Leveraging Machine Learning for Rapid and Accurate Diagnosis of Acute Leukemia
ML models based on leukocyte and CPD parameters enhance the predictability of AL detection and lineage differentiation at a pre-microscopic level. Integrating these models into hematology analyzers provides a cost-effective, novel tool for detection and differentiation. Interpretable predictions assist experts, reducing subjectivity and expediting final diagnosis through immunophenotyping and molecular studies.
Authors
- Anand Kakarla
- Vandana Lal (ORCID: https://orcid.org/0000-0002-2495-9393)
- Gurleen Oberoi (ORCID: https://orcid.org/0000-0003-2913-5171)
- Reena Nakra
- B. Chandrasekhar
- Ajay Gupta
- Beulah Priscilla Maddirala (ORCID: https://orcid.org/0009-0007-8071-4343)
Institutions
- Rangaraya Medical College (IN)
- Woxsen School of Business (IN)
- Program for Appropriate Technology in Health (IN)
- CSIR National Physical Laboratory of India (IN)
Publication Details
- Journal
- International Journal of Laboratory Hematology
- Published
- 2025-09-09
- DOI
- https://doi.org/10.1111/ijlh.14555
- Primary Topic
- Digital Imaging for Blood Diseases
- Type
- article
- Field-Weighted Citation Impact
- 0.00