Development and validation of an AI foundation model for endoscopic diagnosis of esophagogastric junction adenocarcinoma: a cohort and deep learning study

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Background: The early detection of esophagogastric junction adenocarcinoma (EGJA) is crucial for improving patient prognosis, yet its current diagnosis is highly operator-dependent. This paper aims to make the first attempt to develop an artificial intelligence (AI) foundation model-based method for both screening and staging diagnosis of EGJA using endoscopic images. Methods: In this cohort and learning study, we conducted a multicentre study across seven Chinese hospitals between December 28, 2016 and December 30, 2024. It comprises 12,302 images from 1546 patients (590 with advanced EGJA, 243 with early EGJA, 713 without EGJA); 8249 of them were employed for model training, while the remaining were divided into the held-out (112 patients, 914 images), external (230 patients, 1539 images), and prospective (198 patients, 1600 images) test sets for evaluation. The proposed model employs DINOv2 (a vision foundation model) and ResNet50 (a convolutional neural network) to extract features of global appearance and local details of endoscopic images for EGJA staging diagnosis. The performance of our model is assessed using accuracy, sensitivity, specificity, positive predictive value, negative predictive value, area under the receiver operating characteristic curve, average precision, and Kappa. 30 endoscopists with varying experience levels were recruited for comparative and AI-assisted evaluations. Findings: Our model demonstrates satisfactory performance for EGJA staging diagnosis across three test sets, achieving an accuracy of 0.9256 (95% CI 0.9086-0.9426), 0.8895 (95% CI 0.8739-0.9052), and 0.8956 (95% CI 0.8813-0.9112), respectively. In contrast, among representative AI models, the best one (ResNet50) achieves an accuracy of 0.9125 (95% CI 0.8942-0.9308), 0.8382 (95% CI 0.8198-0.8566), and 0.8519 (95% CI 0.8345-0.8693) on the three test sets, respectively; the expert endoscopists achieve an accuracy of 0.8147 (95% CI 0.7895-0.8399) on the held-out test set. Statistical analysis reveals that our model significantly outperforms representative AI models and endoscopists (all P < 0.05), with the exception of ResNet50 on the held-out test set (P = 0.54). Moreover, with the assistance of our model, the overall accuracy for the trainee, competent, and expert endoscopists improves from 0.7035 (95% CI 0.6739-0.7331), 0.7350 (95% CI 0.7064-0.7636), and 0.8147 (95% CI 0.7895-0.8399) to 0.8497 (95% CI 0.8265-0.8728), 0.8521 (95% CI 0.8291-0.8751), and 0.8696 (95% CI 0.8478-0.8914), respectively. Interpretation: To our knowledge, our model is the first application of foundation models for EGJA staging diagnosis and demonstrates great potential in both diagnostic accuracy and efficiency. Besides, the results by the developed model can also be visually probed and interpreted, highlighting the clinical benefits in precision therapy. Nevertheless, the study also has limitations such as the regional constraint of the data sources and the restriction to white-light and narrow-band imaging modalities, which may limit the generalizability of the developed model. Funding: This study was supported by the Shanghai Health Development Commission, Shanghai Science and Technology Commission, Tongji University, and Shanghai Municipal Commission of Economy and Informatization.

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