Evidence map›Paper›PMID 40663112›Full record

ArticleAnnals of hematology2025

Advantages of AI-based whole blood film scanning for blast detection in markedly leucopenic blood films.

Geng Wang, Lin Zheng, Zhejun Fang, Guoju Luo, Qian Chen, Qi Qi Zhang, Bo Shen Wu, Xin Wang, Rongrong Cheng, Ping Deng and 3 more

Abstract read
In one paragraph

Article in Annals of hematology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

13 authors.

Geng WangDepartment of Clinical Laboratory, Peking Union Medical College Hospital, Beijing, 100730, China.
Lin ZhengDepartment of Clinical Laboratory, Peking Union Medical College Hospital, Beijing, 100730, China.
Zhejun FangBeijing Xiaoying Technology Co., Ltd, Beijing, 100085, China.
Guoju LuoDepartment of Clinical Laboratory, Peking Union Medical College Hospital, Beijing, 100730, China.
Qian ChenDepartment of Clinical Laboratory, Peking Union Medical College Hospital, Beijing, 100730, China.
Qi Qi ZhangDepartment of Clinical Laboratory, Peking Union Medical College Hospital, Beijing, 100730, China.
Bo Shen WuBeijing Xiaoying Technology Co., Ltd, Beijing, 100085, China.
Xin WangDepartment of Clinical Laboratory, Peking Union Medical College Hospital, Beijing, 100730, China.
Rongrong ChengDepartment of Clinical Laboratory, Peking Union Medical College Hospital, Beijing, 100730, China.
Ping DengDepartment of Clinical Laboratory, Peking Union Medical College Hospital, Beijing, 100730, China.
Binyao ZhangDepartment of Clinical Laboratory, Peking Union Medical College Hospital, Beijing, 100730, China.
Jing JinDepartment of Clinical Laboratory, Peking Union Medical College Hospital, Beijing, 100730, China.
Wei WuDepartment of Clinical Laboratory, Peking Union Medical College Hospital, Beijing, 100730, China. ww_pumch@sina.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

To evaluate performance of AI-assisted blast detection via whole blood film scanning in cases of severe leukopenia. From August to October 2023, we collected 157 patient specimens with white blood cell (WBC) counts of ≤2.0 × 10⁹/L. We then evaluated and compared the blast detection capabilities of an artificial intelligence-based system (Cygnus instrument) against the CellaVision DI-60 for WBC analysis. Additionally, this study aimed to assess the comparative performance of these two automated blood cell morphology analyzers based on their underlying operational principles. Following manual microscopy confirmation, 17 cases showing ≥ 1 blast cell in peripheral blood smear were identified among 157 specimens with WBC ≤ 2.0 × 10⁹/L. The Cygnus whole-slide scanning mode detected all 17 cases (sensitivity: 100%), whereas the Cygnus 200-cell mode detected only 9 cases (9/17), and the CellaVision DI-60 200-cell mode detected 8 cases (8/17). In leukopenic patients (WBC ≤2.0×10⁹/L), utilizing Cygnus' whole-slide scanning capability substantially improves blast identification rates, potentially enabling earlier detection of hematologic abnormalities.

Indexed as

Artificial IntelligenceLeukopeniaAdultAgedFemaleHumansLeukocyte CountLeukocytesMaleMiddle AgedArtificial intelligenceBlast cellsCygnus instrumentLeucopenicWhole-slide scanning

Identifiers

PMID40663112
PMCPMC12619778

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.