PDF(Pubmed)
Abstract：
Gastric cancer (GC) is a significant healthcare concern, and the identification of high-risk patients is crucial. Indeed, gastric precancerous conditions present significant diagnostic challenges, particularly early intestinal metaplasia (IM) detection. This study developed a deep learning system to assist in IM detection using image patches from gastric corpus examined using virtual chromoendoscopy in a Western country. Utilizing a retrospective dataset of endoscopic images from Sant\'Andrea University Hospital of Rome, collected between January 2020 and December 2023, the system extracted 200 × 200 pixel patches, classifying them with a voting scheme. The specificity and sensitivity on the patch test set were 76% and 72%, respectively. The optimization of a learnable voting scheme on a validation set achieved a specificity of 70% and sensitivity of 100% for entire images. Despite data limitations and the absence of pre-trained models, the system shows promising results for preliminary screening in gastric precancerous condition diagnostics, providing an explainable and robust Artificial Intelligence approach.
摘要：
胃癌（GC）是一个重要的医疗保健问题，高危患者的识别至关重要。的确,胃癌前病变提出了重大的诊断挑战，特别是早期肠上皮化生（IM）检测。这项研究开发了一种深度学习系统，以使用西方国家使用虚拟色素内窥镜检查的胃体图像块来辅助IM检测。利用罗马Sant\'Andrea大学医院的内窥镜图像的回顾性数据集，在2020年1月至2023年12月之间收集，该系统提取了200×200像素的补丁，用投票方案对它们进行分类。斑贴试验集的特异性和敏感性分别为76%和72%,分别。在验证集上的可学习投票方案的优化实现了整个图像的70%的特异性和100%的灵敏度。尽管数据有限且缺乏预训练模型，该系统在胃癌前状态诊断的初步筛查中显示出有希望的结果，提供一种可解释和强大的人工智能方法。
