Article Sidebar

PDF
Issue: Vol. 567 No. 1 (2026)
Section: Các bài báo
DOI: 10.51298/vmj.v567i1.19301
Date Published: 05/11/2026
Views 37
Downloads 24
How to Cite
Nguyễn, T. C. N., Vũ, T. H., Nguyễn, T. H., Nguyễn, T. T., & Lưu, M. H. (2026). TRISOMY 8 AND THE PHILADELPHIA CHROMOSOME IN BONE MARROW CYTOGENETIC ANALYSIS AT BACH MAI HOSPITAL IN 2026. Vietnam Medical Journal, 567(1). https://doi.org/10.51298/vmj.v567i1.19301

TRISOMY 8 AND THE PHILADELPHIA CHROMOSOME IN BONE MARROW CYTOGENETIC ANALYSIS AT BACH MAI HOSPITAL IN 2026

Thị Cúc Nhung Nguyễn1, , Thị Hạnh Vũ1, Thanh Hà Nguyễn1, Tuấn Tùng Nguyễn1, Mạnh Hà Lưu2
1 Bệnh viện Bạch Mai
2 Đại học Quốc gia Hà Nội

Main Article Content

Abstract

Objective: To preliminarily evaluate the effectiveness of Biochrom.AI in supporting automated detection of trisomy 8 and the Philadelphia chromosome (Ph1) in bone marrow chromosome preparations.


Methods: A cross-sectional descriptive study was conducted at the Department of Genetics and Molecular Biology, Hematology and Blood Transfusion Center, Bach Mai Hospital, from January to June 2026. The study included 425 metaphase chromosome images obtained from 85 patients, including 25 patients with trisomy 8 and 60 patients with the Philadelphia chromosome.


Results: The detection accuracy of Biochrom.AI was 96.0% for trisomy 8 and 91.0% for the Philadelphia chromosome. Chromosome classification accuracy was 98.5%, 91.2%, 87.2%, and 71.2% for the large, medium, short, and very short chromosome groups, respectively. At the individual chromosome level, classification accuracy was 96.9% in the trisomy 8 group and 92.6% in the Philadelphia group. Mean analysis time per metaphase decreased from 6.3 ± 4.2 minutes to 4.2 ± 3.9 minutes.


Conclusion: Biochrom.AI demonstrated potential for automated chromosome analysis, improving workflow efficiency and reducing analysis time. Further optimization is needed to improve the classification of small chromosomes and the detection of structural chromosomal abnormalities.

Keywords

Artificial intelligence, Philadelphia, trisomy 8

Article Details

References

Döhner H, Wei AH, Appelbaum FR, Craddock C, DiNardo CD, Dombret H, et al. Diagnosis and management of AML in adults: 2022 recommendations from an international expert panel on behalf of the ELN. Blood. 2022;140(12):1345-1377.
Heim S, Mitelman F. Cancer Cytogenetics: Chromosomal and Molecular Genetic Aberrations of Tumor Cells. 4th ed. Wiley-Blackwell; 2015. 632 p.
Hastings RJ, Moore S, Chia N, editors. ISCN 2024: An International System for Human Cytogenomic Nomenclature. Basel: S Karger; 2024.
Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, et al. A survey on deep learning in medical image analysis. Med Image Anal. 2017;42:60-88.
National Comprehensive Cancer Network. NCCN Clinical Practice Guidelines in Oncology: Chronic Myeloid Leukemia. Version 2.2024. Plymouth Meeting, PA: National Comprehensive Cancer Network; 2024.
Moazzen Y, Çapar A, Albayrak A, Çalık N, Töreyin BU. Metaphase finding with deep convolutional neural networks. Biomedical Signal Processing and Control; 2019;52:353–61.
WHO Classification of Tumours Editorial Board. Haematolymphoid Tumours. 5th ed. Lyon: International Agency for Research on Cancer; 2024.
Zhang W, Song S, Bai T, Zhao Y, Ma F, Su J, et al. Chromosome Classification with Convolutional Neural Network Based Deep Learning. 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). 2018;1-5.