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AI-Based Fine Morphological Subtyping of Myeloma Single Cells for Predicting FISH Abnormalities

NCT07410403 · Status: ENROLLING_BY_INVITATION · Type: OBSERVATIONAL · Enrollment: 10

Last updated 2026-02-13

No results posted yet for this study

Summary

This study developed an artificial intelligence (AI)-based methodology for the quantitative analysis of single-cell morphological data in multiple myeloma (MM). The approach achieves high-precision AI-driven identification and segmentation of myeloma cells, nuclei, cytoplasm, and nucleoli, overcoming the inherent limitations of subjective traditional morphological analysis.

Furthermore, integrating this morphological quantification with cytogenetic abnormality analysis of myeloma cells provides an efficient predictive tool for identifying high-risk cytogenetic abnormalities.

Leveraging AI-guided selection of genetic testing targets, the research applied a rapid genetic abnormality detection technique utilizing first-drop bone marrow aspirate smears. This methodology achieves orders of magnitude improvements in testing cost, sample preprocessing time and detection sensitivity.

Conditions

Sponsors & Collaborators

  • Wuhan Central Hospital

    collaborator OTHER

  • The Affiliated Hospital Of Southwest Medical University

    collaborator OTHER

  • Linyi People's Hospital

    collaborator OTHER

  • Fuling Zhou

    lead OTHER

Eligibility

Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2018-08-01
Primary Completion
2026-12-31
Completion
2026-12-31

Countries

  • China

Study Locations

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Entities

Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07410403 on ClinicalTrials.gov