Evidence map›Paper›PMID 40594492›Full record

ArticleScientific reports2025

Artificial intelligence-driven label-free detection of chronic myeloid leukemia cells using ghost cytometry.

Kohjin Suzuki, Naoki Watanabe, Yutaka Tsukune, Tadaaki Inano, Shintaro Kinoshita, Sayuri Tomoda, Kohei Yamada, Yusuke Konishi, Takuya Kuwana, Takeshi Sugiyama and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Kohjin SuzukiDepartment of Hematology, Juntendo University Graduate School of Medicine, 3-1-3, Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan.
Naoki WatanabeDepartment of Hematology, Juntendo University Graduate School of Medicine, 3-1-3, Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan.
Yutaka TsukuneDepartment of Hematology, Juntendo University Graduate School of Medicine, 3-1-3, Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan.
Tadaaki InanoDepartment of Hematology, Juntendo University Graduate School of Medicine, 3-1-3, Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan.
Shintaro KinoshitaDepartment of Hematology, Juntendo University Graduate School of Medicine, 3-1-3, Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan.
Sayuri TomodaTechnology Innovation, Sysmex Corporation, 4-4-4, Takatsukadai, Nishi-ku, Kobe, 651-2271, Japan.
Kohei YamadaTechnology Innovation, Sysmex Corporation, 4-4-4, Takatsukadai, Nishi-ku, Kobe, 651-2271, Japan.
Yusuke KonishiTechnology Innovation, Sysmex Corporation, 4-4-4, Takatsukadai, Nishi-ku, Kobe, 651-2271, Japan.
Takuya KuwanaTechnology Innovation, Sysmex Corporation, 4-4-4, Takatsukadai, Nishi-ku, Kobe, 651-2271, Japan.
Takeshi SugiyamaTechnology Innovation, Sysmex Corporation, 4-4-4, Takatsukadai, Nishi-ku, Kobe, 651-2271, Japan.
Kenji FukadaTechnology Innovation, Sysmex Corporation, 4-4-4, Takatsukadai, Nishi-ku, Kobe, 651-2271, Japan.
Kazuhiro YamadaTechnology Innovation, Sysmex Corporation, 4-4-4, Takatsukadai, Nishi-ku, Kobe, 651-2271, Japan.
Miki AndoDepartment of Hematology, Juntendo University Graduate School of Medicine, 3-1-3, Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan.
Tomoiku TakakuDepartment of Hematology, Juntendo University Graduate School of Medicine, 3-1-3, Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan. ttakaku@saitama-med.ac.jp.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Early diagnosis and treatment initiation of chronic myeloid leukemia (CML) are considered to increase the rate of deep molecular response. However, the early diagnosis of CML is challenging due to the absence of clinical symptoms and peripheral blood test anomaly, especially at the timing of peripheral white blood cell count is within a normal range. This study explored the possibility of artificial intelligence (AI)-based quantitative detection of CML cells using ghost cytometry (GC) technology. We created pre-trained AI models, using the morphological information data of the peripheral blood leukocytes obtained from patients newly diagnosed with CML and healthy individuals. We applied these models to peripheral blood samples from CML patients after initiating tyrosine kinase inhibitor treatment at various time points, which contains smaller amounts of tumor cells. The AI model accurately detected CML cells and a strong correlation between AI-detected CML cells and actual BCR::ABL1

Indexed as

Artificial IntelligenceFlow CytometryLeukemia, Myelogenous, Chronic, BCR-ABL PositiveAlgorithmsFemaleFusion Proteins, bcr-ablHumansMachine LearningMaleProtein Kinase InhibitorsFusion Proteins, bcr-ablProtein Kinase Inhibitors

Identifiers

PMID40594492
PMCPMC12216930

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Registered trials

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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.