Under construction – not a live service
Sign in
Enrolling by invitationNo phase

Using AI to study myeloma cell appearance and predict genetic changes

Tests an AI method that reads myeloma cells to predict genetic abnormalities.

Plain-language title generated from the registry record
Registry title: AI-Based Fine Morphological Subtyping of Myeloma Single Cells for Predicting FISH Abnormalities

Key facts

Where
Wuhan, ChinaSee the site

Where in China

  • Zhongnan Hospital of Wuhan UniversityWuhan, ChinaNo longer taking part

Questions to ask the study team

  1. How often do I need to come in, and for how long each time?
  2. Are travel costs paid?
  3. Can I keep my own doctor?
  4. What happens if I want to stop?
Print a page for my doctor

Trial details

Who runs it
Fuling Zhou
Trial code
NCT07410403
Record updated
13 February 2026
Original description
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.Read full original descriptionShow less

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

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.

Inclusion: according to the criteria outlined in the Chinese guidelines for MM diagnosis and management (2024 edition).

Exclusion: non-MM.

ClinicalTrials.gov NCT07410403