Development of Machine‐Assisted, Human‐Centred Bone Marrow Cell Classification: Feasibility Analysis in Patients With Myelodysplastic Syndromes
Year: 2025
Type: article
Abstract: Background: Cytomorphological examination and classification of bone marrow (BM) cells using a microscope is essential for diagnosing various diseases affecting the haematopoietic system. However, this requires expertise, is effort-intensive and is inherently subjective. Methods: We developed a method for automatically capturing BM cell images at the single-cell level using an existing cell image analyser designed for peripheral blood. The captured BM cell images were pre-classified by the same analyser and subsequently viewed on a large screen by cytomorphologists for BM cell classification. The data consistency in BM blast percentages between the digital and conventional optical microscopy (CM) methods was examined. Results: During the 3.5-year study period, 657 (96.2%) of the 683 aspirated samples underwent BM cell classification using the digital method. Digital cell images were captured at 2000× magnification and stored permanently, which allowed high-quality cell image analysis at any time by anyone. Cell images could be compared side-by-side across different cell classes, which improves the cell-classification accuracy. The detailed cell morphology in some cases differed slightly between the digital images and CM. BM blast percentage was comparable between the developed method and CM when examining patients with myelodysplastic syndromes (MDS) and other conditions that need to be differentiated from MDS. In a multicentre study, cytomorphologists who used this method for the first time could perform cell classification. Conclusion: : The authors have confirmed clinical trial registration is not needed for this submission.
Source: eJHaem
DOI: 10.1002/jha2.70205
Authors: Kiyoyuki Ogata, Yuto Mochimaru, Leonie Saft, Gerrit Sijm, Bouchra Badaoui, Canan Alhan, Carole Almire, Yosuke Kato, Daisuke Sakamoto, Naoko Chiba, Naho Murata, Nana Kasai, Kazuma Sei, Naoya Kawahara, Mika Ogata, Nicolas Chapuis, Orianne Wagner‐Ballon, Arjan van de Loosdrecht, Yumi Yamamoto
Institutions: Karolinska University Hospital, Amsterdam University Medical Centers, Cancer Center Amsterdam, Amsterdam UMC Location Vrije Universiteit Amsterdam, Centre Hospitalier Universitaire Henri-Mondor, Hôpitaux Universitaires Henri-Mondor, Université Paris Cité, Hôpital Cochin, Assistance Publique – Hôpitaux de Paris, Kyorin University, Kyorin University Hospital
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,860
OpenAlex ID: W4417398336
