Integration of Artificial Intelligence Algorithms with Automated Hematology Analyzers to Enhance Differential Diagnosis of Anemia Subtypes
Sivan Bakr Askandar
Abstract:
Anaemia is one of the most widespread haematological disorders, arising from diverse causes such as nutrient deficiencies, inherited conditions of red blood cells, and systemic chronic illnesses. Conventional diagnostic strategies typically depend on complete blood count (CBC) indices, red cell distribution width (RDW), haemoglobin concentration, and manual review of peripheral blood smears. Although these techniques are central to clinical evaluation, they often prove insufficient when anaemia subtypes share overlapping morphological patterns or present borderline laboratory results.To overcome these limitations, this study proposes a hybrid diagnostic model that merges advanced artificial intelligence (AI) algorithms with automated haematology analysers. The framework combines multiple machine learning approaches—support vector machines, convolutional neural networks, and ensemble boosting methods—applied to parameters generated by automated analysers, together with digitised smear images and supplementary biochemical markers including ferritin, serum iron, and transferrin saturation. Data were obtained from large and heterogeneous cohorts across tertiary medical centres to ensure broad applicability and clinical robustness. The hybrid AI-assisted system demonstrated a clear advantage over conventional workflows. In particular, convolutional neural networks significantly enhanced recognition of iron-deficiency anaemia compared with thalassaemia minor, improving diagnostic precision by over 12%. Ensemble approaches further contributed to the accurate identification of anaemia of chronic disease in scenarios complicated by elevated inflammatory markers. Additionally, probabilistic outputs from the models provided clinicians with improved guidance for confirmatory testing and patient management. Rather than substituting clinical expertise, the integrated platform functions as a supportive tool, delivering consistent and reproducible results while reducing reporting time and laboratory workload. Overall, embedding AI within automated haematology analysers holds promise for earlier detection, better classification of anaemia subtypes, and optimisation of therapeutic decisions. Future investigations should focus on real-time implementation in clinical laboratories and evaluation of the system’s performance in resource-limited healthcare environments.
Keywords:
Differential diagnosis; Anaemia; Machine learning; Peripheral blood smear; Artificial intelligence.