Image-based Explainable Artificial Intelligence Accurately Identifies Myelodysplastic Neoplasms Beyond Conventional Signs of Dysplasia

Résumé fourni par la source

Evaluation of bone marrow morphology by experienced hematologists is key in the diagnosis of myeloid neoplasms, especially to detect subtle signs of dysplasia in myelodysplastic neoplasms (MDS). The majority of recently introduced deep learning (DL) models in cytomorphology rely heavily on manually drafted cell-level labels, a time-consuming, laborious process that is prone to substantial inter-observer variability, thereby representing a substantial bottleneck in model development. Instead, we used robust image-level labels for end-to-end DL and trained several state-of-the-art computer vision models on bone marrow smears of 463 patients with MDS, 1301 patients with acute myeloid leukemia (AML), and 236 bone marrow donors. For the binary classifications of MDS vs. donors and MDS vs. AML, we obtained an area-under-the-receiver-operating-characteristic (ROCAUC) of 0.9708 and 0.9945, respectively, in our internal test sets. Results were confirmed in an external validation cohort of 50 MDS patients with corresponding ROCAUC of 0.9823 and 0.98552, respectively. Explainability via occlusion sensitivity mapping showed high network attention on cell nuclei not solely of dysplastic cells. We not only provide a highly accurate model to detect MDS from bone marrow smears, but also underline the capabilities of end-to-end learning to solve the bottleneck of time-consuming cell-level labeling.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.