Comprehensive bone marrow analysis integrating deep learning-based pattern discovery (BMDeep) - Pontones, Martina; Hoefener, H.; Kock, F.; Schwen, L.; Westphal, M.; Dickel, Nicholas; Kunz, Meik; Metzler, Markus (KLINISCHE PADIATRIE, 2022)
Bone marrow morphology forms the basis for the assessment of hematopoiesis. The currently established approach is dependent on manual microscopic counting of a limited number of cells by specially trained personnel, which is inherently associated with substantial intra- and inter-individual variability. The aim of our project is to automatize and improve the evaluation of bone marrow smears and to identify pathological patterns in pediatric leukemia.<br
CRIS-Team WoS Importer:2022-07-08

Models for the marrow: A comprehensive review of AI-based cell classification methods and malignancy detection in bone marrow aspirate smears. - Ghete, Tabita; Kock, Farina; Pontones, Martina; Pfrang, David; Westphal, Max; Höfener, Henning; Metzler, Markus (HemaSphere, 2024)
Given the high prevalence of artificial intelligence (AI) research in medicine, the development of deep learning (DL) algorithms based on image recognition, such as the analysis of bone marrow aspirate (BMA) smears, is rapidly increasing in the field of hematology and oncology. The models are trained to identify the optimal regions of the BMA smear for differential cell count and subsequently detect and classify a number of cell types, which can ultimately be utilized for diagnostic purposes. Moreover, AI is capable of identifying genetic mutations phenotypically. This pipeline has the potential to offer an accurate and rapid preliminary analysis of the bone marrow in the clinical routine. However, the intrinsic complexity of hematological diseases presents several challenges for the automatic morphological assessment. To ensure general applicability across multiple medical centers and to deliver high accuracy on prospective clinical data, AI models would require highly heterogeneous training datasets. This review presents a systematic analysis of models for cell classification and detection of hematological malignancies published in the last 5 years (2019-2024). It provides insight into the challenges and opportunities of these DL-assisted tasks.
EVALuna2:583328

Selecting A Digitization Device for Bone Marrow Aspirate Smears - Kock, Farina; Pontones, Martina; Ghete, Tabita; Metzler, Markus; Höfener, Henning (Laboratory Investigation, 2025)
The digitization of histologic specimens has become increasingly common within the last years; however, digitization of cytologic slides has its own challenges and there is little guidance on the optimal use of digitization devices for cytopathology. For the morphologic analysis of cells in, for example, bone marrow aspirate smears, different studies have used a variety of scanner setups and there is no direct comparison between those. This makes it nontrivial to choose an optimal scanner setup. This work pragmatically analyzes different whole-slide image scanners but also cost-effective light microscope solutions and identifies important aspects for the digitization of cytopathological samples. We aimed to share our experience in selecting a digitization device for generating a bone marrow aspirate data set.
CRIS-Team Scopus Importer:2025-03-28
Keywords: bone marrow aspirate smear; cytology; digitization; light microscope; whole-slide image scanner