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Related Experiment Video

Updated: Jun 26, 2026

Detection and Enrichment of Rare Antigen-specific B Cells for Analysis of Phenotype and Function

Detection and Enrichment of Rare Antigen-specific B Cells for Analysis of Phenotype and Function

Published on: February 16, 2017

Detection of circulating plasma cells in peripheral blood using deep learning-based morphological analysis.

Pu Chen¹, Lan Zhang¹, Xinyi Cao²

¹Department of Laboratory Medicine, Zhongshan Hospital, Fudan University, Shanghai, China.

|January 18, 2024

Summary

An AI system called Morphogo accurately detects circulating plasma cells (CPCs) in multiple myeloma (MM) patients. This advanced technology offers higher sensitivity than manual methods for improved diagnosis and prognosis.

Keywords:

Morphogo artificial intelligence circulating plasma cells deep learning morphology detection peripheral blood

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Last Updated: Jun 26, 2026

Detection and Enrichment of Rare Antigen-specific B Cells for Analysis of Phenotype and Function

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Published on: February 16, 2017

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

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Published on: November 19, 2018

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Published on: October 28, 2018

Area of Science:

Hematology
Artificial Intelligence
Digital Pathology

Background:

Circulating plasma cells (CPCs) are key indicators for multiple myeloma (MM) diagnosis, staging, and monitoring.
Conventional CPC detection via manual microscopy has limitations in sensitivity and reproducibility.
There is a need for more sensitive and efficient methods for CPC detection in peripheral blood (PB).

Purpose of the Study:

To develop and evaluate an artificial intelligence (AI)-based automated system for CPC morphology detection.
To improve the sensitivity and efficiency of CPC identification compared to manual microscopy.

Main Methods:

Retrospective review of 137 bone marrow and 72 PB smears.
Training an AI system (Morphogo) on 305,019 cell images from an AI-powered digital pathology platform.
Evaluating Morphogo's efficacy on 184 additional PB smears and comparing results with manual microscopy.

Main Results:

Morphogo achieved 99.64% accuracy, 89.03% sensitivity, and 99.68% specificity in CPC classification.
At a 0.60 threshold, Morphogo demonstrated 96.15% sensitivity, approximately double that of manual microscopy.
AI detection of CPCs correlated with significantly shorter progression-free survival (18 months vs. 34 months).

Conclusions:

Morphogo is a highly sensitive system for automated CPC detection.
The AI system shows potential for initial screening, prognosis prediction, and post-treatment monitoring in MM patients.
Automated CPC detection offers a more accessible and efficient approach for MM diagnosis and risk assessment.