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中华胃食管反流病电子杂志 ›› 2025, Vol. 12 ›› Issue (02) : 79 -83. doi: 10.3877/cma.j.issn.2095-8765.2025.02.005

综述

人工智能在胃食管反流病诊疗中的应用与进展
代垚炜1, 刘小南2, 俞德梁2,()   
  1. 1710021 西安医学院
    2710032 西安,空军军医大学西京医院日间手术中心
  • 收稿日期:2025-08-29 出版日期:2025-05-15
  • 通信作者: 俞德梁
  • 基金资助:
    陕西省软科学研究计划(2022KRM184); 空军军医大学学校助推-单病种专项(2024LC2439)

Application and progress of Artificial intelligence in the diagnosis and treatment of gastroesophageal reflux disease

Yaowei Dai1, Xiaonan Liu2, Deliang Yu2,()   

  1. 1Xi’an Medical University, Xi’an 710021, China
    2Ambulatory Surgery Center, Xijing Hospital of Airforce Medical University, Xi’an 710032, China
  • Received:2025-08-29 Published:2025-05-15
  • Corresponding author: Deliang Yu
引用本文:

代垚炜, 刘小南, 俞德梁. 人工智能在胃食管反流病诊疗中的应用与进展[J/OL]. 中华胃食管反流病电子杂志, 2025, 12(02): 79-83.

Yaowei Dai, Xiaonan Liu, Deliang Yu. Application and progress of Artificial intelligence in the diagnosis and treatment of gastroesophageal reflux disease[J/OL]. Chinese Journal of Gastroesophageal Reflux Disease(Electronic Edition), 2025, 12(02): 79-83.

人工智能在胃食管反流病的诊疗中展现了巨大的潜力,利用机器学习、深度学习等算法,在发病机制、诊断、治疗和预后评估等方面发挥着重要作用。虽然人工智能在伦理、数据及法律等方面仍存在一些挑战,但随着人工智能技术的进步,人工智能有望为胃食管反流病的全病程诊疗管理体系提供更多、更精准的证据支持,从而改善患者预后。

Artificial Intelligence (AI) has demonstrated great potential in the diagnosis and treatment of gastroesophageal reflux disease (GERD). By leveraging algorithms such as machine learning and deep learning, AI occupies a pivotal position in understanding the pathogenesis, diagnosis, treatment, and prognosis assessment of GERD. Although challenges related to ethics, data, and legal aspects still exist, with the advancements of AI technology, AI is expected to provide more precise evidence to support the entire course of diagnosis, treatment, and management system for GERD, thereby improving patient outcomes.

1
Wu Y, Guo Z, Zhang C, et al. Mean nocturnal baseline impedance, a novel metric of multichannel intraluminal impedance-pH monitoring in diagnosing gastroesophageal reflux disease [J]. Ther Adv Gastroenterol, 2022, 15: 1098288171.
2
Spechler SJ. Evaluation and treatment of patients with persistent reflux symptoms despite proton pump inhibitor treatment [J]. Gastroenterol Clin North Am, 2020, 49(3): 437-450.
3
Kulkarni PA, Singh H. Artificial intelligence in clinical diagnosis: opportunities, challenges, and hype [J]. JAMA, 2023, 330(4): 317-318.
4
Sharma P, Yadlapati R. Pathophysiology and treatment options for gastroesophageal reflux disease: looking beyond acid [J]. Ann N Y Acad Sci, 2021, 1486(1): 3-14.
5
Wong M, Liu M, Lei W, et al. Artificial intelligence facilitates measuring reflux episodes and postreflux swallow-induced peristaltic wave index from impedance-pH studies in patients with reflux disease [J]. Neurogastroenterol Motil, 2023, 35(3): e14506.
6
Farah A, Abboud W, Savarino EV, et al. Esophageal intelligence: implementing artificial intelligence into the diagnostics of esophageal motility and impedance pH monitoring [J]. Neurogastroenterol Motil, 2025, 37(9): e70038.
7
Jones MP, Sloan SS, Rabine JC, et al. Hiatal hernia size is the dominant determinant of esophagitis presence and severity in gastroesophageal reflux disease [J]. Am J Gastroenterol, 2001, 96(6): 1711-1717.
8
Kafetzis I, Fuchs K, Sodmann P, et al. Efficient artificial intelligence-based assessment of the gastroesophageal valve with Hill classification through active learning [J]. Sci Rep, 2024, 14(1): 18825.
9
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.
10
Dogan Y, Bor S. Computer-based intelligent solutions for the diagnosis of gastroesophageal reflux disease phenotypes and chicago classification 3.0 [J]. Healthcare, 2023, 11(12): 1790.
11
Barriga-Rivera A, Elena M, Moya MJ, et al. Software for symptom association analysis in pediatric gastroesophageal reflux disease [J]. Comput Methods Programs Biomed, 2013, 111(1): 181-188.
12
Lee K, Kim ES, Kim D, et al. Association of gastroesophageal reflux disease with preterm birth: machine learning analysis [J]. J Korean Med Sci, 2021, 36(43): e282.
13
Liu Q, Xin Y, Wu C, et al. Diagnostic value of combining ultrafast cine MRI and morphological measurements on gastroesophageal reflux disease [J]. Abdom Radiol, 2025. Online ahead of print.
14
Zhang S, Joseph AA, Gross L, et al. Diagnosis of gastroesophageal reflux disease using real-time magnetic resonance imaging [J]. Sci Rep, 2015, 5: 12112.
15
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.
16
Li Y, Zhu S, Yu J, et al. Intelligent detection endoscopic assistant: An artificial intelligence-based system for monitoring blind spots during esophagogastroduodenoscopy in real-time [J]. Dig Liver Dis, 2021, 53(2): 216-223.
17
Wang C, Chiu Y, Chen W, et al. A deep learning model for classification of endoscopic gastroesophageal reflux disease [J]. Int J Environ Res Public Health, 2021, 18(5): 2428.
18
Alhithlool AW, Almutlaq AS, Almulla SA, et al. How do medical students perceive the role of artificial intelligence in management of gastroesophageal reflux disease? [J]. Med Teach, 2025, 47(6): 1022-1028.
19
Slater BJ, Dirks RC, Mckinley SK, et al. SAGES guidelines for the surgical treatment of gastroesophageal reflux (GERD) [J]. Surg Endosc, 2021, 35(9): 4903-4917.
20
Moore M, Afaneh C, Benhuri D, et al. Gastroesophageal reflux disease: a review of surgical decision making [J]. World J Gastrointest Surg, 2016, 8(1): 77-83.
21
Lee S, Jeon J, Park J, et al. An artificial intelligence system for comprehensive pathologic outcome prediction in early gastric cancer through endoscopic image analysis (with video) [J]. Gastric Cancer, 2024, 27(5): 1088-1099.
22
Rogers B, Samanta S, Ghobati K, et al. Artificial intelligence automates and augments baseline impedance measurements from pH-impedance studies in gastroesophageal reflux disease [J]. J Gastroenterol, 2021, 56(1): 34-41.
23
Ge Z, Fang Y, Chang J, et al. Using deep learning to assess the function of gastroesophageal flap valve according to the Hill classification system [J]. Ann Med, 2023, 55(2): 2279239.
24
Ge Z, Wang B, Chang J, et al. Using deep learning and explainable artificial intelligence to assess the severity of gastroesophageal reflux disease according to the Los Angeles classification system [J]. Scand J Gastroenterol, 2023, 58(6): 596-604.
25
Yen H, Tsai H, Wang C, et al. An Improved endoscopic automatic classification model for gastroesophageal reflux disease using deep learning integrated machine learning [J]. Diagnostics, 2022, 12(11): 2827.
26
Kim SI, Eun Y, Lee YC. Development of a machine learning model to predict therapeutic responses in laryngopharyngeal reflux disease [J]. J Voice, 2025: S0892-1997(25)00110-9. Online ahead of print.
27
Sumiyama K, Futakuchi T, Kamba S, et al. Artificial intelligence in endoscopy: present and future perspectives [J]. Dig Endosc, 2021, 33(2): 218-230.
28
Gehrung M, Crispin-Ortuzar M, Berman AG, et al. Triage-driven diagnosis of Barrett’s esophagus for early detection of esophageal adenocarcinoma using deep learning [J]. Nat Med, 2021, 27(5): 833-841.
29
Ge H, Zhou X, Wang Y, et al. Development and validation of deep learning models for the multiclassification of reflux esophagitis based on the Los Angeles classification [J]. J. Healthc Eng, 2023, 2023: 7023731.
30
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.
31
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.
32
Rodgers CM, Ellingson SR, Chattrejee P. Open Data and transparency in artificial intelligence and machine learning: a new era of research [J]. F1000Res, 2023, 12: 387.
33
Drabiak K, Kyaer S, Nemon V, et al. AI and machine learning ethics, law, diversity, and global impact [J]. Br J Radiol, 2023, 96(1150): 20220934.
34
Salas M, Petracek J, Yalamanchili P, et al. The use of artificial intelligence in pharmacovigilance: a systematic review of the literature [J]. Pharm Med, 2022, 36(5): 295-306.
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