ArticleBiomedical engineering and computational biology2026
AI-Based Computational Model Integrating Routinely Acquired Blood Parameters for Triage and Early Detection of Acute Myeloid Leukemia.
Article in Biomedical engineering and computational biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Acute Myelogenous Leukemia (AML) is a rapidly progressing blood and bone marrow cancer, prevalent among adults. Very low five-year survival rate, non-specific and ambiguous symptoms, and lack of potent screenings make early detection and prompt treatment crucial, especially for younger patients. The need for multiple tests to confirm AML and possible misdiagnosis often leads to multiple consultations before moving to the next stage of investigations. This can create operational bottlenecks in the hematology department, leading to further delays in the diagnostic pathway. Objective: To develop a lightweight artificial intelligence (AI) model using complete blood count (CBC) data for early AML detection and development of a decision support system (DSS) for triage support. Methods: The data for this study were retrieved retrospectively from the National Center for Cancer Care and Research (NCCCR), Qatar (2016-2022). We used 510 CBC data records of AML and non-AML individuals. Statistical analysis of CBC features (mean ± SD) for AML and control patients was performed, followed by principal component analysis (PCA) to assess the predictive ability of CBC alone. Results: Rigorous feature selection and model tuning resulted in a predictive diagnostic Support Vector Machine (SVM) model to alleviate delays in the AML care pathway. Employing a five-fold cross-validation approach, achieved an accuracy of 96.4% (S.D. 0.029) for test set and 80% (S.D. 0.036) for validation set. This 80% accuracy on the validation set, with high sensitivity (100% recall) but lower precision (77.1%), represents an acceptable triage trade-off, although it may increase follow-up referrals and workload. The developed model showed encouraging performance when used to detect AML using CBCs taken up to 1-year prior diagnosis. Conclusion: This study emphasizes that, with routine CBC data, we can enable better patient screening and referral in the initial stages of triage through a cost-effective, AI-based decision support system that provides complementary support to doctors for timely AML detection.
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.