Prediction of hemoglobin levels and red blood cell parameters using deep learning on blood smear images

Abstract

Objective

Hemoglobin (HGB), hematocrit (HCT), and red blood cell (RBC) concentration are critical parameters for diagnosing anemia, which is traditionally measured using automated hematology analyzers. Recent advancements in deep learning in blood cell analysis have primarily focused on the classification of white blood cells, yet there is limited research on the quantitative prediction of HGB and RBC parameters from peripheral blood smear images. We aimed to develop and validate deep learning models to predict HGB, HCT, and RBC levels directly from blood smear images, offering a potential low-cost alternative to traditional methods.

Methods

A multicenter dataset of over 13,000 stained blood smear images paired with corresponding hematology analysis results was collected from 3 medical centers between March 2021 and March 2022. Deep learning models, based on the ResNet18 architecture, were trained to quantitatively predict HGB, HCT, and RBC levels. The models were evaluated using Pearson’s correlation coefficient (PCC), R-squared (R²) values, and mean absolute error (MAE). Tenfold cross-validation was employed to ensure robustness, and external validation was performed on 2 independent datasets.

Results

The deep learning models demonstrated strong performance in predicting HGB (PCC = 0.977, R² = 0.954, MAE = 4.269 g/L), HCT (PCC = 0.982, R² = 0.965, MAE = 0.013 L/L), and RBC concentration (PCC = 0.982, R² = 0.964, MAE = 0.155 × 10¹²/L). The models could also predict RBC concentration from single images with varying cell densities, leveraging the global cell distribution patterns. External validation showed consistent performance, with PCCs exceeding 0.9 for HGB, HCT, and RBC predictions.

Conclusion

This study demonstrated the feasibility of using deep learning models to predict HGB, HCT, and RBC levels directly from blood smear images, offering a novel, cost-effective, and efficient method for hematology analysis.