干燥综合征 (SS) 是一种罕见的慢性自身免疫性疾病，主要影响成年女性，其特征是慢性炎症以及唾液腺和泪腺功能障碍。它通常与系统性红斑狼疮、类风湿性关节炎和肾脏疾病有关，这些疾病可能导致死亡率增加。早期诊断至关重要，但诊断 SS 的传统方法（主要通过唾液腺组织的组织病理学评估）具有局限性。该研究使用了 100 个唇腺活检，创建全幻灯片图像 (WSI) 进行分析。该模型被称为基于细胞组织图的病理图像分析模型（CTG-PAM），基于图论，表征了单细胞特征、细胞与细胞特征和细胞-组织特征。基于这些功能，CTG-PAM 实现了细胞级分类，从而实现淋巴细胞识别。此外，它利用细胞图结构中的连通成分分析技术，根据淋巴细胞计数进行 SS 诊断。CTG-PAM 在诊断 SS 方面优于传统的深度学习方法。内部验证数据集的受试者工作特征曲线下面积 (AUC) 为 1.0，外部测试数据集的受试者工作特征曲线下面积为 0.8035。这表明准确性高。 CTG-PAM对外部数据集的敏感度为98.21%，准确度为93.75%。相比之下，传统深度学习方法（ResNet-50）的灵敏度和准确性较低。该研究还表明，与初学者相比，CTG-PAM 的诊断准确性更接近熟练的病理学家。我们的研究结果表明，CTG-PAM 是诊断 SS 的可靠方法。此外，CTG-PAM 在改善 SS 患者的预后方面显示出希望，并且在非肿瘤性和肿瘤性疾病的鉴别诊断方面具有巨大的潜力。 AI 模型有可能将其应用扩展到诊断肿瘤微环境中的免疫细胞。© 2024。作者。

Sjögren's Syndrome (SS) is a rare chronic autoimmune disorder primarily affecting adult females, characterized by chronic inflammation and salivary and lacrimal gland dysfunction. It is often associated with systemic lupus erythematosus, rheumatoid arthritis and kidney disease, which can lead to increased mortality. Early diagnosis is critical, but traditional methods for diagnosing SS, mainly through histopathological evaluation of salivary gland tissue, have limitations.The study used 100 labial gland biopsy, creating whole-slide images (WSIs) for analysis. The proposed model, named Cell-tissue-graph-based pathological image analysis model (CTG-PAM) and based on graph theory, characterizes single-cell feature, cell-cell feature, and cell-tissue feature. Building upon these features, CTG-PAM achieves cellular-level classification, enabling lymphocyte recognition. Furthermore, it leverages connected component analysis techniques in the cell graph structure to perform SS diagnosis based on lymphocyte counts.CTG-PAM outperforms traditional deep learning methods in diagnosing SS. Its area under the receiver operating characteristic curve (AUC) is 1.0 for the internal validation dataset and 0.8035 for the external test dataset. This indicates high accuracy. The sensitivity of CTG-PAM for the external dataset is 98.21%, while the accuracy is 93.75%. In comparison, the sensitivity and accuracy for traditional deep learning methods (ResNet-50) are lower. The study also shows that CTG-PAM's diagnostic accuracy is closer to skilled pathologists compared to beginners.Our findings indicate that CTG-PAM is a reliable method for diagnosing SS. Additionally, CTG-PAM shows promise in enhancing the prognosis of SS patients and holds significant potential for the differential diagnosis of both non-neoplastic and neoplastic diseases. The AI model potentially extends its application to diagnosing immune cells in tumor microenvironments.© 2024. The Author(s).