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About this paper
Yikun Ma, Bo Li, Ying Chen, Zijie Yue, Shuchang Xu, Jingyao Li, Lei Ma, Liang Zhong and 15 more
An AI foundation model, utilizing a hybrid architecture, was developed and validated for endoscopic screening and precise staging of esophagogastric junction adenocarcinoma (EGJA). The model achieved up to 0.9256 accuracy across diverse test sets, outperforming expert endoscopists and significantly enhancing their diagnostic capabilities when used as an assistive tool.
Key insights
- A hybrid architecture combining a vision foundation model (DINOv2) for global understanding and a CNN (ResNet50) for local detail, fused by a Mixture-of-Experts, effectively captures nuanced endoscopic features for comprehensive EGJA diagnosis.
- Large-scale, multicentric data collection is essential for building AI models that exhibit strong generalizability and robustness across diverse clinical settings, mitigating data scarcity challenges in rare diseases like EGJA.
- AI models not only achieve higher diagnostic accuracy and efficiency than human endoscopists but also consistently improve human diagnostic performance and consistency when used as an assistive tool.
Summary by alphaXiv.
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