Dehkharghanian Taher, Mu Youqing, Ross Catherine, Sur Monalisa, Tizhoosh H R, Campbell Clinton J V
McMaster University, Hamilton, Canada.
University Health Network, Toronto, Canada.
J Pathol Inform. 2023 Aug 30;14:100334. doi: 10.1016/j.jpi.2023.100334. eCollection 2023.
Deep models for cell detection have demonstrated utility in bone marrow cytology, showing impressive results in terms of accuracy and computational efficiency. However, these models have yet to be implemented in the clinical diagnostic workflow. Additionally, the metrics used to evaluate cell detection models are not necessarily aligned with clinical goals and targets. In order to address these issues, we introduce novel, automatically generated visual summaries of bone marrow aspirate specimens called (CPPs). Encompassing relevant biological patterns such as neutrophil maturation, CPPs provide a compact summary of bone marrow aspirate cytology. To gauge clinical relevance, CPPs were inspected by 3 hematopathologists, who decided whether corresponding diagnostic synopses matched with generated CPPs. Pathologists were able to match CPPs to the correct synopsis with a matching degree of 85%. Our finding suggests CPPs can represent clinically relevant information from bone marrow aspirate specimens and may be used to efficiently summarize bone marrow cytology to pathologists. CPPs could be a step toward human-centered implementation of artificial intelligence (AI) in hematopathology, and a basis for a diagnostic-support tool for digital pathology workflows.
用于细胞检测的深度模型已在骨髓细胞学中展现出实用性，在准确性和计算效率方面取得了令人瞩目的成果。然而，这些模型尚未应用于临床诊断工作流程。此外，用于评估细胞检测模型的指标不一定与临床目标和指标相一致。为了解决这些问题，我们引入了一种名为细胞模式概要（CPPs）的新型自动生成的骨髓穿刺标本视觉摘要。细胞模式概要涵盖了诸如中性粒细胞成熟等相关生物学模式，提供了骨髓穿刺细胞学的简洁摘要。为了评估临床相关性，3名血液病理学家检查了细胞模式概要，他们判断相应的诊断摘要是否与生成的细胞模式概要相匹配。病理学家能够以85%的匹配度将细胞模式概要与正确的摘要相匹配。我们的研究结果表明，细胞模式概要可以代表骨髓穿刺标本中的临床相关信息，并可用于向病理学家有效总结骨髓细胞学。细胞模式概要可能是血液病理学中以人类为中心实施人工智能（AI）的一步，也是数字病理工作流程诊断支持工具的基础。
