Development and multicenter validation of an AI-driven detection system for DLBCL cells in bone marrow smears.

The presence of diffuse large B-cell lymphoma (DLBCL) cells in bone marrow (BM) smears is recognised as a key morphological basis for diagnosing BM involvement by DLBCL. BM involvement directly affects disease staging, treatment strategies, and prognosis assessment. Nevertheless, traditional manual identification of DLBCL cells in BM smears under a microscope is time-consuming, subject to observer variability, and lacks standardization between hospitals. In this study, we developed an artificial intelligence (AI) detection system (DLBCL-CQnet) based on deep learning, using BM smears from 117 patients with newly diagnosed DLBCL across four centers. DLBCL-CQnet comprises a region of interest (ROI) classification model based on MobileNetV2, and a cell detection and classification model (CDC-MOD) based on YOLOv8. Performance was evaluated using accuracy, precision, recall, F1-score, and mean average precision at an intersection over union threshold of 0.5 (mAP50). MobileNetV2 achieved the best ROI classification performance (accuracy 0.925, precision 0.922, F1-score 0.919), while YOLOv8 yielded a classification accuracy of 0.890 and an mAP50 of 0.934 for DLBCL cell detection. The system achieved an ROI classification accuracy of 0.925, a cell classification accuracy of 0.890, and a mean average precision (mAP50) of 0.934 at an intersection-over-union (IoU) threshold of 0.5. The optimal cutoff for identifying BM infiltration by DLBCL cells was 2.12%, with a specificity of 0.941 and positive predictive value (PPV) of 0.923, demonstrating high diagnostic value. The recognition speed significantly outperforms manual assessment, substantially reducing time consumption. DLBCL-CQnet enables automated, standardized identification of DLBCL cells in BM smears across institutions, serving as a reliable auxiliary diagnostic tool for assessing BM involvement by DLBCL. Its high specificity and operational efficiency streamline diagnostic workflows, especially in resource-constrained environments.