Department of Pathology, Yale School of Medicine, New Haven, Connecticut.
Department of Management Information System, College of Business, University of Houston Clear Lake, Houston, Texas.
Lab Invest. 2024 Feb;104(2):100288. doi: 10.1016/j.labinv.2023.100288. Epub 2023 Nov 15.
Liver transplantation is an effective treatment for end-stage liver disease, acute liver failure, and primary hepatic malignancy. However, the limited availability of donor organs remains a challenge. Severe large-droplet fat (LDF) macrovesicular steatosis, characterized by cytoplasmic replacement with large fat vacuoles, can lead to liver transplant complications. Artificial intelligence models, such as segmentation and detection models, are being developed to detect LDF hepatocytes. The Segment-Anything Model, utilizing the DEtection TRansformer architecture, has the ability to segment objects without prior knowledge of size or shape. We investigated the Segment-Anything Model's potential to detect LDF hepatocytes in liver biopsies. Pathologist-annotated specimens were used to evaluate model performance. The model showed high sensitivity but compromised specificity due to similarities with other structures. Filtering algorithms were developed to improve specificity. Integration of the Segment-Anything Model with rule-based algorithms accurately detected LDF hepatocytes. Improved diagnosis and treatment of liver diseases can be achieved through advancements in artificial intelligence algorithms for liver histology analysis.
肝移植是治疗终末期肝病、急性肝衰竭和原发性肝恶性肿瘤的有效方法。然而，供体器官的有限可用性仍然是一个挑战。严重的大液滴脂肪（LDF）巨泡性脂肪变性，其特征是细胞质被大脂肪空泡取代，可导致肝移植并发症。人工智能模型，如分割和检测模型，正在被开发用于检测 LDF 肝细胞。Segment-Anything 模型利用 DEtection TRansformer 架构，具有在没有大小或形状先验知识的情况下分割对象的能力。我们研究了 Segment-Anything 模型在肝活检中检测 LDF 肝细胞的潜力。使用病理学家注释的标本来评估模型性能。由于与其他结构的相似性，该模型表现出高灵敏度但特异性降低。开发了过滤算法来提高特异性。Segment-Anything 模型与基于规则的算法相结合，可以准确检测 LDF 肝细胞。通过人工智能算法在肝组织学分析方面的进步，可以实现对肝脏疾病的诊断和治疗的改善。
