Deep learning ResNet34 model-assisted diagnosis of sickle cell disease via microcolumn isoelectric focusing

Traditional methods for sickle cell disease (SCD) screening can be inaccurate and misleading, and the early and accurate diagnosis of SCD is crucial for effective management and treatment. Although microcolumn isoelectric focusing (mIEF) is effective, the hemoglobinopathies must be accurately identified, wherein skilled personnel are required to analyse the bands in mIEF. Further automating and standardizing the diagnostic methods via AI to identify abnormal Hbs would be a useful endeavor. In this study, we propose a novel approach for SCD diagnosis by integrating the high throughput capability of ResNet34 in image analysis, as a deep learning convolutional neural network, for the precise separation of Hb variants using mIEF. Initially, SCD blood samples were subjected to mIEF and the resulting patterns were then captured as digital images. The sensitivity and specificity of the mIEF analysis were 100% and 97.8%, respectively, with a 99.39% accuracy. Comparison with HPLC showed a strong linear correlation (R² = 0.9934), good agreement with the Bland-Altman plot (average difference ± 1.96 SD, bias = 9.89%) and a 100% match with the DNA analysis. Subsequently, the mIEF images were then input into the ResNet34 model, pre-trained on a large dataset, for feature extraction and classification. The integration of ResNet34 with mIEF demonstrated promising results in terms of precision (90.1%) and accuracy in distinguishing between the various SCD conditions. Overall, the proposed method offers a more effective, automated, and reduced cost approach for SCD diagnosis, which could potentially streamline diagnostic workflows and mitigate the subjectivity and variability inherent in manual assessments.