Development of 3D Intelligent Quantitative Phase Microscope for Sickle Cells Screening

Abstract

Sickle cell disease (SCD) is a genetic blood disorder causing red blood cells to deform into a sickle shape, often leading to misdiagnosis. Early detection is crucial, but traditional screening is slow and labor-intensive. This paper introduces an intelligent microscope system for automated SCD screening, reducing manual intervention. The system uses an interferometric method to capture high-resolution 3D phase images, combined with a deep learning-based UNET model for semantic segmentation of sickle and healthy cells. Various machine-learning models classify RBCs, with the Gradient boosting model achieving 94.9% accuracy. The system is scalable, user-friendly, and well suited for resource-limited settings, offering a faster, more reliable diagnostic tool. This innovation not only improves SCD detection but also sets the stage for AI-driven haematological diagnostics. Future advancements will enhance system robustness and undergo extensive clinical validation.