AI-Driven Portable Hematology Analyzer for Point-of-Ccare Labs

Thesis Abstract

In resource-limited healthcare settings, delays in diagnosing hematological conditions and the logistical constraints of centralized laboratories impede timely decision-making and worsen patient outcomes; this study addresses the need for an AI-driven portable hematology analyzer capable of delivering rapid, accurate complete blood counts at the point of care. The aim is to design, validate, and evaluate a compact device that integrates computer vision, machine learning, and microfluidics to perform automated hematology analysis with clinician-friendly interfaces. Specific objectives include (i) developing an end-to-end hardware-software platform incorporating a smartphone-based imaging module, a microfluidic chamber, and embedded AI algorithms for differential white blood cell counting and red blood cell indices; (ii) validating analytical performance against gold-standard hematology analyzers across 1,000 patient samples collected from three tertiary hospitals; (iii) assessing diagnostic accuracy for common hematologic disorders (anemia, leukocytosis, thrombocytopenia) using classifier models; (iv) evaluating device usability, data interoperability with electronic health records, and data security; and (v) conducting a health economics analysis to estimate cost-per-test and potential impact on turnaround times. The methodology adopts a mixed-methods, cross-sectional design. The population comprises adult patients presenting with indications for hematology testing at tertiary care centers. A stratified random sample of 1,000 blood specimens (approximately 400 from hematology clinics, 300 from emergency departments, and 300 from inpatient wards) will be analyzed. Instrumentation includes a portable hematology analyzer prototype with guided imaging, a calibrated microfluidic cartridge, and software implementing surface-enhanced image processing, CNN-based cell classification, and ensemble regression for quantitative indices. Validity and reliability will be established through calibration against reference analyzers, with repeatability assessments across 10 repeated measurements per sample and inter-device equivalence testing using Bland-Altman plots. Data will be analyzed using regression analysis to compare key parameters (e.g., hemoglobin, hematocrit, mean corpuscular volume) against gold standards, ANOVA to explore differences across clinical settings, and receiver operating characteristic (ROC) analysis to evaluate diagnostic performance for anemia and thrombocytopenia. A multivariate logistic regression model will assess predictors of diagnostic concordance between the portable device and standard analyzers. The theoretical framework integrates the Technology Acceptance Model (TAM) to evaluate usability and adoption, and the Diffusion of Innovations (DOI) theory to interpret integration into clinical workflows. A conceptual model illustrating the interaction between sensor data quality, AI inference, user interaction, and clinical decision support will be developed. The anticipated findings include high analytic concordance with standard analyzers (mean biases within clinically acceptable limits hemoglobin ±0.5 g/dL, white blood cell count ±0.5x10^9/L, platelets ±20x10^9/L), robust classification accuracy for major hematologic conditions (AUC >0.90 for anemia and thrombocytopenia detection), and favorable usability scores (System Usability Scale >70). The study is expected to demonstrate reductions in mean turnaround time by 40–60% and cost-per-test reductions of 10–25% in routine workflows, considering equipment amortization and supply costs. The contribution to knowledge lies in providing an empirically validated framework for deploying AI-enabled point-of-care hematology, advancing nano/microfluidic hardware integration, and offering a scalable model for technology-driven diagnostics in low-resource environments. The main conclusion is that a well-designed AI-driven portable hematology analyzer can achieve clinically reliable performance, integrate seamlessly with existing health information systems, and meaningfully improve access to timely hematology testing. Recommendations include pursuing regulatory pathway validation, expanding pathogen-inactivation and sample multiplexing capabilities, deploying larger-scale longitudinal trials to assess impact on clinical outcomes, and developing standardized interoperability protocols to facilitate cross-institution data sharing and continuous AI model updates.