Romano Nicole H, Ruiz Christian, Schlaepfer Pascal, Balabanov Stefan, Habringer Stefan, Widmer Corinne C
Moonlight AI, Courroux, Jura, Switzerland.
Department of Haematology and Laboratory Medicine, University and University Hospital Basel, Petersgraben 4, Basel, 4031, Switzerland.
Ann Hematol. 2025 Aug 19. doi: 10.1007/s00277-025-06533-5.
Myelodysplastic syndrome / neoplasm (MDS) presents a diagnostic challenge due to the need of expert morphological analysis, and the reliance on genomic analysis of collected bone marrow material for the definite diagnosis. This study aimed to facilitate this process by developing a computer vision AI-based model that is capable of diagnosing MDS from images from peripheral blood smears (PBS). We used a cohort of 43,371 neutrophils from 84 MDS and 60 non-MDS samples to train a neutrophil classifier to differentiate between dysplastic and non-dysplastic cells. The model was initially fed with PBS images from patients with prominent MDS (pMDS) and further refined to detect non-prominent MDS (npMDS), i.e., without clear-cut dysplastic features in their neutrophils. The model learning was only based on the single-cell annotation of the neutrophils from pMDS, without human-generated morphological features as input. The trained neutrophil classifier achieved an overall accuracy of 94%, with a sensitivity and specificity of 0.95 and 0.94, respectively. On a patient-level, the model correctly identified 91 out of the 94 samples, with a sensitivity and specificity of 0.98 and 0.96, respectively, and AUC of 0.999. In npMDS, 43 out of the 44 samples were correctly identified. Our study demonstrates the potential of AI-based models to improve the efficiency of MDS diagnostics. Our model runs on standard CPUs, offering an accessible solution that can be integrated into existing clinical workflows and potentially reduces the dependence on specialized morphologists and genomic analysis from bone marrow punctures.
骨髓增生异常综合征/肿瘤（MDS）由于需要专家进行形态学分析，且确诊依赖于对采集的骨髓材料进行基因组分析，因此带来了诊断挑战。本研究旨在通过开发一种基于计算机视觉人工智能的模型来促进这一过程，该模型能够从外周血涂片（PBS）图像中诊断MDS。我们使用了来自84例MDS和60例非MDS样本的43371个中性粒细胞队列来训练中性粒细胞分类器，以区分发育异常和非发育异常的细胞。该模型最初输入的是来自显著MDS（pMDS）患者的PBS图像，并进一步优化以检测非显著MDS（npMDS），即中性粒细胞无明确发育异常特征的情况。模型学习仅基于pMDS中性粒细胞的单细胞注释，没有将人工生成的形态学特征作为输入。训练后的中性粒细胞分类器总体准确率达到94%，灵敏度和特异性分别为0.95和0.94。在患者水平上，该模型在94个样本中正确识别出91个，灵敏度和特异性分别为0.98和0.96，曲线下面积（AUC）为0.999。在npMDS中，44个样本中的43个被正确识别。我们的研究证明了基于人工智能的模型在提高MDS诊断效率方面的潜力。我们的模型在标准CPU上运行，提供了一种可访问的解决方案，可以集成到现有的临床工作流程中，并有可能减少对专业形态学家和骨髓穿刺基因组分析的依赖。
