Background/Objectives: Endoscopic ultrasound (EUS) is crucial for lymph node (LN) characterization, playing a key role in oncological staging and treatment guidance. EUS criteria for predicting malignancy are imprecise, and histologic diagnosis may have limitations. This multicenter study aimed to evaluate the effectiveness of a novel artificial intelligence (AI)–based system in predicting LN malignancy from EUS images.Methods: This multicenter study included EUS images from nine centers. Lesions were labeled (“malignant” or “benign”) and delimited with bounding boxes. Definitive diagnoses were based on cytology/biopsy or surgical specimens and, if negative, a minimum six-month clinical follow-up. A convolutional neural network (CNN) was developed using the YOLO (You Only Look Once) architecture, incorporating both detection and classification modules.Results: A total of 59,992 images from 82 EUS procedures were analyzed. The CNN distinguished malignant from benign lymph nodes with a sensitivity of 98.8% (95% CI: 98.5–99.2%), specificity of 99.0% (95% CI: 98.3–99.7%), and precision of 99.0% (95% CI: 98.4–99.7%). The negative and positive predictive values for malignancy were 98.8% and 99.0%, respectively. Overall diagnostic accuracy was 98.3% (95% CI: 97.6–99.1%).Conclusions: This is the first study evaluating the performance of deep learning systems for LN assessment using EUS imaging. Our AI-powered imaging model shows excellent detection and classification capabilities, emphasizing its potential to provide a valuable tool to refine LN evaluation with EUS, ultimately supporting more tailored, efficient patient care.
背景/目的：超声内镜（EUS）对淋巴结（LN）特征评估至关重要，在肿瘤分期和治疗指导中发挥关键作用。目前基于EUS预测恶性的标准尚不精确，且组织学诊断存在局限性。本研究旨在通过多中心数据评估一种基于人工智能（AI）的新型系统在EUS图像中预测淋巴结恶性的效能。 方法：这项多中心研究纳入了来自九个中心的EUS图像。所有病灶均被标注（"恶性"或"良性"）并用边界框标定。最终诊断依据细胞学/活检或手术标本结果，若结果为阴性则需至少六个月的临床随访确认。研究采用YOLO（You Only Look Once）架构开发了包含检测与分类模块的卷积神经网络（CNN）。 结果：共分析了82例EUS检查中的59,992张图像。CNN区分恶性与良性淋巴结的敏感度为98.8%（95% CI：98.5–99.2%），特异度为99.0%（95% CI：98.3–99.7%），精确度为99.0%（95% CI：98.4–99.7%）。恶性预测的阴性预测值和阳性预测值分别为98.8%和99.0%。总体诊断准确率达98.3%（95% CI：97.6–99.1%）。 结论：这是首个评估深度学习系统在EUS影像中淋巴结评估性能的研究。我们开发的AI影像模型展现出卓越的检测与分类能力，突显了其作为优化EUS淋巴结评估工具的潜力，最终可为实现更精准、高效的患者诊疗提供支持。
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