The role of artificial intelligence in the endoscopic diagnosis of esophageal cancer: a systematic review and meta-analysis

被引:14
作者
Guidozzi, Nadia [1 ]
Menon, Nainika [2 ]
Chidambaram, Swathikan [3 ]
Markar, Sheraz Rehan [2 ,4 ,5 ]
机构
[1] Univ Witwatersrand, Dept Gen Surg, Johannesburg, South Africa
[2] Oxford Univ Hosp, Dept Gen Surg, Oxford, England
[3] Imperial Coll London, St Marys Hosp, Dept Surg & Canc, Acad Surg Unit, London, England
[4] Univ Oxford, Nuffield Dept Surg, Oxford, England
[5] Churchill Hosp, Nuffield Dept Surg, Old Rd, Oxford OX3 7LE, England
关键词
cancer screening; endoscopic imaging; esophageal cancers; robotics; RESOLUTION MICROENDOSCOPIC IMAGES; QUANTITATIVE-ANALYSIS; BARRETTS-ESOPHAGUS; CELL-CARCINOMA; NEOPLASIA;
D O I
10.1093/dote/doad048
中图分类号
R57 [消化系及腹部疾病];
学科分类号
摘要
Early detection of esophageal cancer is limited by accurate endoscopic diagnosis of subtle macroscopic lesions. Endoscopic interpretation is subject to expertise, diagnostic skill, and thus human error. Artificial intelligence (AI) in endoscopy is increasingly bridging this gap. This systematic review and meta-analysis consolidate the evidence on the use of AI in the endoscopic diagnosis of esophageal cancer. The systematic review was carried out using Pubmed, MEDLINE and Ovid EMBASE databases and articles on the role of AI in the endoscopic diagnosis of esophageal cancer management were included. A meta-analysis was also performed. Fourteen studies (1590 patients) assessed the use of AI in endoscopic diagnosis of esophageal squamous cell carcinoma-the pooled sensitivity and specificity were 91.2% (84.3-95.2%) and 80% (64.3-89.9%). Nine studies (478 patients) assessed AI capabilities of diagnosing esophageal adenocarcinoma with the pooled sensitivity and specificity of 93.1% (86.8-96.4) and 86.9% (81.7-90.7). The remaining studies formed the qualitative summary. AI technology, as an adjunct to endoscopy, can assist in accurate, early detection of esophageal malignancy. It has shown superior results to endoscopists alone in identifying early cancer and assessing depth of tumor invasion, with the added benefit of not requiring a specialized skill set. Despite promising results, the application in real-time endoscopy is limited, and further multicenter trials are required to accurately assess its use in routine practice.
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页数:8
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共 46 条
[1]   Development and validation of artificial neural networks model for detection of Barrett?s neoplasia: a multicenter pragmatic nonrandomized trial (with video) [J].
Abdelrahim, Mohamed ;
Saiko, Masahiro ;
Maeda, Naoto ;
Hossain, Ejaz ;
Alkandari, Asma ;
Subramaniam, Sharmila ;
Parra-Blanco, Adolfo ;
Sanchez-Yague, Andres ;
Coron, Emmanuel ;
Repici, Alessandro ;
Bhandari, Pradeep .
GASTROINTESTINAL ENDOSCOPY, 2023, 97 (03) :422-434
[2]   Using a deep learning system in endoscopy for screening of early esophageal squamous cell carcinoma (with video) [J].
Cai, Shi-Lun ;
Li, Bing ;
Tan, Wei-Min ;
Niu, Xue-Jing ;
Yu, Hon-Ho ;
Yao, Li-Qing ;
Zhou, Ping-Hong ;
Yan, Bo ;
Zhong, Yun-Shi .
GASTROINTESTINAL ENDOSCOPY, 2019, 90 (05) :745-+
[3]   Deep learning algorithm detection of Barrett's neoplasia with high accuracy during live endoscopic procedures: a pilot study [J].
de Groof, Albert J. ;
Struyvenberg, Maarten R. ;
Fockens, Kiki N. ;
van der Putten, Joost ;
van der Sommen, Fons ;
Boers, Tim G. ;
Zinger, Sveta ;
Bisschops, Raf ;
de With, Peter H. ;
Pouw, Roos E. ;
Curvers, Wouter L. ;
Schoon, Erik J. ;
Bergman, Jacques J. G. H. M. .
GASTROINTESTINAL ENDOSCOPY, 2020, 91 (06) :1242-1250
[4]   Deep-Learning System Detects Neoplasia in Patients With Barrett's Esophagus With Higher Accuracy Than Endoscopists in a Multistep Training and Validation Study With Benchmarking [J].
de Groof, Albert J. ;
Struyvenberg, Maarten R. ;
van der Putten, Joost ;
van der Sommen, Fons ;
Fockens, Kiki N. ;
Curvers, Wouter L. ;
Zinger, Sveta ;
Pouw, Roos E. ;
Coron, Emmanuel ;
Baldaque-Silva, Francisco ;
Pech, Oliver ;
Weusten, Bas ;
Meining, Alexander ;
Neuhaus, Horst ;
Bisschops, Raf ;
Dent, John ;
Schoon, Erik J. ;
de With, Peter H. ;
Bergman, Jacques J. .
GASTROENTEROLOGY, 2020, 158 (04) :915-+
[5]   The Argos project: The development of a computer-aided detection system to improve detection of Barrett's neoplasia on white light endoscopy [J].
de Groof, Jeroen ;
van der Sommen, Fons ;
van der Putten, Joost ;
Struyvenberg, Maarten R. ;
Zinger, Sveta ;
Curvers, Muter L. ;
Pech, Oliver ;
Meining, Alexander ;
Neuhaus, Horst ;
Bisschops, Raf ;
Schoon, Erik J. ;
de With, Peter H. ;
Bergman, Jacques J. .
UNITED EUROPEAN GASTROENTEROLOGY JOURNAL, 2019, 7 (04) :538-547
[6]   Endoscopic prediction of submucosal invasion in Barrett's cancer with the use of artificial intelligence: a pilot study [J].
Ebigbo, Alanna ;
Mendel, Robert ;
Rueckert, Tobias ;
Schuster, Laurin ;
Probst, Andreas ;
Manzeneder, Johannes ;
Prinz, Friederike ;
Mende, Matthias ;
Steinbrueck, Ingo ;
Faiss, Siegbert ;
Rauber, David ;
de Souza, Luis A. ;
Papa, Joao P. ;
Deprez, Pierre H. ;
Oyama, Tsuneo ;
Takahashi, Akiko ;
Seewald, Stefan ;
Sharma, Prateek ;
Byrne, Michael F. ;
Palm, Christoph ;
Messmann, Helmut .
ENDOSCOPY, 2021, 53 (09) :878-883
[7]   Real-time use of artificial intelligence in the evaluation of cancer in Barrett's oesophagus [J].
Ebigbo, Alanna ;
Mendel, Robert ;
Probst, Andreas ;
Manzeneder, Johannes ;
Prinz, Friederike ;
de Souza, Luis A., Jr. ;
Papa, Joao ;
Palm, Christoph ;
Messmann, Helmut .
GUT, 2020, 69 (04) :615-616
[8]   Computer-aided diagnosis using deep learning in the evaluation of early oesophageal adenocarcinoma [J].
Ebigbo, Alanna ;
Mendel, Robert ;
Probst, Andreas ;
Manzeneder, Johannes ;
de Souza, Luis Antonio, Jr. ;
Papa, Joao P. ;
Palm, Christoph ;
Messmann, Helmut .
GUT, 2019, 68 (07) :1143-U222
[9]   Comparison of performances of artificial intelligence versus expert endoscopists for real-time assisted diagnosis esophageal sauamous cell carcinoma (with video) [J].
Fukuda, Hiromu ;
Ishihara, Ryu ;
Kato, Yusuke ;
Matsunaga, Takashi ;
Nishida, Tsutomu ;
Yamada, Takuya ;
Ogiyama, Hideharu ;
Horie, Mai ;
Kinoshita, Kazuo ;
Tada, Tomohiro .
GASTROINTESTINAL ENDOSCOPY, 2020, 92 (04) :848-855
[10]   Intrapapillary capillary loop classification in magnification endoscopy: open dataset and baseline methodology [J].
Garcia-Peraza-Herrera, Luis C. ;
Everson, Martin ;
Lovat, Laurence ;
Wang, Hsiu-Po ;
Wang, Wen Lun ;
Haidry, Rehan ;
Stoyanov, Danail ;
Ourselin, Sebastien ;
Vercauteren, Tom .
INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY, 2020, 15 (04) :651-659