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A Deep Learning Model for IMMP-Based Residual Disease Monitoring in AML with Monocytic Differentiation.

Jing Ding1, Huiying Qiu2, Chunling Zhang1

  • 1Department of Laboratory Medicine, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, China.

Diagnostics (Basel, Switzerland)
|May 4, 2026
PubMed
Summary

A new deep learning model automates immature monocyte counting in acute myeloid leukemia (AML), improving diagnostic accuracy and monitoring. This AI tool addresses limitations in traditional methods for monocytic AML, aiding treatment response assessment.

Keywords:
acute myeloid leukemiadeep learningimmature monocyte percentagemonocytic differentiationresidual disease monitoring

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Area of Science:

  • Hematology
  • Computational Biology
  • Medical Diagnostics

Background:

  • Acute myeloid leukemia (AML) with monocytic differentiation presents challenges in diagnosis and treatment monitoring due to limitations in current morphological assessments.
  • Inter-observer variability, low sensitivity, and inefficiency in detecting residual disease necessitate automated and objective diagnostic tools.
  • Deep learning offers a promising approach to extract complex cytomorphological features for improved AML analysis.

Purpose of the Study:

  • To develop and validate an automated deep learning model for quantifying immature monocyte percentage (IMMP) in monocytic AML.
  • To address the limitations of manual morphological assessment in terms of accuracy, consistency, and efficiency.
  • To explore the potential of AI-driven IMMP assessment for monitoring treatment response and predicting patient outcomes.

Main Methods:

  • A retrospective analysis of 184 bone marrow smear slides from patients with monocytic leukemia was conducted.
  • An EfficientNet-based convolutional neural network was trained to classify monoblasts, promonocytes, monocytes, and other cells.
  • The model's performance was evaluated using F1 scores at the cell level and accuracy, recall, and specificity at the slide level, correlating predicted IMMP with expert values.

Main Results:

  • The deep learning model achieved robust cell-level classification (F1 scores: 0.82 for monoblasts, 0.34 for promonocytes).
  • At an optimized IMMP threshold of 0.045, the model demonstrated slide-level accuracy (78.9%), recall (81.1%), and specificity (76.9%).
  • Model-predicted IMMP values showed a strong correlation with expert assessments (Pearson r = 0.827), indicating reliable quantitative agreement.

Conclusions:

  • The developed deep learning model offers an automated and objective method for quantifying immature monocytes in monocytic AML, overcoming current morphological assessment challenges.
  • The AI-derived IMMP metric shows potential for effective treatment response monitoring and relapse prediction.
  • Further prospective multicenter validation is required to integrate this AI tool into routine clinical practice for improved patient management.