NeoJaundice-AI: Smartphone-Based Neonatal Jaundice Detection Using Dual-Input Deep Learning and Synthetic Augmentation
This study addresses the critical challenges of limited laboratory access for neonatal jaundice screening in resource-constrained settings—particularly rural India—and the diagnostic difficulties posed by darker skin tones. To this end, the authors present the first end-to-end offline AI system tailored for Indian newborns, leveraging smartphone-captured images of both skin and sclera. The approach employs a dual-branch EfficientNet-B0 architecture that fuses handcrafted YCbCr features with deep features, enhanced by skin-tone-adaptive normalization and synthetic jaundice data augmentation to mitigate data scarcity. After INT8 quantization and ONNX deployment, the model occupies only 8.3 MB and achieves inference times under 3 seconds on standard Android devices, delivering a jaundice classification accuracy of 91.8% (sensitivity: 93.5%) and a mean absolute error of 1.4 mg/dL in serum bilirubin prediction.