This study introduces a new framework for the artificial intelligence-assisted characterization of Gram-stained whole-slide images (WSIs). As a test for the diagnosis of bloodstream infections, Gram stains provide critical early data to inform patient treatment. Rapid and reliable analysis of Gram stains has been shown to be positively associated with better clinical outcomes, underscoring the need for improved tools to automate Gram stain analysis. In this work, we developed a novel transformer-based model for Gram-stained WSI classification, which is more scalable to large datasets than previous convolutional neural network (CNN) -based methods as it does not require patch-level manual annotations. We also introduce a large Gram stain dataset from Dartmouth-Hitchcock Medical Center (Lebanon, New Hampshire, USA) to evaluate our model, exploring the classification of five major categories of Gram-stained WSIs: Gram-positive cocci in clusters, Gram-positive cocci in pairs/chains, Gram-positive rods, Gram-negative rods, and slides with no bacteria. Our model achieves a classification accuracy of 0.858 (95% CI: 0.805, 0.905) and an AUC of 0.952 (95% CI: 0.922, 0.976) using five-fold nested cross-validation on our 475-slide dataset, demonstrating the potential of large-scale transformer models for Gram stain classification. We further demonstrate the generalizability of our trained model, which achieves strong performance on external datasets without additional fine-tuning.

翻译：本研究提出了一种用于人工智能辅助分析革兰氏染色全玻片图像（WSIs）的新框架。作为血流感染诊断的检测手段，革兰氏染色为患者治疗提供了关键的早期数据。快速可靠的革兰氏染色分析已被证明与更好的临床结局呈正相关，这凸显了改进自动化革兰氏染色分析工具的必要性。在本工作中，我们开发了一种基于Transformer的新型模型用于革兰氏染色WSI分类，与先前基于卷积神经网络（CNN）的方法相比，该模型对大规模数据集具有更好的可扩展性，因为它不需要切片级别的人工标注。我们还引入了来自达特茅斯-希区柯克医疗中心（美国新罕布什尔州黎巴嫩市）的大型革兰氏染色数据集来评估我们的模型，探索了五类主要革兰氏染色WSIs的分类：簇状革兰氏阳性球菌、成对/链状革兰氏阳性球菌、革兰氏阳性杆菌、革兰氏阴性杆菌以及无细菌玻片。在我们的475张玻片数据集上通过五折嵌套交叉验证，我们的模型实现了0.858（95% CI: 0.805, 0.905）的分类准确率和0.952（95% CI: 0.922, 0.976）的AUC值，证明了大规模Transformer模型在革兰氏染色分类中的潜力。我们进一步展示了训练模型的泛化能力，该模型在外部数据集上无需额外微调即可取得优异性能。