ArticleCancers2022
Artificial Intelligence Enhances Diagnostic Flow Cytometry Workflow in the Detection of Minimal Residual Disease of Chronic Lymphocytic Leukemia.
Article in Cancers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 39 citations in OpenAlex.
- From Time-Limited Therapy to Treatment-Free Observation: The Evolving Role of MRD in CLL Management.European journal of haematology · 2026Review
- Article
- Artificial intelligence accelerates the interpretation of measurable residual B lymphoblastic leukemia by flow cytometry.Blood advances · 2026Article
- Artificial intelligence in experimental and clinical in vitro analysis: applications, limitations, and future directions.Biotechnology notes (Amsterdam, Netherlands) · 2026Review
- Artificial intelligence in haematology laboratory diagnosis: Current applications, challenges, and future directions.African journal of laboratory medicine · 2026Review
- Article
- Reducing the workload of medical diagnosis through artificial intelligence: A narrative review.Medicine · 2025Review
- Machine Learning Methods in Clinical Flow Cytometry.Cancers · 2025Review
- Computational assessment of measurable residual disease in acute myeloid leukemia using mixture models.Communications medicine · 2024Article
- Comparison of three machine learning algorithms for classification of B-cell neoplasms using clinical flow cytometry data.Cytometry. Part B, Clinical cytometry · 2024Article
- Deep learning assists in acute leukemia detection and cell classification via flow cytometry using the acute leukemia orientation tube.Scientific reports · 2024Article
- Cytometry in High-Containment Laboratories.Methods in molecular biology (Clifton, N.J.) · 2024Review
- Deep Learning in Hematology: From Molecules to Patients.Clinical hematology international · 2024Review
- Artificial Intelligence-Assisted Diagnostic Cytology and Genomic Testing for Hematologic Disorders.Cells · 2023Review
- Hybrid Techniques for the Diagnosis of Acute Lymphoblastic Leukemia Based on Fusion of CNN Features.Diagnostics (Basel, Switzerland) · 2023Article
- Liquid biopsies and minimal residual disease in lymphoid malignancies.Frontiers in oncology · 2023Review
- The evolving use of measurable residual disease in chronic lymphocytic leukemia clinical trials.Frontiers in oncology · 2023Review
- Clinlabomics: leveraging clinical laboratory data by data mining strategies.BMC bioinformatics · 2022Review
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Flow cytometric (FC) immunophenotyping is critical but time-consuming in diagnosing minimal residual disease (MRD). We evaluated whether human-in-the-loop artificial intelligence (AI) could improve the efficiency of clinical laboratories in detecting MRD in chronic lymphocytic leukemia (CLL). We developed deep neural networks (DNN) that were trained on a 10-color CLL MRD panel from treated CLL patients, including DNN trained on the full cohort of 202 patients (F-DNN) and DNN trained on 138 patients with low-event cases (MRD < 1000 events) (L-DNN). A hybrid DNN approach was utilized, with F-DNN and L-DNN applied sequentially to cases. “Ground truth” classification of CLL MRD was confirmed by expert analysis. The hybrid DNN approach demonstrated an overall accuracy of 97.1% (95% CI: 84.7−99.9%) in an independent cohort of 34 unknown samples. When CLL cells were reported as a percentage of total white blood cells, there was excellent correlation between the DNN and expert analysis [r > 0.999; Passing−Bablok slope = 0.997 (95% CI: 0.988−0.999) and intercept = 0.001 (95% CI: 0.000−0.001)]. Gating time was dramatically reduced to 12 s/case by DNN from 15 min/case by the manual process. The proposed DNN demonstrated high accuracy in CLL MRD detection and significantly improved workflow efficiency. Additional clinical validation is needed before it can be fully integrated into the existing clinical laboratory practice.
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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.