ArticleScientific reports2020
Artificial Intelligence based Models for Screening of Hematologic Malignancies using Cell Population Data.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 18 papers, 1 of them a synthesis that pooled 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.
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
18 citing papers in PubMed, 1 synthesis or guideline pooled it, 44 citations in OpenAlex.
- Machine Learning for Multi-Omics Characterization of Blood Cancers: A Systematic Review.Cells · 2025Pooled it
- Clinical validation of the Hilab lens for AI supported point of care CBC testing in pediatric oncology.Communications medicine · 2025Article
- Integrating Cell Population Data With Hematology Analyzer Messages for Improved Detection of Immature White Blood Cells and Laboratory Efficiency.Journal of clinical laboratory analysis · 2025Article
- Machine learning-based models for screening of anemia and leukemia using features of complete blood count reports.Scientific reports · 2025Article
- Reference intervals for leukocyte cell population data in healthy adults in Zigong region, China.Scientific reports · 2025Article
- A novel method for screening malignant hematological diseases by constructing an optimal machine learning model based on blood cell parameters.BMC medical informatics and decision making · 2025Article
- Single cell RNA sequencing improves the next generation of approaches to AML treatment: challenges and perspectives.Molecular medicine (Cambridge, Mass.) · 2025Review
- Comprehensive application of AI algorithms with TCR NGS data for glioma diagnosis.Scientific reports · 2024Article
- Performance evaluation and clinical application of the UA-5600 automatic dry chemistry urine analyzer in renal diseases.Translational andrology and urology · 2024Article
- Article
- Bio-inspired microfluidics: A review.Biomicrofluidics · 2023Review
- Artificial Intelligence-Assisted Diagnostic Cytology and Genomic Testing for Hematologic Disorders.Cells · 2023Review
- Elements for assistance to patients with hematological malignancies to propose care lines: a scoping review.Revista brasileira de enfermagem · 2023Article
- Article
- Applications of machine learning in routine laboratory medicine: Current state and future directions.Clinical biochemistry · 2022Review
- Beyond the In-Practice CBC: The Research CBC Parameters-Driven Machine Learning Predictive Modeling for Early Differentiation among Leukemias.Diagnostics (Basel, Switzerland) · 2022Article
- Artificial Intelligence in Surveillance, Diagnosis, Drug Discovery and Vaccine Development against COVID-19.Pathogens (Basel, Switzerland) · 2021Review
- Assessing the risk of dengue severity using demographic information and laboratory test results with machine learning.PLoS neglected tropical diseases · 2020Article
Corrections and comments
- Erratum issued
Authors and funding
9 authors at 6 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cell Population Data (CPD) provides various blood cell parameters that can be used for differential diagnosis. Data analytics using Machine Learning (ML) have been playing a pivotal role in revolutionizing medical diagnostics. This research presents a novel approach of using ML algorithms for screening hematologic malignancies using CPD. The data collection was done at Konkuk University Medical Center, Seoul. A total of (882 cases: 457 hematologic malignancy and 425 hematologic non-malignancy) were used for analysis. In our study, seven machine learning models, i.e., SGD, SVM, RF, DT, Linear model, Logistic regression, and ANN, were used. In order to measure the performance of our ML models, stratified 10-fold cross validation was performed, and metrics, such as accuracy, precision, recall, and AUC were used. We observed outstanding performance by the ANN model as compared to other ML models. The diagnostic ability of ANN achieved the highest accuracy, precision, recall, and AUC ± Standard Deviation as follows: 82.8%, 82.8%, 84.9%, and 93.5% ± 2.6 respectively. ANN algorithm based on CPD appeared to be an efficient aid for clinical laboratory screening of hematologic malignancies. Our results encourage further work of applying ML to wider field of clinical practice.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.