| 493 | 3 | 43 |
| 下载次数 | 被引频次 | 阅读次数 |
目的：旨在系统评估基于深度学习的智能辅助内镜诊断模型(Intelligence-assisted endoscopic diagnosis model based on deep learning,DL-IEDM)对萎缩性胃炎和肠上皮化生的诊断效果。方法：系统检索PubMed、Embase、Web of Science、Cochrane Library、CNKI、维普及万方等中英文数据库中有关DL-IEDM诊断萎缩性胃炎和肠上皮化生的研究。纳入的研究根据诊断准确性试验质量评价工具-2进行质量评价，通过Rev Man 5.4、Meta-Disc1.4和Stata17.0软件计算合并后的敏感性、特异性、阳性似然比、阴性似然比、诊断优势比等诊断效能评价指标，并和内镜医师的诊断性能进行比较。结果：纳入的13项研究中，总共图片14 447张，其中萎缩性胃炎的图片为6 985张，肠上皮化生的图片为1 073张。Meta分析后DL-IEDM诊断癌前疾病萎缩性胃炎和肠上皮化生的灵敏度、特异度、阳性似然比、阴性似然比、诊断优势比分别为0.93(95%CI,0.91～0.94)、0.90(95%CI,0.86～0.93)、9.2(95%CI,6.5～13.2)、0.08(95%CI,0.07～0.10)、111(95%CI,71～174)，综合受试者工作特征曲线的曲线下面积(Area under the curve, AUC)为0.96(95%CI,0.94～0.97)。经过亚组和回归分析发现(1)DL-IEDM在内镜中识别萎缩性胃炎和肠上皮化生的AUC值分别为0.96(95%CI,0.94～0.97)、0.95(95%CI,0.93～0.97)，两者无显著差异性；(2)使用白光内镜图像构建的DL-IEDM在识别萎缩性胃炎和肠上皮化生的性能优于使用图像增强内镜构建的DL-IEDM，差异具有统计学意义(χ2=32.53,P<0.05);3)内镜视频参与模型的训练或验证可提高DL-IEDM的诊断性能(χ2=7.47,P<0.05)。最后，将DL-IEDM的AUC值与内镜专家和内镜初学者的AUC值进行相互比较发现，DL-IEDM的诊断效能显著高于内镜专家和内镜初学者(AUC=0.96,AUC=0.91,AUC=0.78)，差异有统计学意义(Z=3.361,P<0.001和Z=9.265, P<0.000 1)。结论：DL-IEDM对癌前疾病萎缩性胃炎和肠上皮化生具有较高的诊断准确性。除此之外，DL-IEDM可以显著提高内镜医师的诊疗水平，尤其是内镜初学者。然而，为了深度学习技术能够在临床得到更为全面的推广应用，尚需更多大样本、多中心、前瞻性的研究证实。
Abstract：Objective: To systematically evaluate the effectiveness of an intelligence-assisted endoscopic diagnosis model based on deep learning(DL-IEDM) in identifying atrophic gastritis and intestinal metaplasia.Methods: Studies that investigated the diagnosis of atrophic gastritis and intestinal metaplasia using DL-IEDM were collected from English databases(PubMed, Embase, Web of Science, and Cochrane Library) and Chinese databases(CNKI、VIP and Wanfang). The quality of these studies was evaluated using the Quality Assessment of Diagnostic Accuracy Studies-2(QUADAS-2) tool. Sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio, and other evaluation metrics were calculated using Rev Man 5.4、Meta-Disc1.4 and Stata17.0 software. The results were also compared to the performance of endoscopists.Results:A total of 13 studies were selected, comprising a total of 14 447 images, including 6 985 images of atrophic gastritis and 1 073 images of intestinal metaplasia. After conducting a meta-analysis, it was found that DL-IEDM has a sensitivity of 0.93(95%CI,0.91-0.94), specificity of 0.90(95%CI,0.86-0.93), positive likelihood ratio of 9.2(95%CI,6.5-13.2), negative likelihood ratio of 0.08(95%CI,0.07-0.10), and diagnostic odds ratio of 111(95%CI,71-174) for diagnosing precancerous diseases GA and IM. Additionally, a Summary Receiver Operating Characteristic Curve was plotted, and the Area Under the Curve(AUC) was calculated to be 0.96(95% CI, 0.94-0.97). Subgroup and regression analysis showed that:(1) there was no distinct difference in the AUC value between DL-IEDM diagnose atrophic gastritis and intestinal metaplasia, with values of 0.96(95% CI, 0.94-0.97) and 0.95(95% CI, 0.93-0.97), respectively;(2) the performance of DL-IEDM based on white light endoscope images was superior to that relying on image-enhance endoscope in identifying atrophic gastritis and intestinal metaplasia, and the difference was statistically significant （χ2=32.53, P<0.05）;(3) Video participation in model training or validation can improve the diagnostic performance of DL-IEDM（χ2=7.47, P<0.05）. Finally, compared with expert and novice endoscopists, the AUC value of DL-IEDM was outstanding(AUC=0.96, AUC=0.91, AUC=0.78, respectively), and the difference was statistically significant(Z=3.361, P<0.001 and Z=9.265, P<0.000 1). Conlusion: DL-IEDM shows high diagnostic accuracy for precancerous diseases such as atrophic gastritis and intestinal metaplasia. Furthermore, it improves the diagnostic and treatment levels of endoscopists, especially those who are inexperienced. However, to promote the application of deep learning technology in clinical practice, more large sample, multicent and prospective studies are needed.
1 Zheng R, Zhang S, Zeng H, et al. Cancer incidence and mortality in China, 2016[J]. J Natl Cancer Cent, 2022,2(1):1-9.
2 Sung H, Ferlay J, Siegel R L, et al. Global cancer statistics 2020:GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J].CA Cancer J Clin, 2021, 71(3):209-249.
3 Niu P, Huang H, Zhao L, et al. Clinicopathological characteristics, survival outcomes, and genetic alterations of younger patients with gastric cancer:Results from the China National Cancer Center and cBioPortal datasets[J]. Cancer Med, 2022, 11(16):3057-3073.
4 Correa P, Piazuelo MB. The gastric precancerous cascade[J]. J Dig Dis, 2012, 13(1):2-9.
5 Masuyama H, Yoshitake N, Sasai T, et al. Relationship between the degree of endoscopic atrophy of the gastric mucosa and carcinogenic risk[J]. Digestion, 2015,91(1):30-36.
6 De Vries AC, Haringsma J, Kuipers EJ. The detection,surveillance and treatment of premalignant gastric lesions related to Helicobacter pylori infection[J]. Helicobacter, 2007, 12(1):1-15.
7 Wang R, Chen XZ. Prevalence of atrophic gastritis in southwest China and predictive strength of serum gastrin-17:A cross-sectional study(SIGES)[J]. Sci Rep,2020, 10(1):4523.
8 Imaeda A. Confocal laser endomicroscopy for the detection of atrophic gastritis:A new application for confocal endomicroscopy?[J]. J Clin Gastroenterol, 2015, 49(5):355-357.
9 East JE, Vleugels JL, Roelandt P, et al. Advanced endoscopic imaging:European Society of Gastrointestinal Endoscopy(ESGE)Technology Review[J]. Endoscopy, 2016, 48(11):1029-1045.
10 Barbeiro S, Libanio D, Castro R, et al. Narrow-band imaging:Clinical application in gastrointestinal endoscopy[J]. GE Port J Gastroenterol, 2018, 26(1):40-53.
11 Lecun Y, Bengio Y, Hinton G. Deep learning[J]. Nature, 2015, 521(7553):436-444.
12 Jiang Y, Zhang Z, Yuan Q, et al. Predicting peritoneal recurrence and disease-free survival from CT images in gastric cancer with multitask deep learning:A retrospective study[J]. Lancet Digit Health, 2022, 4(5):e340-e350.
13 Liu Y, Jain A, Eng C, et al. A deep learning system for differential diagnosis of skin diseases[J]. Nat Med,2020, 26(6):900-908.
14 Cao C, Wang R, Yu Y, et al. Gastric polyp detection in gastroscopic images using deep neural network[J].PLoS One, 2021, 16(4):e0250632.
15 Dilaghi E, Lahner E, Annibale B, et al. Systematic review and meta-analysis:Artificial intelligence for the diagnosis of gastric precancerous lesions and Helicobacter pylori infection[J]. Dig Liver Dis, 2022, 54(12):1630-1638.
16 Mcinnes MDF, Moher D, Thombs BD, et al. Preferred reporting items for a systematic review and meta-analysis of diagnostic test accuracy studies:The PRISMA-DTA statement[J]. JAMA, 2018, 319(4):388-396.
17 Xu M, Zhou W, Wu L, et al. Artificial intelligence in the diagnosis of gastric precancerous conditions by image-enhanced endoscopy:A multicenter, diagnostic study(with video)[J]. Gastrointest Endosc, 2021, 94(3):540-548 e544.
18 Wong PK, Yao L, Yan T, et al. Broad learning system stacking with multi-scale attention for the diagnosis of gastric intestinal metaplasia[J]. Biomed Signal Process Contr, 2022, 73.
19 Chong Y, Xie N, Liu X, et al. A deep learning network based on multi-scale and attention for the diagnosis of chronic atrophic gastritis[J]. Z Gastroenterol, 2022, 60(12):1770-1778.
20 Yan T, Wong PK, Choi IC, et al. Intelligent diagnosis of gastric intestinal metaplasia based on convolutional neural network and limited number of endoscopic images[J]. Comput Biol Med, 2020, 126.
21 Guimaraes P, Keller A, Fehlmann T, et al.Deep-learning based detection of gastric precancerous conditions[J]. Gut, 2020, 69(1):4-6.
22 Zhang Y, Li F, Yuan F, et al. Diagnosing chronic atrophic gastritis by gastroscopy using artificial intelligence[J]. Dig Liver Dis, 2020, 52(5):566-572.
23 Luo J, Cao S, Ding N, et al. A deep learning method to assist with chronic atrophic gastritis diagnosis using white light images[J]. Dig Liver Dis, 2022, 54(11):1513-1519.
24 Yang J, Ou Y, Chen Z, et al. A benchmark dataset of endoscopic images and novel deep learning method to detect intestinal metaplasia and gastritis atrophy[J]. IEEE J Biomed Health Inform, 2023,27(1):7-16.
25 Zhao Q, Jia Q, Chi T. Deep learning as a novel method for endoscopic diagnosis of chronic atrophic gastritis:A prospective nested case-control study[J]. BMC Gastroenterol, 2022, 22(1):352.
26 Siripoppohn V, Pittayanon R, Tiankanon K, et al.Real-time semantic segmentation of gastric intestinal metaplasia using a deep learning approach[J]. Clin Endosc, 2022, 55(3):390-400.
27 华雯俏，管鑫，姜相君.基于人工智能的胃镜图像辅助识别模型在慢性萎缩性胃炎诊断中的临床研究[J].胃肠病学，2020,25(10):588-593.
28 Shi Y, Wei N, Wang K, et al. Deep learning-assisted diagnosis of chronic atrophic gastritis in endoscopy[J].Front Oncol, 2023,13:1122247.
29 牛占岳，于红刚，张静等.基于卷积神经网络的辅助识别模型在白光内镜诊断慢性胃炎中的应用价值[J].胃肠病学，2021,26(12):738-743.
30 Marques-Silva L, Areia M, Elvas L, et al. Prevalence of gastric precancerous conditions[J]. Euro J Gastroenterol Hepatol, 2014, 26(4):378-387.
31 Fang JY, Du YQ, Liu WZ, et al. Chinese consensus on chronic gastritis(2017, Shanghai)[J]. J Dig Dis,2018, 19(4):182-203.
32 Zhang SM, Wang YJ, Zhang ST. Accuracy of artificial intelligence-assisted detection of esophageal cancer and neoplasms on endoscopic images:A systematic review and meta-analysis[J]. J Dig Dis, 2021, 22(6):318-328.
33 Misawa M, Kudo SE, Mori Y, et al. Artificial intelligence-assisted polyp detection for colonoscopy:Initial experience[J]. Gastroenterology, 2018, 154(8):2027-2029.e2023.
34 Hu ZL, Tang JS, Wang ZM, et al. Deep learning for image-based cancer detection and diagnosis-a survey[J].Patt Recognit, 2018, 83:134-149.
35 Bang CS, Lim H, Jeong HM, et al. Use of endoscopic images in the prediction of submucosal invasion of gastric neoplasms:Automated deep learning model development and usability study[J]. J Med Internet Res, 2021,23 (4):e25167.
36 Alrowais F,S Alotaibi S,Marzouk R, et al. Manta ray foraging optimization transfer learning-based gastric cancer diagnosis and classification on endoscopic images[J]. Cancers(Basel), 2022, 14(22):5661.
37 Pham H, Guan MY, Zoph B, et al. Efficient neural architecture search via parameter sharing[J]. 2018.
基本信息:
DOI：10.13210/j.cnki.jhmu.20231026.001
中图分类号:R573.32
引用信息:
[1]康林,刘喜,程浩,等.深度学习在内镜中识别萎缩性胃炎和肠上皮化生的诊断效能：系统综述和Meta分析[J].海南医学院学报,2024,30(17):1335-1345.DOI:10.13210/j.cnki.jhmu.20231026.001.
基金信息:
陕西省重点研发计划（2021SF-182）~~
2023-10-26
2023-10-26
2023-10-26