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

Brief summary (registry, in English)

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.

What you commit to

• Study type: Observational
• Planned participants: 10
• From 1 Aug 2018 to the primary completion date, 31 Dec 2026
• 1 site in 1 country
• Visits and tests: ask the trial team, the registry does not list them

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Who cannot take part

Copied verbatim from ClinicalTrials.gov and grouped by topic. Only the trial team decides who can take part.

Who the study is looking for

Sex: All · Does not accept healthy volunteers

Full criteria on ClinicalTrials.gov

Sites (1)

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