瘤医院暨北京市肿瘤防治研究所,恶性肿瘤发病机制及转化研究教育部重点实验室内镜中心;2.武汉大学人民医院消化内科;3.消化系统疾病湖北省重点实验室;4.湖北省消化疾病微创诊治医学临床研究中心;5.武汉市中心医院消化内科;6.武汉市第一医院消化内科;7.武汉市第三医院消化内科;8.武汉市第八医院消化内镜中心

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基金项目:

国家自然科学基金(81672387);首都卫生发展科研专项(2020-2-2155);湖北省消化疾病微创诊治医学临床研究中心项目(2018BCC337);湖北省重大科技创新项目(2018-916-000-008)


Influence of artificial intelligence on endoscopists' performance in diagnosing gastric cancer by magnifying narrow banding imaging
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Key Laboratory of Carcinogenesis and Translational Research Ministry of Education,Endoscopy Center,Peking University Cancer Hospital and Institute

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National Natural Science Foundation of China (81672387); Capital Scientific Research Program of Development (2020-2-2155);Project of Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision (2018BCC337); Hubei Province Major Science and Technology Innovation Project (2018-916-000-008)

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    摘要:

    目的评估人工智能(artificial intelligence,AI)辅助胃癌诊断系统在实时染色放大内镜视频中对内镜医师识别胃癌能力的影响。方法回顾性收集2017年3月—2020年1月武汉大学人民医院和公开数据集中的早期胃癌和非癌染色放大内镜图片作为训练集和独立测试集,其中训练集包括4 667张图片(1 950张早期胃癌和2 717张非癌),测试集包括1 539张图片(483张早期胃癌和1 056张非癌)。利用深度学习进行模型训练。前瞻性收集2020年6月9日—2020年11月17日来自北京大学肿瘤医院和武汉大学人民医院的100例患者的染色放大内镜视频(包含38例癌和62例非癌)作为视频测试集。纳入来自另外4家医院的4名不同年资内镜医师,分2次(无或有AI辅助)对视频测试集进行诊断,评估AI对内镜医师判断胃癌能力的影响。结果无AI辅助时,内镜医师诊断视频测试集中胃癌的准确率、敏感度和特异度分别为81.00%±4.30%、71.05%±9.67%和87.10%±10.88%;在AI辅助下,内镜医师辨认胃癌的准确率、敏感度和特异度分别为86.50%±2.06%、84.87%±11.07%和87.50%±4.47%,诊断准确率(P=0.302)和敏感度(P=0.180)较无AI辅助时均有提升。AI在视频测试集中辨认胃癌的准确率为88.00%(88/100),敏感度为97.37%(37/38),特异度为82.26%(51/62),AI的敏感度高于内镜医师平均水平(P=0.002)。结论AI辅助诊断系统是染色放大内镜模式下辅助诊断胃癌的有效工具,可提高内镜医师对胃癌的诊断能力。它能实时提醒内镜医师关注高风险区域,以降低漏诊率。

    Abstract:

    ObjectiveTo assess the influence of an artificial intelligence (AI) -assisted diagnosis system on the performance of endoscopists in diagnosing gastric cancer by magnifying narrow banding imaging (M-NBI). MethodsM-NBI images of early gastric cancer (EGC) and non-gastric cancer from Renmin Hospital of Wuhan University from March 2017 to January 2020 and public datasets were collected, among which 4 667 images (1 950 images of EGC and 2 717 of non-gastric cancer)were included in the training set and 1 539 images (483 images of EGC and 1 056 of non-gastric cancer) composed a test set. The model was trained using deep learning technique. One hundred M-NBI videos from Beijing Cancer Hospital and Renmin Hospital of Wuhan University between 9 June 2020 and 17 November 2020 were prospectively collected as a video test set, 38 of gastric cancer and 62 of non-gastric cancer. Four endoscopists from four other hospitals participated in the study, diagnosing the video test twice,with and without AI. The influence of the system on endoscopists' performance was assessed. ResultsWithout AI assistance, accuracy, sensitivity, and specificity of endoscopists' diagnosis of gastric cancer were 81.00%±4.30%, 71.05%±9.67%, and 87.10%±10.88%, respectively. With AI assistance, accuracy, sensitivity and specificity of diagnosis were 86.50%±2.06%, 84.87%±11.07%, and 87.50%±4.47%, respectively. Diagnostic accuracy (P=0.302) and sensitivity (P=0.180) of endoscopists with AI assistance were improved compared with those without. Accuracy, sensitivity and specificity of AI in identifying gastric cancer in the video test set were 88.00% (88/100), 97.37% (37/38), and 82.26% (51/62), respectively. Sensitivity of AI was higher than that of the average of endoscopists (P=0.002). ConclusionAI-assisted diagnosis system is an effective tool to assist diagnosis of gastric cancer in M-NBI, which can improve the diagnostic ability of endoscopists. It can also remind endoscopists of high-risk areas in real time to reduce the probability of missed diagnosis.

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王警,朱益洁,吴练练,等.人工智能对内镜医师染色放大内镜下胃癌识别能力的影响研究[J].中华消化内镜杂志,2021,38(10):783-788.

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  • 收稿日期:2021-01-10
  • 最后修改日期:2021-09-07
  • 录用日期:2021-02-01
  • 在线发布日期: 2021-11-04
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