用于骨髓抽吸物常规分析的新型人工智能(AI)辅助数字显微镜系统的性能评估。

IF 7.1 1区 医学 Q1 PATHOLOGY
Adam Bagg , Philipp W. Raess , Deborah Rund , Siddharth Bhattacharyya , Joanna Wiszniewska , Alon Horowitz , Darrin Jengehino , Guang Fan , Michelle Huynh , Abdoulaye Sanogo , Irit Avivi , Ben-Zion Katz
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引用次数: 0

摘要

骨髓穿刺(BMA)涂片分析对各种良性和肿瘤性血液病的诊断、治疗和监测至关重要。目前,这种分析是通过人工显微镜进行的。我们开展了一项多中心研究,通过人工智能(AI)驱动的决策支持系统来验证计算显微镜方法。我们分析了来自肿瘤和其他临床症状患者的总共 795 份 BMA 标本(615 份罗曼诺夫斯基染色和 180 份普鲁士蓝染色),比较了 Scopio Labs X100 全视野 BMA 系统(测试方法)和人工显微镜(参考方法)的性能。该系统为每个样本提供了平均 1385±536 个(范围 0-3131)细胞供分析。系统平均为每个标本选择了 39.98±19.64 个视场(范围 0-140)进行分析,其中 87±21%(范围 0-100%)被合格操作员接受。这些区域平均被纳入每个标本的 17.62±7.24 个感兴趣区(范围 1-50)。评估了一次和二次骨髓抽吸特征(成熟度、形态和计数评估)的效率、灵敏度和特异性,以及用户间的总体一致性。在罗曼诺夫斯基(效率为 90.85%,灵敏度为 81.61%;特异性为 92.88%)和普鲁士蓝(效率为 90.0%,灵敏度为 81.94%;特异性为 93.38%)染色样本的 BMA 综合评估中,测试方法与参考方法均显示出高度相关性。在 BMA 评估方面,测试与参考方法的总体一致性为 91.1%。在重复性和再现性方面,无论是离散测量(CV 低于 20%)还是差分测量(SD 低于 5%),所有标准偏差和变异系数值均低于预定的接受标准。数字决策支持系统与人工显微镜之间的高度相关性表明,该系统具有提供高质量、准确的数字 BMA 分析的潜力,可加快专家对 BMA 标本的审查和诊断,其实际应用包括远程 BMA 评估,并可能为正常和肿瘤性造血研究带来新的机遇。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Performance Evaluation of a Novel Artificial Intelligence–Assisted Digital Microscopy System for the Routine Analysis of Bone Marrow Aspirates

Bone marrow aspiration (BMA) smear analysis is essential for diagnosis, treatment, and monitoring of a variety of benign and neoplastic hematological conditions. Currently, this analysis is performed by manual microscopy. We conducted a multicenter study to validate a computational microscopy approach with an artificial intelligence–driven decision support system. A total of 795 BMA specimens (615 Romanowsky-stained and 180 Prussian blue–stained) from patients with neoplastic and other clinical conditions were analyzed, comparing the performance of the Scopio Labs X100 Full Field BMA system (test method) with manual microscopy (reference method). The system provided an average of 1,385 ± 536 (range, 0-3,131) cells per specimen for analysis. An average of 39.98 ± 19.64 fields of view (range, 0-140) per specimen were selected by the system for analysis, of them 87% ± 21% (range, 0%-100%) were accepted by the qualified operators. These regions were included in an average of 17.62 ± 7.24 regions of interest (range, 1-50) per specimen. The efficiency, sensitivity, and specificity for primary and secondary marrow aspirate characteristics (maturation, morphology, and count assessment), as well as overall interuser agreement, were evaluated. The test method showed a high correlation with the reference method for comprehensive BMA evaluation, both on Romanowsky- (90.85% efficiency, 81.61% sensitivity, and 92.88% specificity) and Prussian blue–stained samples (90.0% efficiency, 81.94% sensitivity, and 93.38% specificity). The overall agreement between the test and reference methods for BMA assessment was 91.1%. For repeatability and reproducibility, all standard deviations and coefficients of variation values were below the predefined acceptance criteria both for discrete measurements (coefficient of variation below 20%) and differential measurements (SD below 5%). The high degree of correlation between the digital decision support system and manual microscopy demonstrates the potential of this system to provide a high-quality, accurate digital BMA analysis, expediting expert review and diagnosis of BMA specimens, with practical applications including remote BMA evaluation and possibly new opportunities for the research of normal and neoplastic hematopoiesis.

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来源期刊
Modern Pathology
Modern Pathology 医学-病理学
CiteScore
14.30
自引率
2.70%
发文量
174
审稿时长
18 days
期刊介绍: Modern Pathology, an international journal under the ownership of The United States & Canadian Academy of Pathology (USCAP), serves as an authoritative platform for publishing top-tier clinical and translational research studies in pathology. Original manuscripts are the primary focus of Modern Pathology, complemented by impactful editorials, reviews, and practice guidelines covering all facets of precision diagnostics in human pathology. The journal's scope includes advancements in molecular diagnostics and genomic classifications of diseases, breakthroughs in immune-oncology, computational science, applied bioinformatics, and digital pathology.
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