Abstract
In this study, we developed the world's first artificial intelligence (AI) system that assesses the dysplasia of blood cells on bone marrow smears and presents the result of AI prediction for one of the most representative dysplasia—decreased granules (DG). We photographed field images from the bone marrow smears from patients with myelodysplastic syndrome (MDS) or non-MDS diseases and cropped each cell using an originally developed cell detector. Two morphologists labelled each cell. The degree of dysplasia was evaluated on a four-point scale: 0–3 (e.g., neutrophil with severely decreased granules were labelled DG3). We then constructed the classifier from the dataset of labelled images. The detector and classifier were based on a deep neural network pre-trained with natural images. We obtained 1797 labelled images, and the morphologists determined 134 DGs (DG1: 46, DG2: 77, DG3: 11). Subsequently, we performed a five-fold cross-validation to evaluate the performance of the classifier. For DG1–3 labelled by morphologists, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy were 91.0%, 97.7%, 76.3%, 99.3%, and 97.2%, respectively. When DG1 was excluded in the process, the sensitivity, specificity, PPV, NPV, and accuracy were 85.2%, 98.9%, 80.6%, and 99.2% and 98.2%, respectively.
Highlights
Diagnosis of MDS, is defined as 10% or more of dysplastic cells in each cell lineage, or 5–20% of myeloblasts in all nucleated c ells[21]
It is necessary to develop an artificial intelligence (AI) system that assists with the morphological assessment of dysplasia in bone marrow smears, but no reports exist in this field to date
We have described an intelligent system that can determine a type of dysplasia in bone marrow smears, and to the best of our knowledge, this is the first study to do so
Summary
Diagnosis of MDS, is defined as 10% or more of dysplastic cells in each cell lineage, or 5–20% of myeloblasts in all nucleated c ells[21]. Expert skill in morphological examination is required for the accurate diagnosis of dysplasia in bone marrow smears; such expert review is not always available in daily clinical practice. It is necessary to develop an artificial intelligence (AI) system that assists with the morphological assessment of dysplasia in bone marrow smears, but no reports exist in this field to date. We developed an AI system that can diagnose ‘decreased granules (DG)’ in neutrophil, one of the most representative forms of dysplasia
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