Smartphone-based AI for Rapid Blood Smatter Analysis in Hematology Laboratories

Thesis Abstract

In hematology laboratories, timely and accurate peripheral blood smear (PBS) analysis is essential for diagnosing hematologic disorders, yet traditional microscopy is time-consuming and dependent on skilled personnel, leading to variability in interpretation. This study addresses the bottleneck by developing and evaluating a smartphone-based artificial intelligence (AI) platform that automates rapid PBS digitization, anomalous cell detection, and classification to support frontline laboratory diagnostics. The aim is to determine whether a mobile AI workflow can achieve diagnostic accuracy comparable to expert manual review while reducing turnaround time and inter-operator variability. Specific objectives include (1) designing a mobile data acquisition protocol for high-resolution PBS imaging using a standard smartphone coupled with a compact optical adapter, (2) training and validating a convolutional neural network (CNN) model for red blood cell morphology classification, white blood cell differential counting, and abnormal cell detection using a multi-class segmentation approach, (3) evaluating the platform’s diagnostic performance against a gold standard hematopathologist reference in a prospective cohort, (4) assessing operational efficiency gains in turnaround time and user satisfaction among laboratory technicians, and (5) conducting a preliminary cost-benefit analysis for integration into routine workflow. The methodology adopts a cross-sectional, diagnostic accuracy study design conducted in two tertiary care hospital hematology laboratories from a total population of approximately 12,000 PBS slides analyzed per year. A sample of 1,200 PBS slides will be collected over 12 months, with stratified sampling to ensure representation of common abnormalities (anemia, leukocytosis, thrombocytopenia, dysplasia) and rare conditions (blast cells, teardrop cells). Data collection instruments include a smartphone-based imaging module (Samsung Galaxy S23 with a 4× and 10× adapter), a standardized slide preparation protocol, a cloud-based annotation platform for ground-truth labeling by three board-certified hematologists, and a laboratory information system (LIS) integration module. The AI model builds upon a two-stage architecture (i) a trained U-Net-like segmentation network for pixel-level delineation of leukocytes, erythrocytes, platelets, and anomalous structures, and (ii) a CNN classifier (ResNet-50 backbone) for hierarchical classification of cell types and abnormal findings. Model training employs 80/20 training/testing splits with 5-fold cross-validation, data augmentation (rotation, brightness, contrast, tilt), and class-balancing strategies to address rare cell types. Prior to deployment, the model undergoes external validation on an independent dataset from another hospital to assess generalizability. Validity and reliability are established through inter-annotator agreement measures (Cohen’s kappa) for ground-truth labels, calibration analyses for probabilistic outputs, and perturbation testing to evaluate robustness to imaging variance. Data analysis includes descriptive statistics for sample characteristics, diagnostic accuracy metrics (sensitivity, specificity, positive predictive value, negative predictive value) for PBS-based AI outputs against the hematopathologist reference, and area under the receiver operating characteristic (ROC) curves for multi-class and binary classifications. Regression analysis will examine the relationship between AI-derived slide-level scores and expert-perceived diagnostic confidence, while time-to-decision comparisons will quantify turnaround time reductions. A mixed-methods component collects qualitative feedback from technologists via thematic analysis on perceived usability, with theoretical anchoring in Technology Acceptance Model (TAM) and Diffusion of Innovations (DOI) theory to interpret adoption potential. Expected findings anticipate that the smartphone-based AI system achieves sensitivity and specificity above 92% for major PBS abnormalities, with ROC-AUC values exceeding 0.95 for key cell categories, and a reduction in average turnaround time by 40–60% relative to manual review. Secondary outcomes include a measurable decrease in inter-operator variability (kappa improvement from 0.72 to above 0.85) and high user satisfaction scores (System Usability Scale ? 75). The study contributes to knowledge by demonstrating the feasibility and clinical validity of mobile AI-enabled PBS analysis, detailing a scalable framework for point-of-care hematology diagnostics, and providing a cost-benefit perspective for resource-limited settings. The main conclusion is that smartphone-based AI for rapid PBS analysis can augment expert interpretation, streamline laboratory workflows, and improve diagnostic consistency, while preserving data integrity and enabling real-time decision support. Recommendations address standardization of slide preparation, firmware-software integration with LIS, ongoing model recalibration with local data, regulatory clearance pathways, and phased implementation plans across diverse laboratory environments.