Deep-Learning Analysis of Peripheral Blood Smear for Prediction of Acute Inflammatory States and Chronic Comorbidities: Development and Internal Validation Study

Background Peripheral blood smear (PBS) examination provides morphological information on immune cells that reflects systemic pathophysiology, yet whether it can be used to predict systemic states has not been systematically established. Objective This study aimed to develop and evaluate an integrated deep learning framework that leverages cell-level morphology from routine PBS examinations to discriminate acute inflammatory states and chronic comorbidities. Methods An integrated deep learning framework was developed for PBS analysis, in which cell-level morphological features were extracted and aggregated into slide-level representations to predict systemic states. A total of 51,369 PBS examinations were used for self-supervised cell-level pretraining and 9211 for slide-level downstream tasks. Outcomes comprised acute inflammatory states and chronic comorbidities. Acute inflammatory states included white blood cell (WBC) count (categorized as low, normal, or high), elevated C-reactive protein (CRP), elevated erythrocyte sedimentation rate (ESR), and septic shock. Chronic comorbidities included the Charlson Comorbidity Index (CCI), cardiovascular disease (CVD), chronic kidney disease (CKD), and diabetes mellitus (DM). Discriminative performance was assessed using the area under the receiver operating characteristic curve (AUROC) with 95% CIs, computed on the test set of the development dataset and on the holdout dataset. The framework was compared with a demographic model based on age and sex and with a cell-type-only model without morphological features. As a secondary analysis, temporal validation was performed using a chronological split. Results The framework discriminated acute inflammatory states, achieving development and holdout AUROCs of 0.852 (95% CI 0.836‐0.867) and 0.849 (0.833‐0.864) for WBC count, 0.891 (0.870‐0.913) and 0.896 (0.876‐0.915) for CRP, 0.838 (0.794‐0.878) and 0.844 (0.799‐0.882) for ESR, and 0.849 (0.807‐0.886) and 0.822 (0.783‐0.853) for septic shock. Discrimination of chronic comorbidities was more modest but consistent across the 2 datasets, with AUROCs of 0.792 (0.762‐0.818) and 0.781 (0.751‐0.808) for CCI, 0.696 (0.665‐0.727) and 0.680 (0.654‐0.708) for CVD, 0.724 (0.690‐0.755) and 0.728 (0.696‐0.756) for CKD, and 0.645 (0.610‐0.680) and 0.638 (0.603‐0.674) for DM. The framework significantly outperformed the cell-type-only model for all outcomes in both datasets but did not significantly outperform the demographic model for CVD in the holdout dataset or for DM in either dataset. AUROCs in the temporal holdout dataset ranged from 0.621 for DM to 0.883 for CRP, consistent with those in the temporal development test set (0.666 to 0.889). Conclusions An integrated deep learning framework for automated PBS analysis can provide information associated with inflammation-related laboratory results and clinical outcomes, with stronger discrimination for acute inflammatory states than for chronic comorbidities. These exploratory findings suggest the potential of deep learning-based PBS analysis as a novel approach to assessing systemic states in clinical practice.