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Endoscopy | 2019年度被引次数前五论文推荐 2020-03-13

本期为您推荐Thieme医学期刊Endoscopy在2019年被引次数排名前五的论文。以武汉大学人民医院消化内科于红刚教授为通讯作者的论文A deep neural network improves endoscopic detection of early gastric cancer without blind spots入榜。免费阅读或下载本期推荐论文PDF版,请关注微信后回复"313"。

No.1

Management of epithelial precancerous conditions and lesions in the stomach (MAPS II): European Society of Gastrointestinal Endoscopy (ESGE), European Helicobacter and Microbiota Study Group (EHMSG), European Society of Pathology (ESP), and Sociedade Portuguesa de Endoscopia Digestiva (SPED) guideline update 2019

Pedro Pimentel-Nunes et al.

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| Fig. 2 Proposed management for patients with atrophic gastritis, gastric intestinal metaplasia, or gastric epithelial dysplasia. OLGA, Operative Link on Gastritis Assessment; OLGIM, Operative Link on Gastritis Assessment based on Intestinal Metaplasia.

No.2

Endoscopic treatment of chronic pancreatitis: European Society of Gastrointestinal Endoscopy (ESGE) Guideline – Updated August 2018

Jean-Marc Dumonceau et al.

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No.3

Peroral endoscopic myotomy and fundoplication: a novel NOTES procedure

Haruhiro Inoue et al.

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| Fig. 1 Conventional peroral endoscopic myotomy (POEM) is completed at the anterior wall of the esophagus. Next, the peritoneal cavity is accessed through the submucosal tunnel. a Schematic drawing of POEM with fundoplication (POEM + F) procedure (Step 1). The endoscope is advanced into the peritoneal cavity, just after passing the diaphragmatic crus.

No.4

Endoscopic grading of gastric intestinal metaplasia (EGGIM): a multicenter validation study

Gianluca Esposito et al.

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| Fig. 2 The green line represents the receiver operating characteristic (ROC) curve (with 95 % confidence intervals; blue lines) for endoscopic grading of gastric intestinal metaplasia (EGGIM) scores compared to operative link on gastric intestinal metaplasia (OLGIM) stages III and IV for the presence of extensive intestinal metaplasia, giving an area under the curve (AUC) of 0.96.

No.5

A deep neural network improves endoscopic detection of early gastric cancer without blind spots

Lianlian Wu, Honggang Yu et al.

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| Fig. 1 Flowchart of the data preparation and training/testing procedure of the deep convolutional neural network (DCNN) model. The functions of networks 1, 2, and 3 are filtering blurry images, early gastric cancer identification, and classification of gastric location, respectively. The three networks were independently trained. Blurry images and some clear images were used for the training of the network 1. Clear images were further classified into malignant or benign depending on the pathology evidence for the training and testing of network 2. Parallelly, clear images were classified into 10 or 26 gastric locations by two endoscopists with more than 10 years of esophagogastroduodenoscopy (EGD) experience for the training and testing of network 3. When running on the videos, all frames will be first filtered via network 1, so only clear images can enter into networks 2 and 3.

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Endoscopy

Issues per year: 12
Volume: 52
Year: 2020
ISSN: 0013-726X

影响因子 2018:6.381

Endoscopy是有关胃肠内窥镜检查国际发展和最新技术的重要期刊,出版高质量综述、原创研究、前瞻性研究、诊断和治疗进展有价值的调查、以及国内外一系列重要会议等,满足来自全球的内窥镜医生、外科医生、临床医生和科研人员的不同需求。本刊所有论文都经过严格的同行评审,每年出版 12 期,论文经常辅以在线视频内容。

Endoscopy 是欧洲消化内镜学会(ESGE)及其附属学会的官方期刊。