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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

A Machine Learning Approach to the Classification of Acute Leukemias and Distinction From Nonneoplastic Cytopenias Using Flow Cytometry Data
Sara A. Monaghan, Jeng-Lin Li, Yen‐Chun Liu, et al.
American Journal of Clinical Pathology (2021) Vol. 157, Iss. 4, pp. 546-553
Closed Access | Times Cited: 26

Showing 1-25 of 26 citing articles:

Deep learning assists in acute leukemia detection and cell classification via flow cytometry using the acute leukemia orientation tube
Fu‐Ming Cheng, Shih-Chang Lo, Ching‐Chan Lin, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 9
Applications of Artificial Intelligence in Acute Promyelocytic Leukemia: An Avenue of Opportunities? A Systematic Review
Mihnea‐Alexandru Găman, Monica Dugăeșescu, Dragoş-Claudiu Popescu
Journal of Clinical Medicine (2025) Vol. 14, Iss. 5, pp. 1670-1670
Open Access | Times Cited: 1
Machine learning-based clinical decision support using laboratory data
Hikmet Can Çubukçu, Deniz İlhan Topçu, Sedef Yenice
Clinical Chemistry and Laboratory Medicine (CCLM) (2023) Vol. 62, Iss. 5, pp. 793-823
Open Access | Times Cited: 19
Detection of early-stage lung cancer in sputum using automated flow cytometry and machine learning
Madeleine E. Lemieux, Xavier T. Reveles, Jennifer Rebeles, et al.
Respiratory Research (2023) Vol. 24, Iss. 1
Open Access | Times Cited: 13
Artificial intelligence in clinical multiparameter flow cytometry and mass cytometry–key tools and progress
Franklin Fuda, Mingyi Chen, Weina Chen, et al.
Seminars in Diagnostic Pathology (2023) Vol. 40, Iss. 2, pp. 120-128
Closed Access | Times Cited: 13
Validation of Artificial Intelligence (AI)-Assisted Flow Cytometry Analysis for Immunological Disorders
Zhengchun Lu, Mayu Morita, Tyler S. Yeager, et al.
Diagnostics (2024) Vol. 14, Iss. 4, pp. 420-420
Open Access | Times Cited: 5
A Simple Machine Learning-Based Quantitative Structure–Activity Relationship Model for Predicting pIC50 Inhibition Values of FLT3 Tyrosine Kinase
Jackson J. Alcázar, Ignacio Moreno-Torres Sánchez, Cristian Merino, et al.
Pharmaceuticals (2025) Vol. 18, Iss. 1, pp. 96-96
Open Access
Future Applications of Flow Cytometry and Related Techniques
Marie C. Béné, Anna Porwit, Francis Lacombe
Cambridge University Press eBooks (2025), pp. 219-232
Closed Access
Machine Learning Methods in Clinical Flow Cytometry
Nicholas C. Spies, Alexandra E. Rangel, Paul English, et al.
Cancers (2025) Vol. 17, Iss. 3, pp. 483-483
Open Access
Clinical validation of a real‐time machine learning‐based system for the detection of acute myeloid leukemia by flow cytometry
Lauren M. Zuromski, Jacob Durtschi, A Abdul Aziz, et al.
Cytometry Part B Clinical Cytometry (2025)
Open Access
Artificial intelligence applications in pediatric oncology diagnosis
Yuhan Yang, Yimao Zhang, Yuan Li
Exploration of Targeted Anti-tumor Therapy (2023), pp. 157-169
Open Access | Times Cited: 9
Hematology and Machine Learning
Amrom E. Obstfeld
The Journal of Applied Laboratory Medicine (2022) Vol. 8, Iss. 1, pp. 129-144
Closed Access | Times Cited: 13
Machine learning applications and challenges in graft-versus-host disease: a scoping review
Ali Mushtaq, Areez Shafqat, Haneen Salah, et al.
Current Opinion in Oncology (2023) Vol. 35, Iss. 6, pp. 594-600
Closed Access | Times Cited: 6
Acute Leukemia Prediction and Classification using Convolutional Neural Network and Generative Adversarial Network
Jiunn-Woei Lian, Chi-Hung Wei, Mu‐Yen Chen, et al.
Applied Soft Computing (2024) Vol. 163, pp. 111819-111819
Closed Access | Times Cited: 2
The Potential Promise of Machine Learning in Myelodysplastic Syndrome
Valeria Visconte, Jaroslaw P. Maciejewski, Luca Guarnera
Seminars in Hematology (2024)
Closed Access | Times Cited: 2
Acute Myeloid Leukemia: Diagnosis and Evaluation by Flow Cytometry
Feras Ally, Xueyan Chen
Cancers (2024) Vol. 16, Iss. 22, pp. 3855-3855
Open Access | Times Cited: 2
Artificial Intelligence for Clinical Flow Cytometry
Robert P. Seifert, David Gorlin, Andrew Borkowski
Clinics in Laboratory Medicine (2023) Vol. 43, Iss. 3, pp. 485-505
Closed Access | Times Cited: 4
Automated analysis of flow cytometry data with minimal training files: Research evaluation of an elastic image registration algorithm for TBNK, stem cell enumeration, and lymphoid screening tube assays
Allison Irvine, S. A. J. Tahir, V. Tripathi, et al.
Cytometry Part B Clinical Cytometry (2024)
Open Access | Times Cited: 1
A cell-level discriminative neural network model for diagnosis of blood cancers
Edgar E. Robles, Jin Ye, Padhraic Smyth, et al.
Bioinformatics (2023) Vol. 39, Iss. 10
Open Access | Times Cited: 3
Using Artificial Intelligence to Interpret Clinical Flow Cytometry Datasets for Automated Disease Diagnosis and/or Monitoring
Yu‐Fen Wang, Jeng-Lin Li, Chi-Chun Lee, et al.
Methods in molecular biology (2024), pp. 353-367
Closed Access
Critical test result management at Danish hospital laboratories: a national survey
Kaja Kastberg Faurø, Steen Antonsen, Mads Nybo
Scandinavian Journal of Clinical and Laboratory Investigation (2024) Vol. 84, Iss. 3, pp. 174-182
Closed Access
Introduction
Paul K. Wallace
(2024), pp. 139-143
Closed Access
Future Cytometric Technologies and Applications
Holden T. Maecker
(2024), pp. 249-252
Closed Access
A Chunking-for-Pooling Strategy for Cytometric Representation Learning for Automatic Hematologic Malignancy Classification
Jeng-Lin Li, Yun-Chun Lin, Yu‐Fen Wang, et al.
IEEE Journal of Biomedical and Health Informatics (2022) Vol. 26, Iss. 9, pp. 4773-4784
Closed Access | Times Cited: 1
A cell-level discriminative neural network model for diagnosis of blood cancers
Edgar E. Robles, Jin Ye, Padhraic Smyth, et al.
medRxiv (Cold Spring Harbor Laboratory) (2023)
Open Access
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