细胞投影图:骨髓抽吸细胞学的新可视化。

Q2 Medicine
Taher Dehkharghanian , Youqing Mu , Catherine Ross , Monalisa Sur , H.R. Tizhoosh , Clinton J.V. Campbell
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引用次数: 0

摘要

细胞检测的深度模型已经证明在骨髓细胞学中的实用性,在准确性和计算效率方面显示出令人印象深刻的结果。然而,这些模型尚未在临床诊断工作流程中实施。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一致。为了解决这些问题,我们介绍了一种新的、自动生成的骨髓抽吸标本的视觉摘要,称为细胞投影图(CPPs)。CPPs涵盖了中性粒细胞成熟等相关生物学模式,为骨髓抽吸细胞学提供了一个紧凑的总结。为了评估临床相关性,3名血液病理学家对CPPs进行了检查,他们决定相应的诊断概要是否与生成的CPPs相匹配。病理学家能够将CP与正确的概要进行匹配,匹配度为85%。我们的发现表明,CPPs可以代表骨髓抽吸标本的临床相关信息,并可用于向病理学家有效总结骨髓细胞学。CPPs可能是在血液病理学中实现以人为中心的人工智能的一步,也是数字病理工作流程诊断支持工具的基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Cell projection plots: A novel visualization of bone marrow aspirate cytology

Cell projection plots: A novel visualization of bone marrow aspirate cytology

Cell projection plots: A novel visualization of bone marrow aspirate cytology

Cell projection plots: A novel visualization of bone marrow aspirate cytology

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 cell projection plots (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.

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来源期刊
Journal of Pathology Informatics
Journal of Pathology Informatics Medicine-Pathology and Forensic Medicine
CiteScore
3.70
自引率
0.00%
发文量
2
审稿时长
18 weeks
期刊介绍: The Journal of Pathology Informatics (JPI) is an open access peer-reviewed journal dedicated to the advancement of pathology informatics. This is the official journal of the Association for Pathology Informatics (API). The journal aims to publish broadly about pathology informatics and freely disseminate all articles worldwide. This journal is of interest to pathologists, informaticians, academics, researchers, health IT specialists, information officers, IT staff, vendors, and anyone with an interest in informatics. We encourage submissions from anyone with an interest in the field of pathology informatics. We publish all types of papers related to pathology informatics including original research articles, technical notes, reviews, viewpoints, commentaries, editorials, symposia, meeting abstracts, book reviews, and correspondence to the editors. All submissions are subject to rigorous peer review by the well-regarded editorial board and by expert referees in appropriate specialties.
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