Accurate detection of abnormal hemoglobin variations is paramount for early diagnosis of sickle cell disease (SCD) in newborns. Traditional methods using isoelectric focusing (IEF) with agarose gels are technician-dependent and face limitations like inconsistent image quality and interpretation challenges. This study proposes a groundbreaking solution using deep learning (DL) and artificial intelligence (AI) while ensuring human guidance throughout the process. The system analyzes IEF gel images with convolutional neural networks (CNNs), achieving over 98% accuracy in identifying various SCD profiles, far surpassing the limitations of traditional methods. Furthermore, the system addresses ambiguities by incorporating an “Unconfirmed” category for unclear cases and assigns probability values to each classification, empowering clinicians with crucial information for informed decisions. This AI-powered tool, named SCScreen, seamlessly integrates machine learning with medical expertise, offering a robust, efficient, and accurate solution for SCD screening. Notably, SCScreen tackles the previously challenging diagnosis of major sickle cell syndromes (SDM) in newborns. This research has the potential to revolutionize SCD management. By strengthening screening platforms and potentially reducing costs, SCScreen paves the way for improved healthcare outcomes for newborns with SCD, potentially saving lives and improving the quality of life for affected individuals.

SCD; IEF; agarose gel; CNN; visualization; healthcare data analysis

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