Li Xin-Rui, Kong Mo-Wei, Guan Xiang-Feng, Gao Yu
Department of Cardiology, Guiqian International General Hospital, Guiyang 550018, Guizhou Province, China.
Department of Cardiology, The Affiliated Hospital of Southwest Medical University, Luzhou 646000, Sichuan Province, China.
World J Gastrointest Endosc. 2025 Jul 16;17(7):108293. doi: 10.4253/wjge.v17.i7.108293.
With the rapid advancement of technology, artificial intelligence (AI) has emerged as a transformative force in gastroenterology, particularly in diagnosing upper gastrointestinal diseases such as Barrett's esophagus (BE), esophageal cancer, gastroesophageal reflux disease (GERD), and esophagogastric varices. AI's capabilities in image analysis, classification, detection, and segmentation have significantly improved diagnostic accuracy and efficiency. For BE, AI models achieve high sensitivity and specificity in detecting early neoplastic changes and guiding targeted biopsies. In esophageal cancer, AI enhances early lesion detection, improving intervention success rates. For GERD, AI classifies disease severity based on the Los Angeles grading system and accurately segments lesions. Additionally, AI detects esophagogastric varices and predicts bleeding risks more effectively than traditional methods. Despite these advancements, challenges remain, including the need for high-quality data, multi-center validation, and ensuring AI model interpretability. Future research should address these issues and further integrate AI into clinical practice to optimize patient outcomes. This review highlights AI's transformative impact on upper gastrointestinal disease diagnosis, emphasizing its potential to revolutionize endoscopic practice and improve patient care.
随着技术的飞速发展，人工智能（AI）已成为胃肠病学领域的一股变革力量，尤其是在诊断诸如巴雷特食管（BE）、食管癌、胃食管反流病（GERD）和食管胃静脉曲张等上消化道疾病方面。人工智能在图像分析、分类、检测和分割方面的能力显著提高了诊断的准确性和效率。对于巴雷特食管，人工智能模型在检测早期肿瘤变化和指导靶向活检方面具有高灵敏度和特异性。在食管癌方面，人工智能提高了早期病变的检测能力，提高了干预成功率。对于胃食管反流病，人工智能根据洛杉矶分级系统对疾病严重程度进行分类，并准确分割病变。此外，人工智能检测食管胃静脉曲张并预测出血风险比传统方法更有效。尽管取得了这些进展，但挑战依然存在，包括需要高质量数据、多中心验证以及确保人工智能模型的可解释性。未来的研究应解决这些问题，并进一步将人工智能整合到临床实践中，以优化患者治疗效果。本综述强调了人工智能对上消化道疾病诊断的变革性影响，强调了其革新内镜实践和改善患者护理的潜力。
