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Rapid and sensitive acute leukemia classification and diagnosis platform using deep learning-assisted SERS detection.

Dongjie Zhang1, Zhaoyang Cheng2, Yali Song3

  • 1Center for Biomedical-photonics and Molecular Imaging, Advanced Diagnostic-Therapy Technology and Equipment Key Laboratory of Higher Education Institutions in Shaanxi Province, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi 710126, China; Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education & Xi'an Key Laboratory of Intelligent Sensing and Regulation of Trans-Scale Life Information, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi 710126, China; Bi-optoelectronic-integration and Medical Instrumentation Laboratory, Guangzhou Institute of Technology, Xidian University, Guangzhou, Guangdong 510555, China; State Key Laboratory of Electromechanical Integrated Manufacturing of High-Performance Electronic Equipment, Xidian University, Xi'an, Shaanxi 710071, China.

Cell Reports. Medicine
|September 9, 2025
PubMed
Summary

This study introduces a rapid deep learning and surface-enhanced Raman scattering (DL-SERS) method for acute leukemia (AL) diagnosis using cerebrospinal fluid. The DL-SERS approach offers sensitive and accurate detection, aiding in early disease identification.

Keywords:
acute leukemiacerebrospinal fluidsdeep learningsurface enhanced raman scattering

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Area of Science:

  • Biomedical Diagnostics
  • Computational Biology
  • Spectroscopy

Background:

  • Accurate and rapid diagnosis of acute leukemia (AL) is crucial for effective patient management.
  • Current diagnostic methods may require significant time and sample volumes.
  • Subtyping and genetic abnormality identification in AL are essential for treatment stratification.

Purpose of the Study:

  • To develop a combined deep learning and surface-enhanced Raman scattering (DL-SERS) strategy for rapid and sensitive identification of acute leukemia.
  • To evaluate the performance of the DL-SERS approach in classifying AL subtypes and genetic abnormalities.
  • To explore the versatility of the DL-SERS platform for diagnosing other neurological diseases.

Main Methods:

  • Collection and analysis of over 390 cerebrospinal fluid (CSF) samples, including healthy controls, AL patients, and other disease groups.
  • Utilizing surface-enhanced Raman scattering (SERS) for sensitive detection within 5 minutes using minimal CSF volume (0.5 μL).
  • Implementing an integrated feature fusion approach (1D spectra and 2D images) with a transformer model for classification.

Main Results:

  • Achieved rapid and sensitive detection of acute leukemia using DL-SERS.
  • Demonstrated exceptional classification performance in terms of accuracy, sensitivity, specificity, and reliability for AL diagnosis.
  • Showcased the platform's versatility by extending its application to the classification of meningitis diseases.

Conclusions:

  • The developed DL-SERS classification strategy provides a powerful tool for the rapid and sensitive identification of acute leukemia.
  • This approach offers a potential auxiliary in vitro diagnostic tool for clinical settings.
  • The DL-SERS platform exhibits significant potential for broader applications in disease diagnostics.