| 1 |
Sundar R, Nakayama I, Markar SR, et al. Gastric cancer [J]. Lancet, 2025, 405(10494): 2087-2102.
|
| 2 |
赫捷, 陈万青, 李兆申, 等. 中国胃癌筛查与早诊早治指南(2022, 北京) [J]. 中国肿瘤, 2022, 31(7): 488-527.
|
| 3 |
Sharma P, Hassan C. Artificial intelligence and deep learning for upper gastrointestinal neoplasia [J]. Gastroenterology, 2022, 162(4): 1056-1066.
|
| 4 |
Tang D, Wang L, Ling T, et al. Development and validation of a real-time artificial intelligence-assisted system for detecting early gastric cancer: a multicentre retrospective diagnostic study [J]. EBioMedicine, 2020, 62: 103146.
|
| 5 |
Dong Z, Tao X, Du H, et al. Exploring the challenge of early gastric cancer diagnostic AI system face in multiple centers and its potential solutions [J]. J Gastroenterol, 2023, 58(10): 978-989.
|
| 6 |
Ebigbo A, Messmann H, Lee SH. Artificial intelligence applications in image-based diagnosis of early esophageal and gastric neoplasms [J]. Gastroenterology, 2025, 169(3): 396-415.e2.
|
| 7 |
Wang Z, Liu Y, Niu X. Application of artificial intelligence for improving early detection and prediction of therapeutic outcomes for gastric cancer in the era of precision oncology [J]. Semin Cancer Biol, 2023, 93: 83-96.
|
| 8 |
Xu Y, Tan Y, Wang Y, et al. A gratifying step forward for the application of artificial intelligence in the field of endoscopy: a narrative review [J]. Surg Laparosc Endosc Percutan Tech, 2020, 31(2): 254-263.
|
| 9 |
Fu XY, Mao XL, Chen YH, et al. The feasibility of applying artificial intelligence to gastrointestinal endoscopy to improve the detection rate of early gastric cancer screening [J]. Front Med (Lausanne), 2022, 9: 886853.
|
| 10 |
王士旭, 柯岩, 王贵齐. 人工智能在上消化道内镜检查质量控制中的应用 [J]. 中国肿瘤临床, 2021, 48(23): 1215-1219.
|
| 11 |
彭东阁, 万子叶, 卢宁. 人工智能在胃癌诊疗和患者预后预测中的应用现状及未来展望 [J]. 中国癌症杂志, 2025, 35(5): 496-504.
|
| 12 |
张倩, 曹云太, 王志洁, 等. 深度学习在早期胃癌内镜图像诊断中的研究进展 [J]. 实用医学杂志, 2025, 41(14): 2160-2166.
|
| 13 |
Ono S, Kawada K, Dohi O, et al. Linked color imaging focused on neoplasm detection in the upper gastrointestinal tract : a randomized trial [J]. Ann Intern Med, 2021, 174(1): 18-24.
|
| 14 |
Ling T, Wu L, Fu Y, et al. A deep learning-based system for identifying differentiation status and delineating the margins of early gastric cancer in magnifying narrow-band imaging endoscopy [J]. Endoscopy, 2021, 53(5): 469-477.
|
| 15 |
Abe S. Computer-aided endoscopic diagnosis of early gastric cancer on white light endoscopy: No detection, no characterization [J]. Dig Endosc, 2023, 35(4): 492-493.
|
| 16 |
李夏, 于红刚. 内镜诊断早期胃癌的新进展 [J]. 海南医学院学报, 2019, 25(5): 392-395.
|
| 17 |
Ueyama H, Kato Y, Akazawa Y, et al. Application of artificial intelligence using a convolutional neural network for diagnosis of early gastric cancer based on magnifying endoscopy with narrow-band imaging [J]. J Gastroenterol Hepatol, 2021, 36(2): 482-489.
|
| 18 |
Abe S, Makiguchi ME, Nonaka S, et al. Emerging texture and color enhancement imaging in early gastric cancer [J]. Dig Endosc, 2022, 34(4): 714-720.
|
| 19 |
Min M, Sun X, Bai J, et al. Diagnostic accuracy of linked colour imaging versus white light imaging for early gastric cancers: a prospective, multicentre, randomized controlled trial study [J]. Ann Med, 2022, 54(1): 3306-3314.
|
| 20 |
Ikenoyama Y, Hirasawa T, Ishioka M, et al. Detecting early gastric cancer: comparison between the diagnostic ability of convolutional neural networks and endoscopists [J]. Dig Endosc, 2021, 33(1): 141-150.
|
| 21 |
Jin J, Zhang Q, Dong B, et al. Automatic detection of early gastric cancer in endoscopy based on Mask region-based convolutional neural networks (Mask R-CNN)(with video) [J]. Front Oncol, 2022, 12: 927868.
|
| 22 |
Wu L, He X, Liu M, et al. Evaluation of the effects of an artificial intelligence system on endoscopy quality and preliminary testing of its performance in detecting early gastric cancer: a randomized controlled trial [J]. Endoscopy, 2021, 53(12): 1199-1207.
|
| 23 |
Abe S, Tomizawa Y, Saito Y. Can artificial intelligence be your angel to diagnose early gastric cancer in real clinical practice? [J]. Gastrointest Endosc, 2022, 95(4): 679-681.
|
| 24 |
He X, Wu L, Dong Z, et al. Real-time use of artificial intelligence for diagnosing early gastric cancer by magnifying image-enhanced endoscopy: a multicenter diagnostic study (with videos) [J]. Gastrointest Endosc, 2022, 95(4): 671-678.e4.
|
| 25 |
Oh Y, Bae GE, Kim KH, et al. Multi-scale hybrid vision transformer for learning gastric histology: AI-based decision support system for gastric cancer treatment [J]. IEEE J Biomed Health Inform, 2023, 27(8): 4143-4153.
|
| 26 |
Tang D, Ni M, Zheng C, et al. A deep learning-based model improves diagnosis of early gastric cancer under narrow band imaging endoscopy [J]. Surg Endosc, 2022, 36(10): 7800-7810.
|
| 27 |
Deng Y, Qin HY, Zhou YY, et al. Artificial intelligence applications in pathological diagnosis of gastric cancer [J]. Heliyon, 2022, 8(12): e12431.
|
| 28 |
Ku Y, Ding H, Wang G. Efficient synchronous real-time CADe for multicategory lesions in gastroscopy by using multiclass detection model [J]. Biomed Res Int, 2022, 2022: 8504149.
|
| 29 |
Kim JH, Oh SI, Han SY, et al. An optimal artificial intelligence system for real-time endoscopic prediction of invasion depth in early gastric cancer [J]. Cancers (Basel), 2022, 14(23): 6000.
|
| 30 |
Uema R, Hayashi Y, Kizu T, et al. A novel artificial intelligence-based endoscopic ultrasonography diagnostic system for diagnosing the invasion depth of early gastric cancer [J]. J Gastroenterol, 2024, 59(7): 543-555.
|
| 31 |
Nam JY, Chung HJ, Choi KS, et al. Deep learning model for diagnosing gastric mucosal lesions using endoscopic images: development, validation, and method comparison [J]. Gastrointest Endosc, 2022, 95(2): 258-268.e10.
|
| 32 |
Zhu Y, Du L, Fu PY, et al. An automated video analysis system for retrospective assessment and real-time monitoring of endoscopic procedures (with Video) [J]. Bioengineering (Basel), 2024, 11(5): 445.
|
| 33 |
Goto A, Kubota N, Nishikawa J, et al. Cooperation between artificial intelligence and endoscopists for diagnosing invasion depth of early gastric cancer [J]. Gastric Cancer, 2023, 26(1): 116-1122.
|
| 34 |
吴邦钰, 王志霄, 项景轩, 等. 基于卷积神经网络的胃癌病理图像分类诊断与分级识别[J/OL]. 南京医科大学学报(自然科学版), 1-13[2026-2-4].
|
| 35 |
Pagani W, Buysse T, Dua KS. Digital platforms, virtual reality, and augmented reality in gastrointestinal endoscopy training [J]. Clin Endosc, 2025, 58(5): 653-661.
|
| 36 |
Zhang Z, Chen BS, Du L, et al. Expert-AI collaborative training for novice endoscopists: a path to enhanced efficiency [J]. Bioengineering (Basel), 2025, 12(6): 582.
|
| 37 |
Li J, Zhu Y, Dong Z, et al. Development and validation of a feature extraction-based logical anthropomorphic diagnostic system for early gastric cancer: a case-control study [J]. EClinicalMedicine, 2022, 46: 101366.
|
| 38 |
Hu H, Gong L, Dong D, et al. Identifying early gastric cancer under magnifying narrow-band images with deep learning: a multicenter study [J]. Gastrointest Endosc, 2021, 93(6): 1333-1341.e3.
|
| 39 |
阚浩轩, 李博文, 王森, 等. 国际胃癌诊疗新趋势-基于第16届国际胃癌大会的前沿观察 [J]. 中国实用外科杂志, 2025, 45(11): 1244-1248.
|
| 40 |
Kim MJ, Kim SH, Kim SM, et al. The advent of domain adaptation into artificial intelligence for gastrointestinal endoscopy and medical imaging [J]. Diagnostics (Basel), 2023, 13(19): 3023.
|
| 41 |
Huang X, Qin M, Fang M, et al. The application of artificial intelligence in upper gastrointestinal cancers [J]. J Natl Cancer Cent, 2025, 5(2): 113-131.
|
| 42 |
You JG, Hernandez-Boussard T, Pfeffer MA, et al. Clinical trials informed framework for real world clinical implementation and deployment of artificial intelligence applications [J]. NPJ Digit Med, 2025, 8(1): 107.
|
| 43 |
Dong Z, Wang J, Li Y, et al. Publisher correction: explainable artificial intelligence incorporated with domain knowledge diagnosing early gastric neoplasms under white light endoscopy [J]. NPJ Digit Med, 2023, 6(1): 109.
|
| 44 |
Yao Z, Jin T, Mao B, et al. Construction and multicenter diagnostic verification of intelligent recognition system for endoscopic images from early gastric cancer based on Yolo-V3 algorithm [J]. Front Oncol, 2022, 12: 815951.
|
| 45 |
Martins BC, Moura RN, Kum AST, et al. Endoscopic imaging for the diagnosis of neoplastic and ore-neoplastic conditions of the stomach [J]. Cancers (Basel), 2023, 15(9): 2445.
|
| 46 |
Niemiec E. Will the EU medical device regulation help to improve the safety and performance of medical AI devices? [J]. Digit Health, 2022, 8: 20552076221089079.
|
| 47 |
Pattilachan TM, Christodoulou M, Ross S. Diagnosis to dissection: AI's role in early detection and surgical intervention for gastric cancer [J]. J Robot Surg, 2024, 18(1): 259.
|
| 48 |
Alghamdi BM, Rogers S, Roberman S, et al. Implementation and integration of a multidisciplinary pharmacogenomics service in an underserved integrated behavioral health clinic [J]. Front Pharmacol, 2025, 16: 1594032.
|
| 49 |
Gonçalves N, Chaves J, Marques-Sá I, et al. Early diagnosis of gastric cancer: Endoscopy and artificial intelligence [J]. Best Pract Res Clin Gastroenterol, 2025, 75: 101979.
|
| 50 |
Nabi Z, Manchu C, Reddy DN. Robotics in interventional endoscopy-evolution and the way forward [J]. Indian J Gastroenterol, 2024, 43(5): 966-975.
|