ArticleInternational journal of laboratory hematology2025
The Application of Artificial Intelligence-Based Bone Marrow Cell Analysis System in Pediatric Hematological Diseases.
Article in International journal of laboratory hematology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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.
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.
Who cites it
2 citing papers in PubMed.
- Next Generation Digital Morphology: Blast Preclassification in Bone Marrow Aspirates.International journal of laboratory hematology · 2026Review
- The Application of Artificial Intelligence-Based Bone Marrow Cell Analysis System in Pediatric Hematological Diseases.International journal of laboratory hematology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
introductionThe clinical diagnosis of hematological diseases depends on the differential count of nucleated cells on the bone marrow (BM) smears, and an artificial intelligence (AI)-based system was applied to automatically classify BM nucleated cells in pediatric hematological disease samples in this study.
methodsThe BM aspirate smears were collected from 213 pediatric patients (under 18 years old) at Harbin Medical University Affiliated Sixth Hospital from October 2023 to June 2024. The entire smear of BM was scanned by a ×40 objective lens to obtain complete digital images using an automated analysis method named Morphogo. Next, Morphogo was used to capture nucleated cells in an area of BM smears that was selected by hematopathologists, with a magnification of ×100 objective lens.
resultsMorphogo demonstrated a high overall accuracy (> 87.8%) in pre-classifying nucleated cells in BM aspirate smears obtained from masked pediatric patients. In addition, the average values of sensitivity and accuracy in Morphogo cell classification were remarkably high. Moreover, Morphogo could reduce the time costs on classifying BM nucleated cells. Besides, there were positive correlations between Morphogo and manual categorization for immunologic thrombocytopenic purpura, BM failure, hyperplastic anemia, acute leukemia, chronic myeloid leukemia, and other hematological diseases.
conclusionThis research demonstrated the clinical potential of the Morphogo in early screening of pediatric hematological diseases and its reliability as an automated tool for differential counting and analysis of BM nucleated cells.
Indexed as
Identifiers
What Socratic holds
Registered trials
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 Socratic graph.