Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands.
Computational Pathology Group, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands.
Pathobiology. 2024;91(1):8-17. doi: 10.1159/000529701. Epub 2023 Feb 15.
The expanding digitalization of routine diagnostic histological slides holds a potential to apply artificial intelligence (AI) to pathology, including bone marrow (BM) histology. In this perspective, we describe potential tasks in diagnostics that can be supported, investigations that can be guided, and questions that can be answered by the future application of AI on whole-slide images of BM biopsies. These range from characterization of cell lineages and quantification of cells and stromal structures to disease prediction. First glimpses show an exciting potential to detect subtle phenotypic changes with AI that are due to specific genotypes. The discussion is illustrated by examples of current AI research using BM biopsy slides. In addition, we briefly discuss current challenges for implementation of AI-supported diagnostics.
常规诊断组织学幻灯片的数字化不断发展，为人工智能（AI）在病理学中的应用提供了可能，包括骨髓（BM）组织学。从这个角度来看，我们描述了在未来通过对 BM 活检的全切片图像应用 AI 可以支持的诊断任务、可以指导的研究以及可以回答的问题。这些任务包括细胞谱系的特征描述、细胞和基质结构的定量分析以及疾病预测。初步迹象表明，人工智能有潜力检测到由于特定基因型导致的微妙表型变化。讨论通过使用 BM 活检切片的当前 AI 研究示例进行说明。此外，我们还简要讨论了实现 AI 支持的诊断的当前挑战。
