AI-Based Computational Model Integrating Routinely Acquired Blood Parameters for Triage and Early Detection of Acute Myeloid Leukemia.

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.